Editorial Board Advisory Committee Statement from ...

Editorial Board Dr. Capt. Negash Alemu [email protected] Dr. Eristu Yigizaw [email protected] Ato Ebrahim Ali [email protected] Dr. Gashawtena Bayou Ajit Pal Singh [email protected] Dr. Araya Worede Ato Yihenew Jembere

Advisory Committee Mengesha Mamo (PhD) Addis Ababa University Institute of Technology (AAIT), Addis Ababa Wasihun Yimer (PhD) Adama Science and Technology University (ASTU), Adama Col. Ahmed Mamo (PhD) Defence University (DU), Addis Ababa Col. Hishe Hailu (Asst.Prof.) Defence University, College of Engineering (DU-CoE), Bishoftu Col. Fasil Ali (PhD) Ethiopian Airlines, Addis Ababa Dereje Shiferaw (PhD) Addis Ababa University Institute of Technology (AAIT), Addis Ababa

Statement from Bylaws Defense University, College of Engineering shall not be responsible for statements or opinions expressed in papers printed in this journal

For all Correspondence Dr. Eristu Yigzaw e-mail: [email protected] Dean, PG Programs & Research, Defense University, College of Engineering, Bishoftu, Ethiopia

Table of Contents 1.

2.

Effect of Tip Gap Flow on the Performance of a low Speed Contra Rotating Axial Flow Fans Tegegn Dejene, A.M. Pradeep

1

Study and Analysis of Shaped Charge Warhead Cone Material Effect on Penetration of Armour Yared Girma

9

3.

Range Extenders for Electric Vehicles Mulugeta Gebrehiwot, Alex Van den Bossche

15

4.

Optical Properties of Nanostructured Noble Metals and Their Emerging Applications Negash Alemu

23

5.

Waterlogging Problems at Wonj-Shoa Sugar Industry: Identifying Feasible Mitigation or Remedial Measures 32 Megersa Olumana Dinka

6.

Design and Development of Three Phase Converter with ZeroSequence Components to Enhance Power Control Ability 37 S. Ravi, Gidion Solomon, C. Renald and Ashebir Kelibe

7.

An Efficient Model for Radar Target Recognition Eristu Yigzaw

8.

FPGA Implementation of Novel Synchronization Methodology for a New Chaotic System 48 R. Karthikeyan, Ramesh Babu, S.Ashok Kumar and Dennise Mathew

9.

Improving Adaptive Boosting Classification Methods for Concept Drifting Big Data Streams 54 Solomon Getahun Fentie, Bandaru Rama Krishna Rao

10. A Study of Distributed Denial of Service Attacks in Mobile Network T. Pandikumar, R. Sakunthala Jenni 11.

43

60

Productivity Improvement through Lean Manufacturing Tools: A Case Study of Nazareth Garment Share Company 69 Lijalem Mulugeta Kitila

12. Additive Manufacturing of Tooling Element with Conformal Cooling Channel Fisseha Legesse, K.P. Karunakaran

82

13.

Integrated Virtual/Augmented Reality and Haptics Feedback Technology for Defence and Industrial Applications Hailu Gebretsadik, A.K. Das

90

14. Optimization of Sand Casting Process of Aluminum Rod using Taguchi Method: A Case Study at Akaki Basic Metal Industry 96 Solomon Balacha Kebede, Abebaw Mekonnen, Ajit Pal Singh

Journal of Defence Technologies, the official Journal of Defence University, College of Engineering (Vol. 10, No. 1, 2015)

Effect of Tip Gap Flow on the Performance of a Low Speed Contra Rotating Axial Flow Fans Tegegn Dejene

A.M. Pradeep

Department of Aerospace Engineering Indian Institute of Technology-Bombay Mumbai, India E-mail: [email protected]

Department of Aerospace Engineering Indian Institute of Technology-Bombay Mumbai, India

Abstract: The present computational study focuses on the tip clearance effect and its interaction with casing boundary layer and rotor wake on low speed contra rotating fan stage. It is aimed to understand the tip flow behavior and its influence on the performance of low subsonic contra rotating fan stage. Four tip gap cases (0.675, 1.35, 2.7 and 3.5mm) were considered during computational study. The computational results are validated with experimental data performed for low speed contra rotating axial flow fan stage facility designed and built at Indian Institute of Technology Bombay.It is observed that the reduction in total pressure and efficiency for both rotors (rotor 1 and rotor-2) as tip clearance size increases and strong interactions of tip leakage vortex with rotor wake has resulted in increase in local turbulent viscosity entropy at the exit of the second rotor especially near tip regions. The study has also reveals the strong tip leakage vortex existed in rotor-2 has accounted for loss of total energy and decrease in overall performance of the stage in case of larger tip clearance size. Keywords: Contra-rotating rotors, tip clearance, casing boundary layer, tip leakage vortex

I. INTRODUCTION

Contra rotating turbomachines by which stationary blades(stators) are replaced by opposite rotating rotors have several advantages like reduction in specific fuel consumption, compact size and higher aerodynamic performance in both ducted and unducted configurations. Ducted contra rotating fan with required bypass ratio is one of the promising technology in both civil and military engines in terms of better efficiency and improved thrust-toweight ratios [1]. In line with this, the potential of the contra-rotating axial flow compressor/fan has been validated by its successfully utilized in VTOL (Vertical Take-off and Landing) aircraft as the contra-rotating lift fan, such as F35-B[2]. The tip gap between the compressor/fan casing and the tip of the rotor blades is an unavoidable design constraint and it is one of the factor that affect the

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performance of a contra rotating axial fan stage. Tip leakage flow occurs when the flow passes from pressure surface to suction surface of a blade with the available tip gap and loss will be generated when the tip leakage flow mixes with the main passage flow through viscous shearing [3]. The low energy or stagnation pressure fluid due to tip leakage flow also causes blockage of the passage and this phenomena limits the pressure rise across the stage [4].Thus understanding near the end-wall flow behavior is very important since the viscous and tip clearance effects and subsequent interactions with the main passage flow near the end wall of compressors/fan cause three dimensionality of flow. Most of the losses in a turbomachine are related to this endwall flow phenomena. So far a lot of researches have been done on the effect of tip leakage flow on conventional compressors/fans but the flow physics of tip gap flow is not completely understood[5,6,7,8]. The tip leakage flow effect becomes a major impediment to the performance of a contra-rotating fan stage, where the tip leakage flow from the first rotor interacts with the tip flow of the second rotor. Even though previous studies on contra rotating axial flow compressors/fan indicated that better performance compared to conventional compressor/fan detailed studies on the endwall flows like tip leakage flow condition and its interaction with boundary layer and rotor-rotor interaction effects are not fully discussed in open literature. Experimental investigation of endwall flow development across a contra rotating fan unit was carried out by Kumar and Roy [9] and the results were compared with an isolated axial fan. The study indicated reduction in the total pressure loss of the contra rotating fan arrangement at near the casing when the tip gap decreases. Numerical investigation of the effect of tip clearance of contra rotating fans/compressors have been performed recently by Xu et al.[10] and Gao et.al [2] and their


studies reveal that the stage pressure rise and the efficiency decreases with increase of the tip gap. A peculiar behavior observed for the front rotor was that it is relatively less sensitive to tip gap flow effects compared to the rear or aft rotor. More specifically, Gao et.al [2] observed in their study that the first stall stage point of the contra rotating axial flow compressor varies with change in the tip clearance size based on the slope of the pressure curve. In reality it is difficult to judge or conclude the stall inception based on the slope the pressure curve, since it requires separate unsteady measurement or numerical analysis is required for stall inception study. Recently Wang et.al.[11] on their computational study of the effect of tip clearance size on performance of dual rows contra rotating compressors and their result show that the performance of the upstream rotor is more affected when the tip clearance size increases when compared to the downstream rotor. This finding contradicts with the finding by Xu et.al [9] and Gao et.al [10]. It can be seen from the above literatures, the study of tip gap effects of contra rotating fans/compressors is very limited and some of the results are contradicting. From the previous studies we can refer that the tip clearance size effect on performance of on contra rotating rotors is not fully understood. Thus understanding the tip leakage effect for different tip gap of CRF stage is the main concern of the present study. The contra rotating fan rotors blades with high aspect ratio (A.R=3) are designed and built at Indian Institute of Technology-Bombay. Four tip gaps: 0.675, 1.35, 2.7 and 3.5mm and the gaps are 0.5%, 1%, 2% and 3% in terms of the blade span. Thus it is aimed to understand the effects of tip clearance size on the overall performance of contra rotating axial flow fan stage.

II. EXPERIMENTAL SETUP The experimental rig of scaled down model of low subsonic speed contra rotating axial flow contra rotating axial fan (CRAF) stage designed and developed at Indian Institute of Technology Bombay shown in fig.1 was considered for the present study. The CRAF consists of two rotors (Rotor-1 and Rotor-2) which are rotating opposite direction and their specifications are given in table1.The two rotors are driven by two AC

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induction motors of 15KW with separate variable frequency drives (VFD).The VFD also has a power provision to measure and display the power consumed by the motors. A bellmouth at the front of the rig is used for smooth flow of air into the test section and a throttle cone at the exit of the setup is used to control the mass flow rate. The rotors are designed for a total mass flow of 6 kg/s and a rotational speed of 2400 RPM for both rotors. The nominal test rig tip gap is 3.5mm for both rotors. Both rotors have a chord length, tip diameter, and hub-tip ratio 45 mm, 405mm and 0.35 respectively. Further description of the experimental rig can be obtained from Mistry and Pradeep [12] and hence are not reproduced here for brevity. Mass flow at inlet is measured by pitot probe and seven hole probe is used to measure total pressure, static pressure, velocity components and flow angles at inlet of rotor-1, in between rotors and at exit of rotor-2. III. COMPUTATIONAL DETAILS The computations are carried out using Numeca FINE/TurboTM. The boundary conditions are specified based on the experimental data and accordingly the total pressure profile, uniform total temperature and velocity directions are specified at the inlet. At the outlet, static pressure based on radial equilibrium is specified.


IV. VALIDATION OF THE CFD RESULTS WITH EXPERIMENTAL DATA The performance parameters like pressure rise and efficiency and other results of computational fluid dynamics (CFD) were compared with the experimental (EXPT) data available for larger tip clearance (3.5mm) cases and the results are given in fig.3. It can be seen that the simulation results of span wise total pressure at the exit of rotor-1 and rotor-2 matches well along the blade span and the maximum error between the experimental and CFD result is about 5%. This therefore indicates that the CFD results are representative of the actual flow physics. Adiabatic and no slip conditions are specified at the solid walls. The design rotational speed (2400 RPM for both rotors) and design mass flow conditions for tip clearance of 0.675, 1.35, 2.7 and 3.5 mm were considered for present study. A. Generation of Grids The O4H topology of AutoGrid5©is used to generate grid and the butterfly topology is used at the tip gap regions. A grid independence study is done to determine the optimum grid size and grid size 1x106 and 0.96x106 are chosen for rotor-1 and rotor-2, respectively. The minimum grid spacing on the solid wall is 8x10-6 m to capture viscous effects of end walls and blade surfaces resulting in y+ values < 2 at the walls. Details of mesh size of rotor1 and rotor2 is given in table 1. B. Boundary Conditions The boundary conditions are specified based on the experimental data obtained and accordingly total pressure profile, uniform total temperature and velocity direction are given at inlet and at outlet static pressure based on radial equilibrium were specified. Adiabatic and no slip conditions were specified at the solid wall. One equation turbulence model of the Spalart–Allmaras (SA), used to estimate the eddy viscosity. The SA turbulence model is widely used to investigate turbomachinery problems due to its robustness and its ability to treat complex flows [13].

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V. RESULTS AND DISCUSSIONS The present paper discusses the detailed flow structure of different tip clearance size at design mass flow condition. Parameters like total pressure, efficiency, static pressure near casing, blade surface loading distribution, relative velocity streamlines, turbulent viscosity are considered in the present study. A. Radial Distribution of Total Pressure and overall efficiency In order to know the contra rotating fan performance it is better to look at the span wise variation of important parameters like total pressure rise coefficient(  ) for different tip gap size and it is shown in fig.4. The blade tip speed ( U t ) and density (  ) is used to calculate the dynamic speed. P0i and P0local are the inlet total pressure and local total pressure at specific location along the blade span.   (P0i  P0local) / 0.5U t 2

(1)

It is observed from the figure that reduction of the total pressure from smaller tip clearance to larger tip clearance near the tip region for both rotors and the change of the total pressure rise coefficient at other sections of blade span is negligible as tip gap size changes. The performance of both rotors is highly affected when tip gap increases from smaller to larger size. The advantage of lower tip gap in improving the performance is clearly seen in the figure (fig.4a and 4b).The strong suction effect


created by rotor-2 at the exit of rotor-1 improves the performance of rotor-1 at smaller tip gap. At large tip clearance size, the performance of rotor 2 is much affected due to the strong tip leakage vortex and the wake from rotor-1.The tip leakage flow and the related vortex will be discussed in the subsequent section. Similarly the overall efficiency (  ) of the CRF stage is shown in fig.5 and it can be seen that the highest isentropic efficiency is obtained for lower tip clearance size. The mass flow is non- dimensionalized with local atmospheric pressure and temperature (NDMF). It is also observed that at higher mass flow the efficiency of 1% TC matches with the efficiencies of 2% and 3% TC.

a) Rotor 1 exit

B. Blade Static Pressure Distribution In order to know the blade loading, blade static pressure profile of both rotors at different spanwise location is given in fig.5 and fig.6. It is known that the static pressure difference between the pressure surface and suction surface shows the blade loading and the gradient of static pressure between the two surfaces at tip region drives the tip leakage flow. Figure 6 shows the static pressure profile at 50% of blade span of rotor-1 and rotor-2 and blade static distribution for small tip clearance (0.675mm and 1.35mm) shows increase of pressure side static pressure compared to large tip clearance cases(2.7mm and 3.5mm) for both rotors. The static pressure profile at 97% of blade span of rotor-1 and rotor-2 in fig. 7 shows that there is high decrease in suction side static pressure for larger tip clearance (3.5mm) due to strong tip leakage vortex and its interaction with main passage flow. The static pressure contours near casing for two tip clearance cases (1.35mm and 2.7mm) is presented in fig.8 and it further clarifies the flow behavior and pressure trough which corresponds to local minimum pressure. As stated in paper of M. Furukawa [14], the casing wall pressure distribution is connected with the behavior of tip leakage vortex; it is clearly observed that minimum static pressure region with vortex core for 2% TC gap case. The tip leakage vortex strength decreases in case of 1% TC tip gap and the pressure trough area diminishes at suction side of both rotors.

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b) Rotor 2 exit Figure 3: Span-wise variation of total pressure rise coefficient Table 1: Details of mesh size CRF StreamSpanwi Tangentia Stage wise se l direction directio direction n Rotor 1 185 77 33 Rotor 2 153 77 33 Tip gap 185 29 17 R1 Tip gap 153 29 17 R2


a) Rotor 1

a) Rotor 1 exit

b)Rotor 2 Figure 6: Static pressure distribution of Rotor 1 and Rotor 2 at 50% blade span b) Rotor 2 exit Figure 4: Total pressure variation along a rotor blade span

a) Rotor 1

Figure 5: Isentropic efficiency of CRF stage

b) Rotor 2 Figure 7: Static pressure distribution of Rotor 1 and Rotor 2 at 97% blade span

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level increases as tip gap size increases (The entropy contour is not given).

Figure 8: Static pressure contours at 97% blade span

Figure 9: Relative velocity (W) streamlines at the meridional plane for smaller tip clearance size

C. Tip Leakage Flow Behavior In order to know the strength of the tip leakage vortex, the streamlines of relative velocity in averaged meridional planes are shown in Fig. 9a and fig.9b for 1% tip clearance (TC) and 2% tip clearance (TC) at design mass flow condition. In case of smaller tip clearance size except small deviation of streamlines no tip leakage vortex observed. In case of 2% TC, Strong tip leakage vortex existed in rotor-2 and the vortex is positioned near trailing edge.This indicates that the tip leakage vortex has affected the performance of rotor-2. The same phenomena was observed by Wang et.al[11] for larger tip clearance size of CRC. D. Turbulent Viscosity and Entropy Interaction of casing boundary layer with tip leakage vortex increases the local turbulence intensity of the flow and the entropy. Since the tip leakage vortex strength is more when tip gap size increases and these results in increase local turbulent viscosity and entropy. It can be seen from fig. 10a and 10b that the turbulent viscosity magnitude doubles for one percent increase in tip clearance size. The strong flow shear and tip leakage vortex induce increment in entropy generation and hence loss. Similarly the entropy

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Figure 10: Contours of turbulent viscosity at planes of rotor-2 VI. CONCLUSIONS The present paper discusses, computational fluid dynamic study of effect of tip gap size on the performance of low subsonic contra rotating axial fan stage. The design mass flow and design


rotational speed were considered for the present numerical simulation. Based on the work carried out the following conclusions are drawn:  The overall performance like total pressure rise coefficient and efficiency of CRF stage decreases as the tip gap increases and the second rotor is more sensitive to the effect of tip gap change.  The performance of rotor-1 is not affected in case of 0.5% and 1% tip clearance case and we can say that rotor-1 is insensitive to lower and medium tip clearance size.  The strong suction effect created by rotor-2 at the exit of rotor-1 improves the performance of rotor-1 at smaller tip gap.  The high strength tip leakage vortex region existed at trailing edge of rotor-2 has affected the overall performance of CRF stage.  The tip leakage vortex and the high turbulent viscosity and hence entropy near tip region of rotor-2 is responsible for reduction total pressure as tip clearance size increases. VII. RECOMMENDATION In order to understand the stall point of both rotors it is required to perform unsteady measurements using dynamic pressure sensors and/or unsteady computational analysis near stall mass flow condition. REFERENCES [1] C. S. Mistry and A. M. Pradeep, “Design and performance analysis of a low-speed, high aspect ratio contra-rotating fan stage”. The 11th Asian international conference on fluid machinery, IIT Madras, Chennai, India, Paper no. 156, pp.1–10’ November 2011 [2] GAO Limn, L.I. Xiaojun, F. Xuedong and L.I.U. Bo, The effect of tip clearance on the performance of contra-rotating compressor,” Proceedings of ASME Turbo Expo 2012, pp. 1–10, 2012 [3] R. Williams, D. Gregory-Smith and Li he," A study of tip clearance flows in axial compressor blade row" Proceedings Turbo Expo 2006, Barcelona, Spain, ASME Paper No. GT2006-90463

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[4] S.A Khalid and et.al.“Endwall blockage in axial compressor”, J. Turbomach. 121(3), pp. 499-509, Jul 1999 [5] C. Sakulkaew, C.S Tan, ,E. Donahoo, C. , Cornelius, and M. Montgomery, "Compressor efficiency variation with rotor tip gap from vanishing to large tip clearance", Proceedings of ASME Turbo Expo 2012, Copenhagen, Denmark, ASME Paper No. GT2012-68367 [6] Y. Wu and W. Chu, “Behavior of tip-leakage flow in an axial flow compressor rotor” Proceedings of the Institution of Mechanical Engineers Vol.221, Part A: J. of Power and Energy, pp 99-110, Feb 2007 [7] Chenkai Zhang, Jun Hu, Zhiqiang Wang, Wei Yan, Chao Yin, Xiang Gao, Numerical Study on Tip Leakage Flow Structure of an Axial Flow Compressor Rotor, Proceedings of ASME Turbo Expo 2014, N0. GT2014-27291, 2014 [8] C.M. Jang, D. Sato and T. Fukano, Experimental Analysis on Tip Leakage and Wake Flow in an Axial Flow Fan According to Flow Rates, Transactions of the ASME Vol. 127 pp.322-329 MARCH 2005 [9] S. Kumar and B. Roy “Endwall Flow Development across a Contra-Rotating Fan Unit”, International Gas Turbine and Aero Engine congress and Exposition, Germany, 2000. [10] J. Xu,C.Tan, H.Chen, Y. Zhu, and Z .Dhang, Influence of tip clearance on performance of a contra-rotating fan,” J. of Therm. Sci. , vol. 18, no. 3, pp. 207–214, Sep. 2009. [11] Wang, Y., Chen, W., Wu, C., and Ren, S, 2014, “Effects of Tip clearance size on the Performance and Tip Leakage Vortex in DualRows Counter-Rotating compressor”, Proceedings IMechE. Part G: J. of Aerospace Eng, pp 1-13. [12] C. S. Mistry and A. M. Pradeep, “Effect of variation in axial spacing and rotor speed combinations on the performance of a high aspect ratio contra rotating axial fan stage,” Proceedings of the Institution of Mechanical Engineers, Part A: J. of Power and Energy, vol. 227, no. 2, pp. 138–146, 2012. [13] Qingjun Zhao, Jiafei Qiao, Huishe Wang, Xiaolu Zhao, and Jianzhong Xu, “Influence of Tip Clearance Size on Flow Characteristics of a Vaneless Counter-Rotating Turbine” 45th


AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, Denver, Colorado, August 2009. [14] M. Furukawa, K. Saiki, K. Nagayoshi, M. Kuroumaru and M. Inoue, "Effects of Stream Surface Inclination on Tip Leakage Flow Fields in Compressor Rotors, J. Turbomach. vol. 120 pp.683-692, October 1998.

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Study and Analysis of Shaped Charge Warhead Cone Material Effect on Penetration of Armour Yared Girma Armament Engineering Department College of Engineering, Defense University, Bishoftu,Ethiopia E-mail: [email protected] Abstract: Shaped charges are explosive devices with high penetration capability. They are extremely useful when an intense, localized force is required for the purpose of piercing a barrier. Shaped charges are used for both civilian and military purposes. The objective of this paper is to study, design and develop a cone shaped high explosive anti-tank for missile warhead with various cone materials and undertake practical studies on the terminal effect, in terms of penetration on a designated target. Other shaped charge performance parameters like jet (penetration) velocity, momentum and energy per unit depth in the target has been analyzed with the data available in the literature and with the proven theories in the field. The analysis of practical results and comparison of the same with existing theories will give an insight in to the process of the target effect by the shaped charge warhead to optimize the design for effective target neutralization. From the theoretical computation and field results in terms of penetration depth and width, copper has been found to be a preferable choice of cone material for shaped charge warheads. Keywrods: Shaped charge warhead, Cone material, Armor.

I. INTRODUCTION A warhead is defined as “the specific device or part of an armament system that damages a target and renders it incapable of performing its intended function” [2]. Conventional warheads can be divided into two categories, directed energy and omnidirectional warheads. Directed energy warheads focus the explosive energy by use of a cavity lined metal, most commonly an accelerated liner. Shaped charge warheads convert the liner material in to a high velocity and temperature atomized jet penetrator. Other warheads which are effective in all directions surrounding the warhead are called Omni-directional warheads. These are of two categories; fragmentation and blast. Fragmentation

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warheads accelerate large number of metal fragments. Fragment size and amount and the emerging angles can be either controlled or produced by natural fragmentation of casing due to expansion. Blast type warheads utilize the chemical energy of the warhead to produce a high pressure shock wave in the air [2], [6]. II. APPLICATIONS OF SHAPED CHARGES The purpose of a shaped charge is to make a slender hole in a material that is otherwise difficult to penetrate. This being so, much of its development has occurred because of the high demand from the military. However, there are many relatively peaceful applications to this remarkable technology. The major application is in the oil industry, which uses more shaped charges per year than the military. This is the process of borewell perforation. Other major applications lie in industries in which a hole needs to be made. They include, among others, applications in the mining industry (drilling rock and tunneling), demolition work (e.g. decommissioning unwanted structures), torpedoes, tree-felling, avalanche-control, jetfighter ejector seats and safe-breaking. Although the underlying principles are the same, many of these applications have very specific demands on the use and accuracy of this technology. The main application is in the military arena, for high explosive antitank (HEAT) rounds including hand-held (bazooka type) rounds, gun-launched rounds (e.g., rifle grenades), cannon-launched projectiles, and various bombs. The targets are armors, bunkers, concrete or geological fortifications, and vehicles [7]. II. SHAPED CHARGE PRINCIPLES Walters defines a shaped charge as “a cylinder of explosive with a hollow cavity at the opposite end of the initiation train” [6].If the cavity does not


contain a liner, it is referred as a hollow charge or an unlined-cavity charge. If it contains a liner made from metal, alloy, glass, ceramic, wood or another material, the device is called a shaped charge or cumulative charge. Generally used liner geometries are conical, hemispherical, parabolic or any acute shape. In shaped charge warheads, generally narrow angled cone-like geometries are used. Optimized liner geometries may have double or variable angled cones, tulip or trumpet shapes [2]. The most common shaped charge consists of a detonator-booster explosive train for initiation of a right circular cylinder of explosive which upon detonation collapses a metallic lined cavity at the opposite end of the detonator. When the warhead strikes a target, the fuse detonates the charge from the rear. A detonation wave sweeps forward and begins to collapse the metal cone liner at its apex. The collapse of the cone results in the formation and ejection of a continuous high-velocity molten jet of liner material (typically greater than 12 km/s). This produces a velocity gradient that tends to stretch out or lengthen the jet. The jet is then followed by a slug that consists of about 80% of the liner mass [5]. When the jet strikes a target of armor plate, pressures in the range of hundreds of kilo bars are produced at the point of contact. This pressure produces stresses far above the yield strength of steel, and the target material flows like a fluid out of the path of the jet. The pressure generated in the region of impact has been shown to be so great that the strength of the jet and target material could be ignored, and therefore, these materials could be treated as perfect fluids. There is so much radial momentum associated with the flow that the difference in diameter between the jet and the hole it produces depends on the characteristics of the target materials [1].

vulnerability is another factor to be considered. Some targets are soft and do not require high velocity jet, whereas others may require high velocity and deep penetration. The penetrating power of shaped charge is proportional the cube of its diameter and also proportional to the detonation pressure of the explosive used [5]. The performance of shaped charge depends on cone angle, material of liner, explosive type, and standoff distance between detonation point and target. In addition to these parameters, the performance of shaped charge also depends on the reliability of manufacturing processes, e.g. concentricity, consistency, homogeneity of the explosive filling [6]. Besides all above factors, past experience is a good guide for the selection of materials; however, one should not overlook the possibility of new materials. The liner design is one of the most important aspects the designer has to pay special attention.Penetration test is conducted in order to study the effectiveness of weapon to produce desired effect in the intended target plate. IV. EXPERIMENTAL SETUP The term shaped charge refers to all jet forming warheads with cone shaped liner in particular for this paper which is dedicated on the analysis of the effect of the cone material on penetration of armour plate. The study conducted mainly involved a shaped charge warheads with Copper , Aluminum and Mild steel cones to analyze data on penetration at an optimum standoff distance with the quality and quantity of explosive filling remaining the same in all firings. Experimental model and the nomenclatures are illustrated in figure 1.

III. SHAPED CHARGE WARHEAD DESIGN CONSIDERATIONS The jet velocity of a shaped charge warhead can be controlled by characteristics of selected materials, obviously, the cone material density, strength and ductility. Similarly, the detonation velocity, power, filling density of explosive selected, availability and even the cost must also be considered. When designing shaped charge warhead, target

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Figure 1: Shaped charge warhead test setup model and nomenclatures


(i) Warhead development: The test samples were developed on basis of design criteria of shaped charge. The key shaped charge components that require careful design are: a) Liner configuration, b) Explosive configuration, and c) Case configuration (a) Liner configuration Cone shaped liners are classified as single cone, double cone, trumpet and single cone trumpet in shape[2]. In this study a single cone liner is adopted because it can made with lesser difficulties than the other shapes and a clear contrast can be presented in experiment. Some of the main parameters of the cone are all the same for the selected materials; thus the warhead used in this study had internal cone diameter of 50mm and the thickness was 4% of the base cone diameter CD. The liner had also 42 degree of cone angle, Production process of liners: The material production process has a great impact on the material penetration performance[2]. Even though machining process of producing a cone shaped material is expensive and produces lesser quality of the shape in terms of crystalline structure, the samples were made by machining because it is the only easily available process. Figure 2shows the manufactured and used cones with different materials.

Figure 2: Cone liners of different materials (b) Explosive configurations The suitable fillings for shaped charges are cast cyclotol or RDX/TNT.Processing techniques known for explosive compositions to their final form are cast, pressed or extruded. We used casting filling technique because our chemical contains TNT flecks which has relatively low melting temperature (80 degree Celsius) compared with its ignition temperature (240 degree Celsius) [3]. Casting essentially involves heating

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the explosive composition until it melts, pouring it into container and leaving it to solidify by cooling. The munitions are filled with an explosive cyclotol which is a mixture of explosives with 60% Research Department Explosive (RDX) and 40% Trinitrotoluene (TNT). (c) Casing configuration The case is designed to retain the charge before detonation and obviously must be made strong enough to withstand the environment that the weapon will experience at launch and during flight. Symmetry and a regular fracture to allow the explosive gases to expand evenly all rounds are major factors to be considered during manufacturing. The salient aspects of adapted shaped charge munitions are shown in figure 3.The dimensions of the casing for the three of the cone materials is taken similar. Figure 4 shows the manufactured and used case. At the rear it has a cap with a hole for detonator.

Figure 3: Designed case dimensions

Figure 4: Designed and fabricated case (ii) Target set up: The target used was made up of two bonded armor plates of an overall thickness 30 mm along with concrete slabs with thickness 350 mm thus combine armor and concrete target of an overall thickness was mounted on a firm structure at an optimum standoff distance. (iii) Mounting frame A rugged steel mount with a firm holder for warhead firing as shown in the figure 5 to with


stand the reaction forces that the warhead creates during firing designed and fabricated locally. A detailed survey was undertaken for material and fabrication process availability with tools and expertise support to develop the warhead and test set up as per the design.

Figure 5: Target and warhead setup for firing test V. FIELD FIRING AND RESULTS The open air detonation set up was built to study the target penetration and secondary effects of the detonation. In all the firings the combined targets of armor and concrete were totally defeated and there was residual of jet slug found in majority of firings. Figure 6 and figure 7 reveals these test results.

VI. DATA ANALYSIS AND COMPUTATION The quantity of explosive is calculated from the volume of the case dimensions. And the other important parameters of explosive chemical used for the computation are all in the reference [3]. (i) Mathematical analysis of relevant parameters for various cone materials (a) Copper Cone Performance Characteristics The density of the copper, Cu material is 8.95 g/cm3, the length, L is equal to the standoff distance which was calculated depending on the cone diameter and it has different values for the selected cone materials, so as per the design the length for copper is 181.5mm. (b) Penetration Depth: is given by equation 1,where

 jV 2

account for target material strength

effects Where Y is the yield strength of the target material and α is an experimental constant as proposed by Pack and Evans for armoured target Y

 jV 2 t

is = 0.3.The subscript j and t represent jet

and target characteristics respectively [4]. PL

Figure 6: Penetration in to armoured target

Y

 j  Y 1  t   jV 2 

   

(c) Jet Velocity (Vj): It is given by equation 2, where Vo is collapse velocity which is dependent on explosive property specifically on Gurney velocity 2E and detonation velocity. The value of 2E constant and detonation velocity for different kinds of explosives are available in many references. Explosives used in this experiment are given in the table 1. The Greek letters 𝛼 , and β represents half cone angle, projection angle and collapse angle respectively. Equation 3 shows slug velocity [3], [6].   Vo Cos      2  Vj   Sin 2

Figure 7: Penetration into combined armour and concrete target

12

(1)

(2)


Table 1: Gurney velocity , √𝟐𝐄 and detonation velocity, D for some common explosives Types of explosive

Density g/cm3

√𝟐𝑬

1.754

8.25

2.79

RDX

1.77

8.70

2.83

Armoure d Target Thicknes Penetrati s on Diameter Penetrati on Depth

Table 3: Data from the experiments

Standoff (SOD)

mm/s

Submunition sample

Cyclotol

Detonation Velocity mm/s

Copper, Aluminum and Steel materials penetration performance is shown below. Besides defeating the given target, the combined target of armour and concrete structure were totally defeated.

TNT

1.65

6.86

2.46

Copper

181.5 mm 187m m 187m m

30m m 30m m 30m m

60/40

(d) Slug Velocity (VS)   Vo Cos      2  (3) VS   Cos 2 a. Mass of jet (Mj):can be found by equation

4, where m mass of the given cone liner material, mass of copper in this case. The slug mass that follows the jet can be calculated by equation 5 [6]. Mj 

m 1  Cos 2

(4)

b. Mass of slug (MS) m M s  1  Sin   2

(5)

In same manner the mathematical analysis of relevant parameters for aluminum, Al and mild steel, Ms cone materials performance characteristics had been performed and the overall summary of theoretical computed data are shown in the table 2. Table 2: Summary of theoretical computed data Material of the cone Mild steel

Jet Slug Penetration, velocity, velocity, mm Km/sec Km /sec 129.5 18.285 5.924

Aluminum

75.9

23.846

10

Copper

134.9

17.638

5.567

(ii) The experimental data The data collected from three firings are shown in table 3. A comparative study and analysis of cone material penetration performance of shaped charge warheads on armour, the selected cone materials

13

Alumin um Mild steel

23m m 21.4 mm 18.3 mm

30m m 30m m 30m m

VIII. CONCLUSION The computed results, which is shown in table 2, relevant theoretical studies in the area of penetration effects of various cone materials and also the experimental results, which is shown in table 3 prove beyond doubt the following:  The copper and mild steel as cone material are relatively comparative in their depths of penetration. The jet and slug velocities too are approximately close.  The aluminum has comparatively high jet and slug velocities but the penetration is low. However, it has enormous heateffect, which can lead to significant igniferous effect on target and surroundings.  All three experiments have proved the above in totality. However, copper has a wider and a deeper penetration in to the armour and in to the concrete as well, that was backing the target. ACKNOWLEDGMENT We would like to thank Gafat Armament Industry, for providing the possible support especially in manufacturing of the warhead sample. We also thank, Homicho Ammunition and Chemical Engineering Industries that extended support in preparation and conduct of practical studies with available experimental firing setup.


REFERENCES [1] Baker, E., Modeling and Optimization of Shaped Charge Liner Collapse and Jet Formation, PhD. Thesis, Washington State University, 1992 [2] Carleone, Joseph. Tactical Missile Warheads, American Institute of Aeronautics and Astronautics Inc, 1993 [3] Cooper, Paul, Explosive Engineering, 1996, PP.38-59. [4] Pack, D.C., Evans, W.M., Penetration by High Velocity Jets: I, Proceedings of Physics Soc. (London), B64, 1988 [5] Pugh, E.M., Eichelberger, R.J., and Rosteker, N., Theory of Jet Formation by Charges with Lined Conical Cavities, Journal of Applied Physics, 23, 532-526, 1976 [6] Walters, W., Zukas, J.A, Fundamentals of Shaped Charge Jets, John Wiley and Sons, 1989 [7] Walters, William, A Brief History of Shaped Charges, 24th International Symposium on Ballistics, New Orleans USA, 2008

14


Range Extenders for Electric Vehicles Mulugeta Gebrehiwot, Alex Van den Bossche Department of Electrical Energy, Systems & Automation Ghent University, Ghent, Belgium E-mail: [email protected] Hybrid electric vehicles are gaining popularity both in civil and military applications due to their promising advantages in achieving better fuel economy and reduced pollutant emissions when compared to the conventional ICE vehicles. However, the advantages gained in vehicle electrification do have different significance and requirement in the Army than the commercial ones. In this paper, the payoffs of electric vehicles as applied to military application are discussed. The fuel economy, silent watch, silent mobility, gradeability and thermal signature of military vehicles are highly dependent on the load carried, role, duty cycle and the proper sizing of the drivetrain components. This paper, therefore, is also intended to address sizing of the drive components and energy requirements of a lightweight and efficient range extender for electric vehicles. Abstract:

Keywords: Hybrid electric vehicles, Military applications, Traction motor, Sizing, Range extender, ICE.

I. INTRODUCTION Despite all the research efforts being carried out, the commercialization of electric vehicles (EVs) hasn’t yet been successful. The main reason is their unsuccessfulness to satisfy the consumers` needs due to high cost and limited range. Batteries take the major accountability for being the bottleneck in the development of modern electric vehicles. Limited energy density of batteries causes heavy and expensive battery packs and a small driving range. Additional vehicle features, such as heating the passenger compartment, further limit this range. According to SAE J1715 [1],an electric vehicle is a vehicle in which its propulsion is accomplished entirely by electric motors (including series hybrid configuration), regardless for the means of obtaining that electric energy, and ‘hybrid car’ is only used for parallel or combined hybrid systems. When we consider high mobility, high manoeuvrability and silent driving, the series hybrid architecture is the best candidate for combat

15

vehicles [2], without any mechanical connection between power plant and tractive wheels [3]. Typical auto trips are within the driving range of efficient electric vehicles (EVs), as almost 90% of daily car use is for less than 40 km, while occasional trips exceed EV range. However, for the occasional extended range, an additional battery cost is extremely high [4]. A solution to overcome this limitation is to add a range extender to a pure electric vehicle thereby reducing range anxiety. Range extenders are small electricity generators operating only when required. The range extender consists of mainly three parts[5]:a combustion engine, a starter/generator and a power electronic converter. The starter/generator is used as a starter motor during engine starting and then work as a generator during the rest of the vehicle operation transforming mechanical energy to electrical energy. A power electronic converter connects electrical generator to the battery-bus of the electric vehicle to provide the vehicle with electricity. There is a growing wide-range of interest in the development of electric vehicle (EVs), hybrid electric vehicles (HEVs) and plug-in-hybrid electric vehicles (PHEVs) for military applications. II.

BENEFITS OF ELECTRIFICATIONS

MILITARY

VEHICLE

Even though range extended electric vehicles are applicable for both civilian and military mobility purposes, the potential payoffs and technical challenges have a different place in the case of military applications. a. Improved Fuel Economy When a range extender is added to a pure electric vehicle it forms an architecture equivalent to series hybrid electric vehicles. So, in all forms of electric drives ,like in the case of range extended electric vehicle, the engine doesn’t have a direct link to the ground speed and is made to operate at its efficient


operating point at various vehicle speeds [6]. The reduced fuel consumption can have several implications, such as increased range, reduced fuel tank volume and a reduced logistical burden [7]. With the use of a range extender, it is possible to control the engine independently of the vehicle load and speed. This will enable the engine to spend most of its time in the parts of the engine efficiency map where efficiency is highest (high torque, moderate speed), as shown in Fig.1. When the battery pack is charged sufficiently, one can shut the engine off and still continue to drive the car in a silent mode. b. Stealth Mode of Operation There are military missions which require silent watch and reduced thermal signature mobility. Range extended electric vehicles can give these benefits by running the vehicle in the all-electric mode by putting the engine off. The plots in Fig.1, Fig.2 and Fig.3 are based on the vehicle parameters and performance objectives given in Tables 1 and 2. Moreover, ICE power =12 kW, Generator power=12 kW and traction motor power 18 kW were given as input for simulation using Advanced Vehicle Simulator (ADVISOR). Fuel Conv erter Operation Geo 1.0L (12kW) SI Engine - transient data (EELAB-Ghent)

30 26.1 32.1

20

26.1

33.1 30.1 26.1

Torque (Nm)

10

18.1

22.1

18.1

0

-10 max torque curve gc max torque curve design curve output shaft op. pts(includes inertia & accessories)

-20

-30

0

1000

2000

3000 4000 Speed (rpm)

5000

6000

7000

Figure 1: Engine operation As can be seen in Fig.2, one driving cycle of UDDS is used. The vehicle goes in silent (allelectric) mode for almost 80% of the driving cycle. As soon as the state-of-charge (SOC) of the battery depletes to a predetermined value, the engine starts, and as a result there can be noticed emissions which can represent a thermal signature in the mobility.

16

ICE All-electric mode

ON

Figure 2: Silent and engine mode operations in one UDDS cycle c. Available Onboard Power Another payoff in applying range extenders in military electric vehicles is the capability of power generation to feed the auxiliary loads while in field [8]. Moreover, depending on the capacity and vehicle architecture, the power unit can also be used as a stationary generator, to power a field hospital for instance [9]. d. Flexibility In range extended electric vehicles (series hybrid electric vehicle), the ICE is not mechanically coupled to the driven wheels. The power from the prime mover to the wheels is transferred electrically. Therefore, this drive-by-wire system allows a more flexible architecture, and the habitable space can be increased [10]. e. Engine Downsizing The ICE of the range extender is set to operate at its most efficient operating point as seen in Fig.3. Due to the presence of the battery pack to deliver the power required for short bursts of high acceleration instead of the engine, it is possible to downsize the internal combustion engine as it doesn’t need to be designed for the peak power requirement. This will result to a considerable weight reduction of the vehicle. This will bring even better energy efficiency.


In order to calculate the traction force and power, three major forces which can resist a vehicle from moving are considered. (a) Rolling Resistance Force (b) Air Resistance Force (c) Gravitational Force as a vehicle moves up As to [12], for off-road and military hybrid vehicles, the gradeability requirement on soft roads dictates the power of the traction motor.

Fuel Converter Efficiency 0.4 0.35 0.3

efficiency

0.25 0.2 0.15 0.1 0.05 0

0

200

400

600 800 time (s)

1000

1200

1400

Figure 3: Engine efficiency at different operation modes The special requirements of electric vehicles for military applications, however, always bring serious constraints in both electrical and mechanical design [11]. The severity of the technical challenges and the improvements in vehicle performance to meet the special requirements depend on the vehicle’s intended performance objectives. The performance parameters are influenced by weight, size, readability, battery capacity, configuration and role of the vehicle where they all need a proper dimensioning of the vehicle components. Specially, when soldiers in a special mission are stranded in an electric vehicle in remote area due to a complete discharge of the battery packs, then standalone portable lightweight range extenders may be of high interest to bring them back home. III. CONSIDERATIONS FOR A LIGHTWEIGHT RANGE EXTENDER DESIGN

The determination of the power ratings of the major components (i.e. traction motor, power electronics, battery, engine and generator) is done on the basis of the constraints like acceleration performance, cruising and zero emission range. A. Traction Motor Power The power rating of the electric motor in the electric vehicle is chosen to deliver the maximum required power during the acceleration from 0 to 50 km/h in 8 seconds. The maximum power required during this acceleration is usually sufficient to meet the power requirement at the vehicle maximum speed.

17

Table 1: Vehicle parameters Parameter Wheel radius, 𝒓𝒗 Mass of vehicle+driver,𝒎 Rolling resistance coefficient, 𝝁𝒓 Aerodynamic drag coefficient,𝑪𝑫 Area of car seen from front, 𝑨𝒇 Density of air, 𝝆𝒂 Force of gravity, g

Value 0.3m 1100kg 0.01 0.38 1.1 m2 1.12kg/m3 9.81m/s2

Rolling Resistance Force: 𝐹𝑟 = µ𝑟 𝑚𝑣 𝑔𝑟 = 161.87 𝑁 (1) Air Resistance Force: 1 2 𝐹𝑎 = 2 𝜌𝑎 𝐶𝐷 𝑉𝑐𝑚𝑎𝑥 = 94.8 𝑁 (2) Force required to go up hill: 𝐹ℎ = %𝑔𝑟𝑎𝑑𝑒 ∗ 𝑚𝑣 𝑔𝑟 = 237.4 𝑁 (3) Force required for forward motion: 𝐹𝑓 = 𝐹𝑟 + 𝐹𝑎 + 𝐹ℎ (4) Power required for forward motion: 𝑃𝑓 = 𝐹𝑓 ∗ 𝑉 (5) where V is speed of vehicle. Power required for acceleration: Assuming the vehicle acceleration to be from 0 to 50 km/h (13.889 m/s) in 8 seconds 𝑉 Acceleration, 𝑎 = 𝑡 𝑓 = 1.736 𝑚/𝑠 2 𝑎

Peak Power required for acceleration, 𝑃𝑎𝑐𝑐 = 𝑚𝑣 ∗ 𝑉𝑓 ∗ 𝑎 = 26.52 𝑘𝑊 (6) As the vehicle accelerates, the required power increases until it reaches the maximum power rating of the electric motor. Then the vehicle drive train operates at its maximum power.


Ff

θ

Figure 4: Traction force components acting on a vehicle climbing a graded hill If we consider a vehicle accelerating up a graded road, then the maximum traction power demanded by the vehicle will be: 𝑃𝑚𝑎𝑥 = 𝑃𝑓 + 𝑃𝑎𝑐𝑐 = 40𝑘𝑊 (7) Assuming that in the range of speeds from 0 to 𝑉𝑓 the power required overcoming rolling resistance and aerodynamic drag is relatively small, an approximate result for the required traction power rating 𝑃𝑣 as a function of acceleration time can be given as [13] : 𝑃𝑣 ≈

𝑚𝑣 𝑉𝑓2 2𝑡𝑎

1

1

2

(1 + 𝑥 2 ) + 5 𝜌𝑎 𝐶𝐷 𝐴𝑣 𝑉𝑓3 + 3 µ𝑟 𝑚𝑣 𝑔𝑟 𝑉𝑓 𝑓

(8) 𝑃𝑣 = 16.5 𝑘𝑊 𝑉 where 𝑥𝑓 = 𝑉𝑓, assuming 𝑥𝑓 = 3, 𝑉𝑏 is vehicle 𝑏

base speed. Table 2: Vehicle performance objectives Parameter Maximum continuous cruising speed, 𝑽𝒄𝒎𝒂𝒙 Acceleration time (0 to 50 Km/h), 𝒕𝒂 %grade (at 50 km/h) Gear tr. eff (single gear tr.), 𝜼𝒕 Traction motor efficiency, 𝜼𝒎𝟐 Generator efficiency,𝜼𝒈𝟏 Rectifier efficiency,𝜼𝒓𝟏 Inverter efficiency,𝜼𝒊𝟐

Value 70km/h 8s 5% 0.95 0.95 0.95 0.95 0.95

In series hybrid vehicles the range extender is not mechanically connected to the driven wheels. Traction force is provided only by traction motor and the traction power comes from the engine-

18

generator set and/or battery via AC/DC converter. From the battery voltage VDC , DC/AC converter generates variable frequency 3-phase voltages and currents for the traction motor. The traction motor shaft turns at N2 rpm and produces traction torque T2. A single-gear transmission turns the wheels at Nv rpm with torque Tv . To find the required power rating 𝑃𝑚2 of an electric motor providing the traction power, the transmission efficiency 𝜂𝑡 should also be taken into account. Therefore, the mechanical power at the output of the traction motor is: 2𝜋 60

𝑃𝑣 𝜂𝑡

𝑃𝑚2 = 𝑇2 𝑁2 ( ) =

=

16.5 09

= 18.34 𝑘𝑊

(9)

The traction force in terms of traction torque can be given as: 𝑇 𝐹𝑣 = 𝑣 (10) 𝑟𝑣

This propels the vehicle at a speed𝑉, where 𝑟𝑣 is the wheel radius. The traction power propelling the vehicle forward is: 2𝜋 𝑃𝑣 = 𝐹𝑣 𝑉 = 𝑇𝑣 𝑁𝑣 ( 60 ) (11) In the series hybrid of Fig. 5[14], the entire traction power must be provided by the traction motor. Therefore, the motor must be sized according to the maximum traction power requirement, which follows from the acceleration performance specification The maximum speed of the traction motor (N2max= 5000 rpm) should match the maximum vehicle speed (vmax= 90km/h = 25m/s). Since 30 π

Vmax ) rv

Nvmax = ( ) (

= 795.8 rpm

(12)

and the required gear ratio is 𝑛𝑔 =

𝜋𝑁2 max 30𝑣𝑚𝑎𝑥

𝑁

5000

𝑟𝑣 = 𝑁2𝑚𝑎𝑥 = 795.8 = 6.28 𝑣𝑚𝑎𝑥

(13) PM BLDC machines inherently have a short constant power range due to their rather limited field weakening capability. This is a result of the presence of the permanent magnet field, which can only be weakened through production of a stator field component, which opposes the rotor magnetic field. So, the speed ratio, x, is usually less than 2 [13].


Assuming the motor speed ratio = 1.8 , the motor base speed is 𝑁 𝑁2𝑏 = 2𝑚𝑎𝑥 = 2777.8 𝑟𝑝𝑚. (14) 𝑥 The maximum torque 𝑇2𝑚𝑎𝑥 that the traction motor should produce for 0 0 (2) Where Zt is the moving average, Xt is the latest measurement and  is the weight parameter given to the latest measurement usually 0 < l < 1. In [16] it is shown that the mean (t) and the standard deviation (x) at a time t are calculated as: Zt=t 

𝜎𝑍t = √2− (1 − (1 − )2𝑡 )𝜎𝑥

(3)

EWMA computes the recent estimates of error rate by progressively down-weighting the older data, and the parameter controls how much weight is given to recent measurements of the data based on the misclassification rate of stream classifier. IV METHODOLOGY USED In Algorithm 1, we present the pseudo code of improved method OzaBoostEWMA. It has shown that, let H has a set of classifiers h1, h2, h3.., each training example is trained accordingly to a random variable drawn from a Poisson (dist) distribution, where dist is increased on-the-fly on misclassifications of training examples by previous classifier (hm-1) and decreased otherwise. The associated parameters msc, and msw are the sum of dist values scaled by half of the sum of total weight (N) for correctly and incorrectly classified examples respectively. The classification is checked by each expert in the ensemble. After the classification, the algorithm predicts a label for an instance Ii with the existence of the data point Ii in the first iteration, the true label is received. If an expert classifies correctly (i.e. hm(Ii) = yi), the prediction variable becomes true and the error monitoring is handled by EWMA chart with smoothing factor parameter  = 0.01, which means that the parameter  determines the rate at which the “older" data get in to EWMA statistics. The true or false variable prediction result tells the class membership for a given instance and which in turn

56

helps to determine the factor to down-weight or keep constant for the next iteration. On the other hand, if an expert misclassifies the training examples (i.e. hm(Ii) = yi), the variable prediction becomes false and the error monitoring is done using EWMA again with smoothing factor parameter  = 0.01 and next the predicted label is then submitted to the change detector. The EWMA drift detection method computes three levels of threshold values (in-control, warning and outcontrol). Initially, the drift detection setting will set to in-control level where there is no change between the two distributions. The change is fagged when, Zt>0 + LZt, which is out-control level, where L is the control limit calculated from ARL. The warning threshold can be also calculated as Wt = 0.5Lt, Zt>0 + WtZt, where Wt is the warning threshold. The Average Run Length (ARL) is the expected time between false positive detections usually determined by a control limit parameter which helps for the performance measure of fast detection strategy. A. Algorithm and Parameter Selection We choose three algorithms for our evaluation including our proposed method. The first one is OzaBoost predictive classification model without drift detection method and the second is OzaBoostADWIN classifiers which is an extended version of OzaBoost with ADWIN change detection method. The reason why we choose these models for our evaluation can be found in different literatures [3, 5, 6, 10, 13]. Since we have employed some of windowed techniques for the purpose of comparison, we should involve on parameter selection to determine the number of examples used by the model. For all algorithms chosen for comparative evaluation, we keep on default setting (parameters) provided by MOA framework. B. Data sets Table 1, presents the synthetic experiments used in our evaluation whereboth abrupt and gradual drifts were presented. SEA-1 and SEA-2 were synthesized using the SEA generator presented in [23. Similarly, AGRAWAL-1 and AGRAWAL-2 experiments are as stated in[25]. Additionally, all synthetic experimentsembed 10% of noisy data. From real benchmarking datasets, we took the known concept drift benchmarking datasets electricity and poker-hand as proposed by different literatures [22, 24].


Table 1: Synthetic data sets used in the experiment Drift # of Position(s) width drift s (t0) (w) 34,000 AND 2 67,000 0 SEA-1 34,000 AND 1,000 2 67,000 each SEA-2 34,000 AND AGRAWAL 2 67,000 0 -1 34,000 AND 1,000 AGRAWAL 2 67,000 each -2 C. Experimental Setup An experiment was implemented using Java under Machine learning and Data mining framework called Massive Online Analysis (MOA) [10] developed at Waikato University, New Zealand. The experiment was conducted on Intel(R), core(TM) i3 CPU M 380 @2.53GHz (4CPUs) 4.0 GB of RAM.

V. RESULTS AND DISCUSSION As the empirical results have shown in Table 2 that our improved proposal OzaBoostEWMA achieves higher accuracy than Oza’s original predictive classification model (OzaBoost) without drift detection and the state-of-art classification model (OzaBoostADWIN) with ADWIN drift detection method. Table 2. Accuracy of different algorithms in (%)

SEA-1 SEA-2 AGRAWAL1 AGRAWAL2 ELEC POKER

Oza OzaBoost OzaBoost Boost ADWIN EWMA 88.4 85.1 88.0 87.5 84.8 88.8 93.5

89.0

94.1

93.1 88.5 87.0

91.7 87.6 86.8

93.5 90.2 93.2

For the sake of verifying the existence of statistical significance difference among all methods, we employed a parameter free Friedman’s test followed by Bonferroni-Dunn's test at significant level of 95%. The need for using combination of both tests is due its corrections provided to diminish type II errors [26]. Following this, the test results have shown that our improved proposal OzaBoostEWMA is capable of surpassing others OzaBoost and OzaBoostADWIN in terms of accuracy with average improvement of ±3.2%. VI. CONCLUSIONS AND FUTURE WORK Both Incremental and ensemble approaches of learning methods were proposed solutions of concept drifting big data streams in the literature. Our proposed improvement OzaBoostEWMA is able to enhance its accuracy by performing incremental online boosting with EWMA

57


procedure to detect and adapt to drifts more quickly on-the-fly. An empirical evaluation has shown that combining Online Boosting with the EWMA drift detector is a feasible strategy in terms of accuracy. Although there is no significant difference between algorithms under evaluation, based on our Friedman's test followed by Bonferroni-Dunn's test, our improvement OzaBoostEWMA outperforms others with its improvement on average ±3.2%. However, with the emergence of big data using 3Vs (Volume, Velocity and Variety)and other inherent open challenges which are apparent for further researching, We identified some of open issues such as incorporating the finer scale change detection strategies for slow gradual changes and delayed concepts, applying the diversified classifiers in an ensemble, optimization of big data challenges in high dimensions. REFERENCES [1] C. Eaton, T. Deutsch, D. deRoos, G. Lapis, P. C. Zikopouslos, "Understanding Big Data: Analytics for Enterprise class Handoop and Streaming Data," Ed. New York: McGrawHill, 2012, pp.31-40. [2] A. Bifet, W. Fan, "Mining Big Data: Current Status, and Forecast to the Future," SIGKDD Explorations, vol. 14, no. 2, p. 5, December 2012. [3] J. Gama, I. Zliobai’e, A. Bifet, M. Pechenizkiy, A. Bouchachia, "A Survey on Concept drift(s) Adaptation," ACM Computing Surveys, 2013. [4] M. Harel , K. Crammer, R. El-Yaniv, S. Mannor, "Concept drift(s) Detection Through Resampling," in Proceedings of the 31st International Conference on Machine learning, Beijing, 2014. [5] I. Zliobait'e, M. Pechenizkiy, "Reference Framework for Handling Concept drift(s): An Ap-plication Perspective," Neurocomputing, December 2010. [6] G. Krempl et al., "Open Challenges for Data Stream Mining Research," SIGKDD Explorations, vol. 16, no. 1, 2014. [7] J. N. v. Rijn, G. Holmes, B. Pfahringer, J. Vanschoren, "Algorithm Selection on Data Streams," 2014.

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[8] J. Gama, P. Medas, G. Castillo, P. Rodrigues, "Learning with drift detection," in In SBIA Brazilian Symposium on Artificial Intelligence, Brazil, 2004, pp. 286–295. [9] M. Baena-García, R. Morales-Bueno, R. Gavaldá, J d. C. Ávila, A. Bifet, R. Fidalgo, , "Early drift detection method," in In Fourth International Workshop on Knowledge Discovery from Data Streams, 2006. [10] A. Bifet, R. Kirkby, “Massive Online Analysis Manual, “ 2009. [11] A. Bifet, B. Pfahringer, G. Holmes, J. Read. (2012) Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data. [12] A. Bifet, et al., "MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering," in JMLR: Conference Proceedings, 2011. [13] N. C. Oza, S. Russell, "Online Bagging and Boosting," University of California, Computer Science Division, 2001. [14] A. Bifet, R. Gavald`, "Learning from Time-Changing Data with Adaptive Windowing," 2006. [15] I. Žliobait. Class Lecture, Topic:"Adaptive preprocessing for streaming Data" Bournemouth University, September 22, 2011. [16] G. J. Ross, N. M. Adams, D. K. Tasoulis, David J. Hand ,"Exponentially weighted moving average charts for detecting concept drift," Pattern Recognition Letters, Elsevier, 2012. [17] A. Bifet, G. Holmes, B. Pfahringer, E. Frank. Fast Perceptron Decision Tree Learning from Evolving data streams, 2010. [18] A. Bifet , G. Holmes, B. Pfahringer. Leveraging Bagging for Evolving data streams, 2010. [19] A. Bifet , B. Pfahringer, G. Holmes, J. Read. Batch-Incremental versus InstanceIncremental Learning in Dynamic and Evolving Data, 2012. [20] L. G. Malik, P. B. Dongre, "Stream Data Classification and Adapting to Gradual Concept drift," International Journal of Advance Research in Computer Science and Management Studies, vol. 2, no. 3, March 2014.


[21] J. Gama , P. P. Rodrigues, G. Castillo. Evaluating Algorithms that Learn from Data Streams, 2009. [22] N. S. Wales, M. Harries. (1999) Splice-2 comparative evaluation: Electricity pricing. [23] W. N. Street, Y. Kim, "A streaming ensemble algorithm (sea) for large classification," in In Proc. of the seventh ACM SIGKDD international confer-ence on Knowledge discovery and data mining, 2001, pp. 377-382. [24] D. Deugo, F. Oppacher, R. Cattral, "Evolutionary data mining with automatic rule generalization," In Recent Advances in Computers, Computing and Communications, pp. 296-300, 2002. [25] R. Agrawal, A. Swami, T. Imielinski, "Database mining:A performance perspective," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 914-925, 1993. [26] G.W. Corder, D.I. Foreman, “Nonparametric Statistics for NonStatisticians: A Step-by-Step Approach,” Wiley, 2001. [27] W. Zang, P. Zhang, C. Zhou, L. Guo, “Comparative study between incremental andensemble learning on data streams: Case study,” Springer, Journal of Big Data, 2014.

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A Study of Distributed Denial of Service Attacks in Mobile Network T. Pandikumar Department of Computer & Information Technology, College of Engineering Defense University, Bishoftu, Ethiopia E-Mail: [email protected] Abstract: A

denial of service (DoS) attack is characterized by an explicit attempt by an attacker to prevent legitimate users of a service from using the desired resources. A DDoS (Distributed DenialOf-Service) attack is a distributed, large-scale attempt by malicious users to flood the victim network with an enormous number of packets. This exhausts the victim network of resources such as bandwidth, computing power, etc. The victim is unable to provide services to its legitimate clients and network performance is greatly deteriorated. The distributed format adds the “many to one” dimension that makes these attacks more difficult to prevent. The task of deploying these attack daemons requires the attacker to gain access and infiltrate the host computers. The third component of a distributed denial of service attack is the control master program. Its task is to coordinate the attack. Finally, there is the real attacker, the mastermind behind the attack. By using a control master program, the real attacker can stay behind the scenes of the attack. Security in mobile ad hoc networks is a hard to achieve due to dynamically changing and fully decentralized topology as well as the vulnerabilities and limitations of wireless data transmissions. Existing solutions that are applied in wired networks can be used to obtain a certain level of security. Nonetheless, these solutions are not always being suitable to wireless networks. Therefore ad hoc networks have their own vulnerabilities that cannot be always tackled by these wired network security solutions. Keywords: Mobile network, Denial of service, Flooding, drooping, Attacker, Reflector, Victim, Prevention. I. INTRODUCTION

In view of the increasing demand for wireless information and data services, providing faster and reliable mobile access is becoming an important concern. Nowadays, not only mobile phones, but also laptops and PDAs are used by people in their professional and private lives. These devices are used separately for the most part that is their applications do not interact. Sometimes, however,

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R. Sakunthala Jenni Department of Computer Science & Engineering Kalaivani College of Technology Coimbatore, India E-Mail: [email protected] a group of mobile devices form a spontaneous, temporary network as they approach each other. A mobile network (MN) is a spontaneous network that can be established with no fixed infrastructure. This means that all its nodes behave as routers and take part in its discovery and maintenance of routes to other nodes in the network i.e. nodes within each other's radio range communicate directly via wireless links, while those that are further apart use other nodes as relays. Its routing protocol has to be able to cope with the new challenges that a MN creates such as nodes mobility, security maintenance, and quality of service, limited bandwidth and limited power supply. These challenges set new demands on MN routing protocols. Ad hoc networks have a wide array of military and commercial applications. They are ideal in situations where installing an infrastructure network is not possible or when the purpose of the network is too transient or even for the reason that the previous infrastructure network was destroyed. One of the very distinct characteristics of MNs is that all participating nodes have to be involved in the routing process. Traditional routing protocols designed for infrastructure networks cannot be applied in ad hoc networks, thus ad hoc routing protocols were designed to satisfy the needs of infrastructure less networks. Due to the different characteristics of wired and wireless media the task of providing seamless environments for wired and wireless networks is very complicated. One of the major factors is that the wireless medium is inherently less secure than their wired counterpart. Most traditional applications do not provide user level security schemes based on the fact that physical network wiring provides some level of security. The routing protocol sets the upper limit to security in any packet network. If routing can be misdirected, the entire network can be paralyzed. This problem is enlarged in ad hoc networks since routing usually needs to rely on the trustworthiness


of all nodes that are participating in the routing process. An additional difficulty is that it is hard to distinguish compromised nodes from nodes that are suffering from broken links. Recent wireless research indicates that the wireless presents a larger security problem than conventional wired and wireless networks. Distributed Denial of Service (DDoS) attacks has also become a problem for users of computer systems connected to the Internet. A DDoS attack is a distributed, largescale attempt by malicious users to flood the victim network with an enormous number of packets. This exhausts the victim network of resources such as bandwidth, computing power, etc. The victim is unable to provide services to its legitimate clients and network performance is greatly deteriorated. II. RESEARCH PROBLEM Preventing DDoS attacks is difficult especially due to the following problems:  Very little has been done to compare, contrast, and categorize the different ideas related to DDoS attacks and defenses. As a result it is difficult to understand what a computer network user needs to do and why to prevent the threat from DDoS attacks.  There are no effective defense mechanisms against many important DDoS attack types.  There is no guidance on how to select defense mechanisms.  Existing defense mechanisms have been evaluated according to very limited criteria. Often relevant risks have been ignored (such as in [1]) or evaluations have been carried out under ideal conditions (such as in [2]). No research publications exist for giving a systematic list of issues related to defense evaluation. III. PROPOSED GOALS The main goals of is researches are:  To measure network performance which include parameters like first packet received at [s], last packet received at [s], total number of collisions, total number of bytes received, total number of packets received and total energy consumption etc.

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

Study the effect of Distributed Denial of Service (DDoS) attacks under different number of attackers and different node mobility.  To measure impact of Distributed Denial of Service (DDoS) attacks on network performance.  Detection of Distributed Denial of Service attacks in Mobile Ad-hoc Network.  Prevention of Distributed Denial of Service attacks in Mobile Ad-hoc Network using defense techniques.  Analysis of the effectiveness of the prevention techniques. To achieve the main goal the following have to be achieved:  In depth understanding of how the AODV protocol operates in route discovery and maintenance.  Understanding of Glomosim, network simulator, where the implementation of the DDoS attacks take place.  Implementation of selected routing attacks and development of the prevention techniques that prevent them. IV. PROPOSED TECHNIQUES A. DDoS ATTACK MECHANISMS As one of the major security problems in the current Internet, a denial-of-service (DoS) attack always attempts to stop the victim from serving legitimate users. A distributed denial-of-service (DDoS) attack is a DoS attack which relies on multiple compromised hosts in the network to attack the victim. There are two types of DDoS attacks. The First type of DDoS attack has the aim of attacking the victim node in order to drop some or all of the data packets sent to it for further forwarding even when no congestion occurs, which is known as Malicious Packet Droppingbased DDoS attack. The second type of DDoS attack is based on a huge volume of attack traffic, which is known as a Flooding-based DDoS attack. A flooding-based DDoS attack attempts to congest the victim's network bandwidth with real-looking but unwanted IP data. As a result, legitimate IP packets cannot reach the victim due to a lack of bandwidth resource. To amplify the effects and hide real attackers, DDoS attacks can be run in two different distributed coordinated fashions. In the


first one, the attacker compromises a number of agents and manipulates the agents to send attack traffic to the victim. The second method makes it even harder to determine the attack sources because it uses reflectors. A reflector is any host that will return a packet if it receives a request packet [7]. For example, a Web server can be reflector because it will return a HTTP response packet after receiving a HTTP request packet. The attacker sends request packets to servers and fakes victim's address as the source address. Therefore, the servers will send back the response packets to the real victim. If the number of reflectors is large enough, the victim network will suffer exceptional traffic congestion. B. Problems Due to DDoS Attacks:  DDoS attack is an attempt to make a computer resource unavailable to its intended users.  The bandwidth of a router between the Internet and a LAN may be consumed by DDoS, compromising not only the intended computer, but also the entire network.  Slow network performance (opening files or accessing web sites) due to DDoS attacks.  Unavailability and inability to access a particular web site due to DDoS attacks.  Dramatic increase in the number of spam emails received due to DDoS attacks. C. Malicious Packet Dropping Based DDoS Attack Mechanism Malicious packet dropping attack presents a new threat to wireless ad hoc networks since they lack physical protection and strong access control mechanism. An adversary can easily join the network or capture a mobile node and then starts to disrupt network communication by silently dropping packets. It is also a threat to the Internet since the various software vulnerabilities would allow attackers to gain remote control of routers on the Internet. If malicious packet dropping attack is used along with other attacking techniques, such as shorter distance fraud, it can create more powerful attacks i.e. black hole which may completely disrupt network communication. D. Flooding Based DDoS Attack Mechanisms Flooding-based DDoS attacks involve agents or reflectors sending large volume of unwanted traffic to the victim [3]. The victim will be out of service

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for legitimate traffic because its connection resources are used up. Common connection resources include band width and connection control in the victim system. Generally, floodingbased DDoS attacks consist of two types: direct and reflector attacks [4]. Figure 1 is another view of the process of a direct flooding-based DDoS attack. The architecture of the direct attack is same as the typical DDoS attack illustrated in Figure 4.1. The agents send the Transmission Control Protocol/Internet Protocol (TCP), the Internet Control Message Protocol (ICMP), the User Datagram Protocol (UDP), and other packets to the victim directly. The response packets from the victim will reach the spoofed receivers due to IP spoofing. In a reflector attack, presented in Figure 2, the response packets from reflectors truly attack the victim. No response packets need be sent back to reflectors from the victim. The key factors to accomplishing are flector attack include: setting the victim address in the source field of the IP header and finding enough reflectors. Basically, an attacker can utilize any protocol as the network layer platform for a flooding-based attack [5]. Direct attacks usually choose three mechanisms: TCP SYN flooding, ICMP echo flooding, and UDP data flooding [12]. The TCP SYN flooding mechanism is different from the other two mechanisms. It causes the victim to run out of all available TCP connection control resources by sending a large number of TCP SYN packets. The victim cannot accept a new connection from a legitimate user without new available control resources. ICMP echo flooding-based attacks will consume all available bandwidth as a large number of ICMP ECHO REPLY packets arrive at the victim.

Figure 1: A direct flooding-based DDoS attack


UDP data flooding-based attacks achieve the same result as ICMP echo attacks by sending a large number of UDP packets to either random or specified ports on the victim [6].

Figure 2: A reflector flooding-based DDoS attack Reflector attacks rely on protocol features in the victim. Any protocol which will send a response message to the victim can be utilized for a reflector attack. To create a stronger reflector attack, the attacker can utilize the packet amplification technique. An amplifier is used between the agents and the real reflectors. It broadcasts the request packets from agents to all reflectors address of which are within the broadcast address range. Most routers support the IP broadcast feature in current network [6].Therefore, there exist a large number of potential amplifiers. This helps an attacker increase the volume of an attack with a lesser reflectors-finding cost. For attacks which target the bandwidth of the victim, the architecture of the victim network decides how large a volume of attack traffic is needed. Increasing the bandwidth of links and erasing bottleneck links in its own network can increase the ability of a victim to tolerate flooding-based attacks. An attack which target connection control resources usually relies on flaws of the control mechanism of the operating system of the victim. Regularly updating software patches for the operating system can fix these problems and avoid being effectively attacked in future. Advantages of the proposed scheme:  The proposed scheme incurs no extra overhead, as it makes minimal modifications to the existing data structures

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and functions related to blacklisting a node in the existing version of pure AODV.  Also the proposed scheme is more efficient in terms of its resultant routes established, resource reservations and its computational complexity.  If more than one malicious node collaborate, they too will be restricted and isolated by their neighbors, since they monitor and exercise control over forwarding RREQs by nodes. Thus the scheme successfully prevents DDoS attacks. E. Proposed Prevention Technique Disabling IP Broadcasts:Abroadcast is a data packet that is destined for multiple hosts. Broadcasts can occur at the data link layer and the network layer. Data-link broadcasts are sent to all hosts attached to a particular physical network. Network layer broadcasts are sent to all hosts attached to a particular logical network. The Transmission Control Protocol/Internet Protocol (TCP/IP) supports the following types of broadcast packets:  All ones: By setting the broadcast address to all ones (255.255.255.255), all hosts on the network receive the broadcast.  Network: By setting the broadcast address to a specific network number in the network portion of the IP address and setting all ones in the host portion of the broadcast address, all hosts on the specified network receive the broadcast. For example, when a broadcast packet is sent with the broadcast address of 131.108.255.255, all hosts on network number 131.108 receive the broadcast.  Subnet:By setting the broadcast address to a specific network number and a specific subnet number, all hosts on the specified subnet receive the broadcast. For example, when a broadcast packet is set with the broadcast address of 131.108.4.255, all hosts on subnet 4 of network 131.108 receive the broadcast. Because broadcasts are recognized by all hosts, a significant goal of router configuration is to control unnecessary proliferation of broadcast packets. Cisco routers support two kinds of broadcasts:


directed and flooded. A directed broadcast is a packet sent to a specific network or series of networks, whereas a flooded broadcast is a packet sent to every network. In IP internetworks, most broadcasts take the form of User Datagram Protocol (UDP) broadcasts. V. SIMULATION ENVIRONMENT A. GloMoSim OVERVIEW GloMoSim stands for Global Mobile information systems Simulation library [8], [9], [10], was designed as a set of library modules, each of which simulates a communication protocol in the protocol stack. The library uses the OSI layer approach and supports multiple protocols in each layer: The layers are separated and each layer has its own API. The layers interact with each other using message-passing approach. A combination of different protocols at various layers into a complete protocol suite, as well as extension with alternative protocols can be done simply. The simulator is built above PARSEC [11], a C-based language that was developed for discrete-event simulations. The simulator enables various scenarios, using configuration files, and allows analysis by a trace file with statistics. The visualization tool of GloMoSim, written in Java, shows the network look, nodes mobility and packet transmissions. Table 1: GloMoSim OSI library Layer Model Physical Free-space, Two-ray Data Link CSMA, MACA, 802.11, TSMA Network Bellman-Ford, FISHEYE, WRP, AODV, DSR,LAR1, ODMPR Transport TCP, UDP Application CBR, HTTP, TELNET, FTP B. SIMULATION PARAMETERS Various network scenarios were analyzed to prove the model’s correctness and characteristics. Every plot was taken as an average of ten different runs. In the simulation experiment, we tested networks from 10 up to 500 mobile hosts. The area, in which the nodes were placed randomly, was chosen based on the metrics

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presented in [13] and [15] to maintain the network density and connectivity as constant and balanced. In all the simulations, we used standard parameters of the channel and radio model: channel capacity of 2MB/s, free space propagation model and radio propagation range of 250 meters. The IEEE 802.11 protocol was used as the medium access control protocol. The mobile nodes use the random waypoint as the movement model. The range of the speed is from 5 to 20 m/s. Simulations in [34] have shown that minimum speed of zero in the random waypoint model cannot reach a steady state because the speed is continuously decreasing as the simulation progresses. The solution is to set a positive minimum speed and, thus, we give our simulation a minimum speed of 5. The pause time is varied randomly between 0 and 500. The traffic was produced using a traffic generator, which made randomly constant bit rate (CBR) sessions. The data packet size was 64 Bytes and no fragmentation was used. We avoided data packet transmissions between neighbors, and all the results refer to packets on routes that are above 1hop length, so more accurate results are achieved. Default values for some of the protocol parameters are given in Table 2. These values are not attempted to be the optimal ones for any network, but we found them as reasonable and effective in the simulation. The original parameters of AODV, as described in RFC 3561 [14] section 10, remain unchanged. VI. IMPLEMENTATION OF PROPOSED TECHNIQUE This section presents implementation of Malicious Packet Dropping and Flooding based DDoS attacks in AODV. It also presents implementation of detection and prevention mechanism for flooding attack. A. IMPLEMENTATION OF ATTACK MECHANISMS In this section, I have presented attack mechanisms for DDoS attacks. There are two types of attack mechanisms: i) Malicious Packet Dropping ii) Flooding. Malicious Packet Dropping is a technique to implement DDoS attack in which a node drops data packets (conditionally or randomly) that it is supposed to forward. Another technique to implement DDoS attack is Flooding,


which deliver unusually large amount of data or control packets to the whole network or some target nodes. Both of them are implemented using AODV file. I have used GloMoSim Simulator for simulation. In GloMoSim, there is “aodv.pc” file for AODV routing protocol which is used for implementing code for both the attack mechanisms. Table 2: Configuration parameters Parameter Value Rating interval for rating 8.5 s calculation and distribution γ, δ, weight of past behavior for 0.8 direct and total rating μ, weight of past belief 0.8 ∆, the deviation test window size 0.5 w, maximum weight of indirect 5 rating (depends on the number of neighbors) Rating threshold for misbehaving -0.2 nodes (together with some minimal observations. As much as the rating is smaller, the smaller number of observations that are required) Reliability threshold for path 0.25 selection (together with some minimal observations) Trustworthy threshold for 0.75 accepting reports A unreliable node’s rating (-0.2- 0.25) Reliable node’s rating 0.25 - 0.75) Very reliable node’s rati [0.75 - 1] Reply delay 80 ms B. IMPLEMENTATION OF FLOODING BASED DDOS ATTACK Another class of DDoS attacks is Flooding. A flooding-based DDoS attack attempts to congest the victim's network bandwidth with real-looking but unwanted IP data. As a result, legitimate IP packets cannot reach the victim due to a lack of bandwidth resource. Here, we introduce a new attack in the mobile ad hoc network, which is called the Ad Hoc Flooding Attack. The attack acts as an effective denial- of- service attack against all currently proposed on demand ad hoc network routing protocols, including the secure protocols. Thus, existing on-demand routing protocols, such

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as AODV cannot be immune from the Ad Hoc Flooding Attack. In the following, we describe the effect of the Ad Hoc Flooding Attack against the AODV protocol. Overview of aodv routing protocol The Ad Hoc On-Demand Distance Vector (AODV) routing protocol is built on the Dynamic Destination Sequenced Distance-Vector (DSDV) algorithm. AODV is an improvement on DSDV because it typically minimizes the number of required broadcasts by creating routes on a demand basis, as opposed to maintaining a complete list of routes as in the DSDV algorithm. AODV is classify as a pure on-demand route acquisition system, since nodes that are not on a selected path do not maintain routing information or participate in routing table exchanges. In general, the operations in AODV can be classified into two phases: the route construction phase and the route maintenance phase. The main work in route construction phase is to create a route from source node to destination node while in route maintenance phase, the main work is to rebuild a route between source and destination nodes since the previous found route may be broken due to the nodes movement. In the route construction phase, when a source node needs to send packets to a destination node and there is no valid route between the source node and the destination node, the source node initiates a path discovery process to locate the destination node. The source node will broadcast a route request (RREQ) packet to explore a route to the destination. AODV uses the destination sequence number to ensure that all routes are loop-free and contain the most recent route information. During the route discovery process, each intermediate node that receives the RREQ packet will re-broadcast the packet to its neighbors. The duplicate copies of the same RREQ that received by an intermediate node will be discarded. Once the RREQ reaches the destination or an intermediate node with a “fresh enough” route to the destination is located, the destination/intermediate node will send a route reply (RREP) packet back to the source along the reverse routing path. Figure 3 shows the process of route discovery in AODV. In Figure 3 (a), the source node broadcast RREQ packet to its neighbor, and so on, while in Figure (b), the


destination node send the RREP packet back to the source node. The route between source and destination nodes may be broken since the nodes along the route will move from time to time. If a source node moves, it is able to reinitiate the route discovery protocol in order to create new route to the destination. If a node along the route moves, its upstream neighbor will notice the move and propagate a Route Error (RERR) packet to each of its active upstream neighbors to inform them the erasure of that part of the route. These nodes in turn propagate the RRER packets to their upstream neighbors, and so on until the source node is reached. The source node may then choose to reinitiate route discovery for that destination if a route is still desired.

to flood attack, two gray nodes do not send data packets to each other because on that path congestion occurs. But by using Retry RREQ, two gray nodes find another path which larger than the path through attacking node, and communicate with each other. Similarly, Handle RREQ function handles the all RREQ initiated on attacking node and mitigate the effect of flooding based DDoS attack.

Figure 4: Another path in case of flood attack using retry RREQ VII. RESULTS

Figure 3: AODV route discovery (1) PREVENTION OF FLOODING BASED DDoS ATTACK There are two techniques which are used to prevent or mitigate the effect of flood attack. These are: i) Existing technique and ii) Proposed technique. Both are implemented by changing the code of aodv.pc file of GloMoSim simulator. a) Existing Prevention Technique Existing technique uses two functions to prevent flood attack. They are: i) Retry RREQ and ii) Handle RREQ. Retry RREQ function tries to find another path in case of flood attack. Figure 4 shows a black node which is flood attack node and two gray nodes which want to communicate. Due

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Two types of DDoS attacks mechanisms are implemented; first we measure the effect of Packet Dropping and Flooding attack on network performance. Then, we compare these two attack mechanisms and analyze their effects. In next section, we analyze the effect of different prevention techniques and shows that our proposed technique is better than existing prevention technique. (i) Effect of Packet Dropping Based DDoS Attack Mechanism Table 3 and Figure 5 shows the effect of packet dropping attack on number of collisions per network with varying number of attackers. As the number of attackers increases, it causes increase in number of collisions i.e. packets are unable to reach at their destination. Thus, we can predict that as the number of attackers increases, the performance of the network will deteriorate even further. (ii) Comparison of DDoS Attack Mechanisms Table 4 and Figure 6 show the effects of different attack mechanism on packet delivery ratio. From the table it is clear that flooding based DDoS attack has greater effect on PDR. As shown in the table PDR of Flooding is very less as compare to Packet Dropping. But it is easy to prevent than packet dropping.


Figure 5: Effect of packet dropping on number of collisions with varying number of attackers Table 4: Compare PDR of two DDoS attack mechanisms with varying number of attackers Number of Packet delivery ratio (PDR) attackers Withou Packet Flooding per network Attack dropping based based DDoS DDoS attack attack 3 .926 .833 .32 4 .926 .813 .31 5 .926 .75 .22 6 .926 .66 .20 7 .926 .583 .175 8 .926 .55 .15 9 .926 .50 .12

Compare PDR of Two DDoS Attack Mechanisms

pkt delivery ratio

Table 3: Effect of packet dropping on Number of collisions with varying no. of attackers Number of Number of collisions per attackers per network network Without Packet attack dropping based DDoS attack 3 11 12 4 11 14 5 11 15 6 11 17 7 11 18 8 11 22 9 11 24

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

without attack packet dropping attack flood attack

0

2

4

6

8

10

number of attackers

Figure 6: Compare PDR of two DDoS attack mechanisms with varying number of attackers VIII. CONCLUSION Detection and prevention of DDoS attacks is a part of an overall risk management strategy for an organization. Each organization must identify the most important DDoS risks, and implement costeffective set of defense mechanisms against those attack types causing the highest risk for business continuity. Studies and news about real-life DDoS attacks indicate that these attacks are not only among the most prevalent network security risks, but that these attacks can also block whole organizations out of the Internet for the duration of an attack. The risk from DDoS attacks should not thus be underestimated, but not overestimated, either. In the future the problem from DDoS attacks will most probably increase because the number of hosts connected in the Internet increases, access lines get faster, software products get more complex, and security continues to be difficult for an ordinary home user and even many organizations. The more there are hosts in the Internet, th emore of them can potentially be used for DDoS purposes. The intensity of DDoS attacks can also increase, as a higher number of hosts can produce more traffic over faster Internet access lines. As software gets more complex, more vulnerability will reside in them to be used for compromising hosts. The fast pace of new revisions does not make the situation easier. Finally, it will continue to be difficult to evaluate security risks in existing computer systems, especially by ordinary people. REFERENCES 1. D. Adkins, K. Lakshminarayanan, A. Perrig, and I. Stoica; Towards a morefunctional and secure

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network infrastructure; University of California, Berkeley,Tech. Rep. UCB/CSD-03-1242, 2003. 2. D. Sterne, K. Djahandari, B. Wilson, B. Babson, D. Schnackenberg, H. Holliday, and T. Reid; Autonomic response to distributed Denial of Service attacks; In Proceedings of Recent Advances in Intrusion Detection, 4th InternationalSymposium, Davis, California, USA; Oct. 2001; pp. 134–149. 3. Yonghua You; A defense framework for flooding-based DDoS attacks; Master of Sc. Thesis; Queen's UniversityKingston, Ontario, Canada; August 2007. 4. J. MÄolsÄa; Mitigating denial of service attacks in computer networks; PhD thesis; Helsinki University of Technology, Espoo, Finland; June 2006. 5. Stephen M. Specht and Ruby B. Lee; Distributed Denial of Service: Taxonomies of Attacks, Tools, and Countermeasures; Proceedingsof the 17th International Conference on Parallel and Distributed Computing Systems, 2004 International Workshop on Security inParallel and Distributed Systems, pp. 543-550; September 2004. 6. L. Bajaj, M. Takai, R. Ahuja, R. Bagrodia, and M. Gerla; Glomosim: A scalable network simulation environment; Technical Report 990027, UCLA Computer Science Department; 1999;

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7. J. Nuevo; A comprehensible Glomosim tutorial; March 2003;11. PARSEC; Available on: http://pcl.cs.ucla.edu/projects/PARSEC. 8. Cisco Systems, Inc.; Characterizing and tracing packet floods using Cisco routers; May 2005. 9. S. J. Lee, E. M. Belding-Royer, and C. E. Perkins; Scalability study of thead hoc ondemand distance vector routing protocol; International Journal onNetwork Management, 13(2):97–114; March-April 2003; 10. C. E. Perkins, E. M. Belding-Royer, and S. R. Das; Ad hoc on-demand distancevector (AODV) routing; RFC 3561, IETF; July 2003; Available on: http://www.ietf.org/rfc/rfc3561.txt. 11.GloMoSim; Available on: http://pcl.cs.ucla.edu/projects/glomosim. 12. Yonghua You; A defense framework for flooding-based DDoS attacks; Master of Sc. Thesis; Queen's University Kingston, Ontario, Canada; August 2007. 13. Kamanshis Biswas and Md. Liakat Ali; Security Threats in Mobile Ad Hoc Network; Master Thesis; Thesis no: MCS-2007:07; March 22, 2007. 14.J. MÄolsÄa; Mitigating denial of service attacks in computer networks; PhD thesis; Helsinki University of Technology, Espoo, Finland; June 2006. 15. Felix Lau, Stuart H. Rubin, Michael H. Smith and Ljiljana TrajkoviC; Distributed Denial of Service Attacks; 2275-2280/2004 IEEE.


Productivity Improvement through Lean Manufacturing Tools: A Case Study of Nazareth Garment Share Company Lijalem Mulugeta Kitila Mechanical and Electromechnical Engineering Department Hawassa University, Hawassa, Ethiopia E-mail: [email protected] Abstract: Lean manufacturing (LM) involves a variety of tools and techniques such as one-piece flow, kaizen, cellular manufacturing, standardized work, work place organization or visual management. The major purposes of the use of lean manufacturing tools are to increase productivity, improve product quality, reduce inventory, reduce lead time, and eliminate manufacturing waste or non-value added activities. Nazareth Garment Share Company follows the conventional production system and is facing problems concerning, long production lead times, poor line balancing, long transportation and material movement, etc. The main objective of this research is to enhance productivity by minimizing/eliminating problems, wastes found in company, and increase market competency. A description and a critical analysis to the company’s productive system was made, acknowledging the problems it was facing. By using lean manufacturing tools like; work standardization through time study, and line balancing, the existing manufacturing system and the problem of N.G. Share Company (Adama city, Ethiopia), which is taken as a case for the study, is assessed by collecting the data from both primary and secondary resources. In the investigation, high work-in-process, poor line balancing, high manufacturing cycle time, high production lead time, unbalance work assignment, long movement-and-transportation of materials in the company have been found out. After implementation of some mentioned lean tools, results observed involve cycle time is reduced to 3 minutes (reduced by 32.73%), cycle time is balanced with takt time, number of operators in sewing line of trousers with one welted pocket (product) are reduced to 14 through time study; standard allowable minute of product is standardized to 41 minutes, productivity is increased to 140 trousers per day, total labour sewing productivity increased to 10 trouser per day, sewing direct labour cost per day per line reduced to 367.5 ETB per day, and sewing direct operator/labour cost per unit reduced to 2.65 ETB per trouser. The implementation of the new LM practices can lead to better product quality and greater participation by workers in efforts to improve manufacturing processes, the products, and

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the company as a whole. It is also examined its impact on the profit of the company by producing quality products and timely delivery, as well customer satisfaction. Keywords: Garment manufacturing, Lean manufacturing tools, Time study, Line balancing, Takt time.

I. INTRODUCTION

In Ethiopia, garment industry is one of the industries that have potential and believed in developing the economy of the country. History depicts that this industry sector has been a base for many successful industrial developments and hence Ethiopian government has defined a policy where one of the tasks identified is rapid export growth through production of high value agricultural products and increased support to export oriented manufacturing sectors such as textile and garment [1]. In developed countries due to the increasing labour wage, the apparel manufacturing has been migrating from the high wage developed world to low wage developing countries [2]. Even though the labour cost is cheaper than in developed countries; due to the specific market nature of the garment industries for example: the short production life cycle, high volatility, low predictability, high level of impulse purchase, the quick market response; garment industries are facing the greatest challenges these days [3]. Garment industries in developing countries are more focused on sourcing of raw materials and minimizing delivery cost than labour productivity because of the availability of cheap labour. Due to this, labour productivity is lower in developing countries than in the developed ones. Now the worry is about labour productivity making production flexible; because the fashion industry is highly volatile if the orders are not fulfilled on time, the fear for losing business is real [4]. Even today, industries are getting the same or more volumes (orders), but the number of styles they have to handle has increased drastically. Earlier industries were getting bulk order so there is no


need to worry; if the production line was set for the first time it would run for a month or at least a week or two. But nowadays due to small order quantities and complex designs, the garment industry has to produce multiple styles even within a day; this needs higher flexibility in volume and style change over [5]. The popular definition of LM and the TPS usually consists of the following [8].  It is a comprehensive set of techniques which when combined allows you to reduce and eliminate the wastes. This will make the company leaner, more flexible and more responsive by reducing waste.  Lean is the systematic approach to identifying and eliminating waste through continuous improvement by flowing the product or service at the pull of your customer in pursuit of perfection [9]. Thus the organization who wants to implement lean should have strong customer focus, should be willing to remove wastes from the processes they operate on daily basis should have the motivation of growth survival. There are numbers of LM tools which, when used in proper ways will give the best results. Once the source of the waste is identified it is easier to use the suitable lean tool to reduce or eliminate them and try to make waste free systems. As per the literature review survey, top 25 lean tools has been found [10], out of which more relevant tools related to garment companies are Kaizen, Kanban, Poke Yoka (Mistake proofing devices), Takt time (line balancing) cycle time, Cell layout (Group technology), 5S (Work place organizations), Visual stream mapping (VSM), Ergonomics work (Safety health and environment), Reduce set up time or Single minute die exchange (SMDE), Point of use system, Small lot size, Supplier management, Total productive maintenance (TPM), Multifunction employees, Uniform workload, Employee involvement (Quality circles), Total quality management (TQM), Training, Teamwork, Production smoothing, Work standardization, Visual management, Fishbone diagram (Ishikawa diagram), JIT (pull demand model), Visual displays and control, Operational planning and Six sigma. What can an organization expect as bottom-line results of applying lean thinking to eliminate waste? Documented results across various

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industries indicate the results in Table 1 can be achieved [11]. Table 1: Lean benefits [11] Element Capacity Inventory Cycle time Lead time Product development time Space First pass yield Service

Benefit 10-20% gains in capacity by optimizing bottlenecks Reductions of 30 to 40% in inventory Through put time reduced by 50 to 75% Reduction of 50% in order fulfilment Reductions of 35 to 50% in development time 35 to 50% space reduction 5 to 15% increase in first-pass yield Delivery performance of 99%

In Ethiopia the first industrialized garment industry dates back to the 1960s, with the establishment of Addis garments. According to Rahel (2010) the total garment industries in Ethiopia were 40 in number including (NGSC) [1]. However, the concept of LM is still relatively new for these industries except some garment industries like Almeda Textile P.L.C, Adwa; MAA garment and industry P.L.C, Mekelle which is introducing kaizen system. Still any studies or implementation related to the production system or lean manufacturing systems for Ethiopian garment industries are very little. Similarly, NGSC follows the traditional production system. In addition some problems exhibited in NGSC are stated as follows:  Unequal work distribution or assignment among operators due to poor line balancing,  Poor flow of materials with high backtracking,  No standard times has been studied and set for various production operations and target setting is based on guesswork or experience,  Poor flow of information to operators regarding their daily work,  Poor delivery time of finished garment due to higher lead time,  Long material transfer in the production shop,  High rework level.


Therefore, the objective of this study is enhancing productivity for garment section through lean toolsand-techniques to show that the lean methodology is not only limited to specific type of organizations, but it can be applied successfully to all organization types as long as the right transition path is applied effectively. Two basic LM tools that have been used in this study are work standardization and line balancing [6]. A. General Objective The general objective of the study is to enhance productivity through lean manufacturing tools at Ethiopian ‘NGS Company, Adama’, Ethiopia. B. Specific Objectives  To meet customer demand on time by eliminating non value added (NVA) work from the manufacturing process,  To reduce throughput time per unit/product,  To set standard working time for the selected product, and  To ensure smooth flow of materials in the production process. C. Limitation of the Study  High product mix and limitation of continuous production process.  No garment production standard or benchmark data.  Short time product availability in the production line.  Study was conducted at sewing section only. II. MATERIALS AND METHODS A. Data Presentation, Analysis and Interpretation In this study, both primary and secondary data was used for the analysis. It starts by time study, work standardization, and line balancing continuing by describing how the line was balanced. B. Time Study for Work Standardization In order to balance the sewing line as well as to increase the efficiency of the line, at first a detailed work and time study was carried out to find the task durations [12]. However, the time required to complete a task depends on a lot of factors such as the task, the operator, the properties of fabric and sub materials, working environment, quality level of the product, the hour of the day, psychology of the operator etc. To do this, the standard ‘trouser with one welted pocket’ was selected as a base line because operations differ from style to style and it is difficult to correlate all these operations of individual styles.

71

The following basic steps were followed to calculate the standard allowable minute (SAM) using stopwatch time study technique for the selected product. Step 1: Define objective of the study. This is to compute standard time of the selected product. Step 2: Select the work to be studied. In this case, a ‘Trouser with one welted pocket’ was selected. Step 3: Breaking down the operations into four main operations (phases) viz., back part preparation, front part preparation, waist-and-loop preparation and assembly operation. Under each main operation sub-elements are also divided. Step 4: Determining the number of cycle to be timed: The number of cycles to be observed for back part of the trouser is calculated as per Eq. 1:  (1) n  (zs  a x) 2 where: z=number of normal standard deviations needed for desired confidence level [13], s=sample standard deviation, a=desired accuracy, x bar=sample mean. n  [(2  2.1)  (0.05 19.68)]2 16 Cycle times As manufacturing processes are different in nature when the number of observation increases the confidence level increases, twenty cycles (observations) are taken for each element of the four main operations of ‘trouser with one welted pocket’, by comparing with the minimum 95% confidence level with percentage of error 5%. Moreover, common values for z are taken from standard table. Step 5: Extending the observed times to “normal times”: In the back part of trouser operation, the average observed time was 10.1 min., as per Eq. 2. (2) NT  OT  PR  100 where: NT=normal time, OT=observed time, PR=performance rating NT 10.1100 100  10.1min .

Step 6: Determining the allowances over and the normal time for the operation: According to the work nature and the working environment observed the required allowances are taken for all elements of the main four operations of trouser with one welted pocket. These allowance factors includes: Personal allowance (5%), Basic fatigue allowance (4%), Sitting allowance (2%), and contingency allowance of (4%) [14]. Therefore, all these factors are taken as 15% of total time. Similarly the average performance rating is taken 100% for the


ease of calculation only. This rating is adjusted average of actual ratings. Step 7: Calculating the “standard time” for the operation(s): From back part of the trouser preparation, for sub-operation of ‘right back over locking’, the SAM is calculated as normal time plus the required allowance in the production process, as per Eq. 3. (3) ST  NT  (NT A) where: ST=standard time, NT= normal time, A= allowance [69]. SAM 1.18 (1.1815%) 1.36min.

The computed SAM for right back over locking is shown in Table 2. Hence, from Table II, the total SAM for back part of trouser preparation is: 11

SAM 

 ST  11.61min. i 1

In Table 3, the standard time for each of the operation is calculated. This standard time is the maximum time to produce the trouser with one welted pocket because all the required allowances are given to each of the production processes. If all the required materials for production are available, the operators are able to produce the trailer based on the standard time. Therefore, standard time to produce one trouser with one welted pocket is summarized as in Table 3. Table 2: SAM for back part of the trouser Main Operation: Back Part of the Trouser Detail of sub-parts Observed Calcula operation time ted (min.) standar d Tot Avera time al ge (min.) Right back over 23.6 1.18 1.36 locking Left back over locking 23.6 1.18 1.36 Back leap operation 12.4 0.62 0.72 Right back peans 19.4 0.97 1.12 sewing Left back peans 19.4 0.97 1.12 sewing Back pocket facing 11.2 0.56 0.65 over locking Back pocket facing 10.6 0.53 0.62 attach with pocket bag Back pocket bag 30 1.5 1.73

72

facing assembly attach Back rise sewing 9.8 0.49 0.57 Pocket bag closing 7.2 0.36 0.42 interlock Back pocket mouth 34.8 1.74 2.01 cut stitch leap attach Total observed time 202 10.1 11.61 for back part operation Allowance-15%, Performance rating (PR)-100% Table 3: Summary of SAM for trouser with one welted pocket Main Operations of SAM Trouser Back part preparation 11.61 Front part preparation 11.80 Waist-and-loop 8.66 preparation Assembly operation 8.75 finishing Total (min.) 40.82 C. Productivity Measurement of Trouser with One Welted Pocket Before balancing, it is better to evaluate the existing productivity of the selected production line and product. Therefore, as per Eq. 4, it has standardized the benchmark target of garment (trouser with one welted pocket) at 65% organization efficiency [15] as follows.  Totalman power per line     SAM 100% Target    Totalworking min.per day 

(4) 20 worker  420 min. Target   65% 133unit per day 41min.per unit Target 

20 worker  420 min. 100%  205unit per day 41min.per unit

Therefore, the existing output (benchmark) of the selected line based on the calculated SAM is 133 trousers per day, but 205 trousers per day means the output at 100% efficiency. Moreover, to compute the target output as per each operator with each SAM and as per (4) is as follows. For example to compute the target to sew back part of the trouser per hour as follows. Target 

1operator 420 min. per day  64%  23per hour 11.61min.

The actual production capacity of the selected line is 120 ‘trouser with one welted pocket’ per day, if


no absenteeism, no electricity off, no more machine down time, if bill of material and supply of fabric from the cutting section is complete, and the total manpower in production line is 20 operators. The obtained SAM for the trouser with one welted pocket is 41min. Totallabour sewing productivity  Totalnumber of units produced per day per line Totallabour input per day per line

630 ETBper month 20 labours  525ETBper day 24 days per month Value of direct labour cost per unit

LC 



Direct labour cost per day Totalout put per day

LC 

(10)

525ETBper day  4.375ETB per trouser 120 trouser per day

(5)

D. Line Balancing The product in study is ‘trouser with one welted pocket’. The manufacturing line for this trouser has 120 trouser per day Totallabour sewing productivity  a total of 44 operations and 3 quality checking 20 operators points and a total of 47 operations. These  6 unit per operatorper day operations result in a total of 42.85min. to produce The sewing operator productivity efficiency is trouser with one welted pocket. Here, among these computed as per Eq. 6 with excluding the helpers. operations three quality checking point other than Tpo end line inspection are considered as non-value Efficiency: η 0 %   (6) 100 adding activities and they are resulting in an Tao increase of cycle time by 1.85min. In reducing this where: ηo  operator efficiency, Tpo  Total time a total time to complete the product/garment is minute produced by an operator, Tao  Total 41min. Therefore, to start balancing the line, first minute attended by operator. 6 trouser per day  41min. determine that customer actual demand, that is the Operatoreffiency 100% 59% takt time. The takt time in this case is 3min./unit, as 420 min.par day per Eq. 14. Now the focus is to balance the cycle Machine productivity  time with the takt time to prevent late deliveries, (7) Totalnumber of out put per day per line excess inventory or the use of excessive resources. Number of machineused Now, in order to balance the line, the following 120 trouser per day steps were followed. Machine productivity  (i) Specify the sequential relationships among tasks 20 machines using a precedence diagram:The precedence  6 trouser per day per machine diagram of ‘Trouser with one welted pocket’ was Line efficiency developed in order to better visualize the flow and Totalout put per day per lineSAM sequencing between operations as in Fig. 1. The 100% Totallabour per line totalworking min.per day letters inside the circle represents individual operations alongside with their standard time to complete that specific operation and it was (8) described in the Table 4. (120trouser) (41min.) Line efficiency 100  58.57% (ii) Determine the required workstation cycle (20operators) (420min.) time:Production time/day is 420 min. and required Value of labour/operator cost (LC) per trouser per output/day is 145unit. line in the sewing section: As per the company Production timeavailable per day (11) scale the average salary per labour is 630 ETB per Cycle time(CT)  Units required per day month. 420 CT 

Value of direct labour cost (DLC) 

Totalsalary per month Totalworking days per month

73

(9)

145

 3.00 min .


K L M N O P Q R S T U V W Figure 1: Precedence diagram (in min.) of ‘trouser with one welted pocket’

X

(iii) Determine minimum number of work station:The next activity is calculating minimum number of work stations in keeping the cycle time (3min.) at each work station.

Y

Minimum numbe r of workstation (N t ) n



 Time for task i

(12)

i 1

Cycle time 41min. Nt   13.66  14 3min.

Therefore, minimum number of work station required should be 14. Table 4: Task descriptions in precedence diagram Tas k A B C D E F G H I J

Product: Trouser with One Welted Pocket SA Operation Description M Right fly over locking 0.61 Left fly over locking 0.56 Right front over locking 0.84 Left front over locking 0.84 Right back over locking 1.36 Left back over locking 1.36 Belt loop operation 0.61 Back leap operation 0.72 Right front peans sewing 0.61 Left front peans sewing 0.61

74

Z A1 B1 C1 D1 E1 F1 G1 H1 I1 J1 K1 L1 M1 N1 O1 P1 Q1 R1 S1

Right back peans sewing 1.12 Left back peans sewing 1.12 Pocket attach on front 0.86 Pocket body seam stitching 0.91 Pocket top side edge sewing 1.27 Pocket bag interlocking 0.58 Right fly zip attach 0.52 Right front right fly zip assembly 0.6 attach Left fly attach with left front edge 0.77 stitch Left front stitching 0.62 Left front attach with right front at 0.59 zip Front rise sewing stitching 1.01 Back pocket facing over locking 0.65 Back pocket facing attach with 0.62 pocket bag Back pocket bag facing assembly 1.73 attach Back pocket mouth cut stitch 2.01 leap attach Pocket bag closing interlock 0.42 Back rise sewing 0.57 Side seam sewing 1.3 Inseam sewing 0.88 Label attach sewing 0.71 Loop attach on waist 1.59 Waist bag position marking 0.88 Left Right waist attach 0.93 Inside outside waist top centre 0.97 attach Waist bag attach with waist 1.12 Waist edge sewing (clean finish) 1.85 Bottom over locking 0.78 Bar tacking 1.83 Position mark for button hole 0.51 Button hole marking on waist 0.47 Position mark for button 0.48 Button sewing 0.44 Trimming 1.56 End line inspection 0.5 Total=41.00

(iv) Assign tasks:Balance the line by assigning specific tasks to form workstation 1, workstation 2 and so forth until all tasks are assigned. It is important to meet precedence and cycle time requirements as the assignments are made. An


efficient balance is one that will complete the required assembly, follow the specified sequence, and keep the idle time at each work stations to a minimum. In Table 5, tasks are assigned without violating the sequence of operation and without exceeding cycle time. From Table 5, the first work station consumes maximum 2.85min. and has an idle time of 0.15min., second workstation uses 3.00min. has no any idle time, third workstation uses 2.97min. and has an idle time of 0.03min. Similarly, it was done for other workstations, a total idle time of 1.1min. per cycle. Table 5: Tasks assigned to workstations Wor k statio n WS1 WS2 WS3

Tasks includ ed A,B,C, D I,J,M, N O,P,Q, R S,T,U, V E,F

Tim e (mi n.) 2.85

Wor k statio n WS8

Tasks included

Z,A1,B1

Tim e (mi n.) 3.00

3.00

WS9

G,E1,F1

2.91

2.97

WS1 G1,H1,I1 2.78 0 WS4 2.99 WS1 J1,K1 2.97 1 WS5 2.72 WS1 C1,D1,L1 2.96 2 WS6 H,K,L 2.96 WS1 M1,N1,O 2.81 3 1 WS7 W,X,Y 3.00 WS1 P1,Q1,R1 2.98 4 ,S1 (vii) Evaluate the efficiency of the balance: If fifteenth workstation (for whatever reason) will be opened, it will decrease the efficiency of the line balance to 91.11%.

together operations that require the same type of operation as well considering the sequence of operation. Therefore, the line is balanced with fourteen work stations and operators. If the line is balanced accordingly with customer demand, it is a must to determine the takt time. The available productive time is 420min. and being this specific order of production of 870 trousers with one welted pocket, to be delivered in one week, it gives us a takt time of 3 min./unit as per Eq. 14. Available time per shift Customer order quantity per shift 420 Takt time   3min./unit 145

Takt time 

(14)

E. Comparison of Line Balancing Figure 2 compares the line balancing analyzed in the sewing section with the solution given in this study, and some of the characteristics of each solution. In the sewing line that was being used previously, it was required twenty operators a cycle time of 4.46min., formed by the longest workstation cycle time (i.e. work station 18). The balance between each workstation can easily be evaluated in in Fig. 2. It is clear that the line isn’t balanced accordingly with the takt time of 3min.; instead there are workstations with times much below takt time and other with times above. Workstation 13 had also the responsibility of transporting materials from workstation to workstation, yet it would still be left with much free time, distant from takt time. With this balance, excessive work in process is noted between workstations and there’s the risk of late delivery and the use of excessive resources. Table 7 show tasks performed at each workstation described along with their time to complete those tasks at each workstation.

   n     timefor task i      i 1   Efficiency     100   Actual number of work station       Work stationcycle time   



(13) 41min. Efficiency   97.61% 14 3

This balance was achieved as presented in the Table 6, where it was also attempted to group

75

Figure 2: Balance between work stations on the line


Table 6: Line work station description

Ta sk

A B C D I J M N O

P Q R

S

T U

V

E

Product: Trouser with one welted pocket Tas Wor Time k k Operation accumula time stati description ted (min on (min.) .) (WS) Right fly over 0.61 0.61 locking Left fly over 0.56 1.17 locking WS Right front 0.84 2.01 1 over locking Left front over 0.84 2.85 locking Right front 0.61 0.61 peans sewing Left front 0.62 1.23 peans sewing WS Pocket attach 0.86 2.09 2 on front Pocket body 0.91 3.00 seam stitching Pocket top 1.27 1.27 side edge sewing Pocket bag 0.58 1.85 interlocking Right fly zip 0.52 2.37 WS attach 3 Right front 0.6 2.97 right fly zip assembly attach Left fly attach 0.77 0.77 with left front edge stitch Left front 0.62 1.39 stitching Left front 0.59 1.98 WS attach with 4 right front at zip Front rise 1.01 2.99 sewing stitching Right back 1.36 1.36 over locking WS

76

F H K L W

X

Y

Z

A1

B1 G E1 F1 G1

H1 I1

J1

K1

C1

Left back over locking Back leap operation Right back peans sewing Left back peans sewing Back pocket facing over locking Back pocket facing attach with pocket bag Back pocket bag facing assembly attach Back pocket mouth cut stitch leap attach Pocket bag closing interlock Back rise sewing Belt loop operation Label attach sewing Loop attach on waist Waist band position marking Left and Right waist attach Inside and outside waist top centre attach Waist band attach with waist Waist edge sewing (clean finish) Side seam

1.36

2.72

0.72

0.72

1.12

1.84

1.12

2.96

0.65

0.65

0.62

1.27

5

WS 6

WS 7 1.73

3.00

2.01

2.01

0.42

2.43

0.57

3.00

0.61

0.61

0.71

1.32

1.59

2.91

0.88

0.88

0.93

1.81

0.97

2.78

1.12

1.12

1.85

2.97

WS 11

1.3

1.3

WS

WS 8

WS 9

WS 10


D1 L1 M1 N1 O1

P1 Q1 R1 S1

sewing Inseam sewing Bottom over locking Bar tacking Position mark for button hole Button hole marking on waist Position mark for button Button sewing Trimming End line inspection

12 0.88 0.78

2.18 2.96

1.83 0.51

1.83 2.34

0.47

2.81

0.48

0.48

0.44 1.56 0.5

0.92 2.48 2.98

where: Capacity in hours=(Shift hours per day)  (Total number of operators)  (1labour  420 min.)    97.61% Production capacity   41min.per trouser   9.99 10 trouser per oprator per day This means, 10 trouser per day per operator  14

WS 13

operators=140 trousers per day Table 7: Task description at each work station on the line

WS 14

Using cell layout, it is required fourteen operators, six operators less than the previous, and has a cycle time of 3min. The balance between workstations is illustrated in the Fig. 3. Workstations now have operation times balanced accordingly with the determined takt time, with some workstations slightly above or below. With this cell layout, it is now required fewer employees working as a team and less in-process inventory and handling.

Tas k

A

B

C

D

E

F

G Figure 3: Balance between work stations on the cell F. Productivity Measurement after Line Balancing The changing from traditional layout to balanced layout model, there are considerable improvements in this study. Standard working hours of the company is 420 min. Total labor per production line is 14 after balancing the line.  Capacityin hour  60   Productioncapacity   Product SAM  (15)  Line efficiency

H I

J

K

L

77

Product: Trouser with one welted pocket Operatio Task Time Work n time accumulate statio descriptio (min. d (min.) n n ) Right fly 0.61 0.61 over locking Left fly 0.56 1.17 over locking WS 1 Right 0.84 2.01 front over locking Left front 0.84 2.85 over locking Right back 1.36 1.36 over locking WS 2 Left back 1.36 2.72 over locking Belt loop 0.61 0.61 operation WS 3 Back leap 0.72 1.33 operation Right 0.61 0.61 front peans sewing WS 4 Left front 0.62 1.23 peans sewing Right back 1.12 1.12 peans WS 5 sewing Left back 1.12 2.24


M

N

O

P

Q R

S

T U

V

W

X

Y

peans sewing Pocket attach on front Pocket body seam stitching Pocket top side edge sewing Pocket bag interlockin g Right fly zip attach Right front right fly zip assembly attach Left fly attach with left front edge stitch Left front stitching Left front attach with right front at zip Front rise sewing stitching Back pocket facing over locking Back pocket facing attach with pocket bag Back pocket bag

78

0.86

0.86 Z

0.91

1.77

1.27

1.27

0.58

1.85

WS 6

A1

WS 7

B1 C1

0.52

0.52 D1

0.6

1.12 WS 8

E1

F1 0.77

0.77 G1 WS 9

0.62

1.39 H1

0.59

0.59

1.01

1.60

0.65

0.65

0.62

1.27

WS 10

I1

J1

K1 WS 11

L1 1.73

1.73 WS

M1

facing assembly attach Back pocket mouth cut stitch leap attach Pocket bag closing interlock Back rise sewing Side seam sewing Inseam sewing Label attach sewing Loop attach on waist Waist band position marking Left and Right waist attach Inside and outside waist top centre attach Waist band attach with waist Waist edge sewing (clean finish) Bottom over locking Bar

12

2.01

3.74

0.42

0.42 WS 13

0.57

0.99

1.3

1.3

0.88

2.18

0.71

0.71

1.59

2.30

0.88

0.88

0.93

1.81

0.97

0.97

WS 14

WS 15

WS 16

WS 17

1.12

2.09

1.85

1.85

0.78

2.63

1.83

4.46

WS 18


N1

O1

P1

Q1 R1 S1

tacking Position mark for button hole Button hole marking on waist Position mark for button Button sewing Trimming End line inspection

0.51

0.51 WS 19

0.47

0.98

0.48

1.46

0.44

1.9

1.56 0.5

1.56 2.06

Table 8: Overall improvements for single production line balancing

WS 20

Operatorproductivity 

(16)

Totallnumber of output per day per line Number of operators 140 trouser per day Total labour productivity  14 operators  10 trouser per operator per day Operator efficiency 

10 trouser per day  41min.  100 420min.

 97.61%

Machine productivity  Total number of out put per day per line Number of machine used

(17)

Value of direct labour cost (DLC) 630ETB per month 14labours 24days per month

Direct labour cost per day Totalout put per day

Value of direct labourcost per unit (LC) 

367 ETB per day  2.65ETB per trouser 1400trousers per day

79

Before

After

Improvement (%)

20

14

30 reduction

120

140

16.66 increment

Total labour sewing productivity

6 trouser per day

10 trouser per day

66.66 increment

59%

97.61%

65.44 increment

10 trouser per day

66.66 increment

367.5 birr per day

30 reduction

2.65 birr per trouser

39.42 reduction

Sewing operator efficiency Sewing machine productivity

6 trouser per day 525 birr per day 4.375 birr per trouser

III. CONCLUSION

 367.5ETB per day per line Value of direct labourcost per unit (LC)



Productivity Variables Number of operators Production

Sewing direct labour cost (DLC) per day per line Sewing direct labour cost (DLC) per unit

140 trouser per day Machine productivity  14 machines  10per machine per day



Comparing productivity improvement in percent before and after line balancing using work measurement analysis is presented in Table 8. The line balancing is made as per manual calculation and assuming every operator knows at least three to four operations of respective workstations. Thus while selecting operators for the several operations it is necessary to check whether the operator is suitable for that work or not because the workstation will perform best if all the group members have the same skill level.

(18)

In this study the lean manufacturing tools techniques were studied used in case company (NGSC). Accordingly, the finding of the problem in this study is summarized as follows:  Analyzing the existing layout and motion study, it can be concluded that there is too much movement and transportation of work pieces between workstations and that the cycle time of




 

 



  

the line is not balanced with the takt time and causing large WIP between processes. There is high motion or movement of workers from one workstation to another due to poor line balancing and machine layout and resulting in WIP in the production line. High inventory WIP in cutting section as well in sewing section due to unbalanced work assignment. The company has not set standard time for the selected product which has a high demand; instead they are using based on guess work and experiences. Poor line balancing is there due to lack of timestudy or work measurement techniques. There is bottlenecks process especially on workstation of waist edge sewing (clean finish), bottom over locking and bar tacking due to unequal work assignment among operators. This is resulting in the highest cycle time by 4.46min. and other work stations are waiting for it to finish its task. Unnecessary quality checking points at each phase of the first three main operations namely; front part preparation, back part preparation and waist-and-loop preparation part which tend to increase the cycle time. Production cycle time is not balanced according to takt time, so that customers are complaining due to late delivery of finished garment. Poor flexibility of style-change over due to limited skill of operators. Low work satisfaction due to low motivation or incentive of workers and poor working condition.

IV. RECOMMENDATIONS Based on the review of literature and the findings of this study, the following recommendations are forwarded to NGSC.  In this study, only one product ‘trouser with one welted pocket’ were standardized due to time limitation. So, the company could do work standardization for other products in the same manner.  In order to continuously reduce or eliminate waste, management of the company need to apply different lean tools and techniques, accordingly while giving adequate training to their employees.

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 The lean production system and its principle should be observed and whenever performance improvement is intended.  Work-study departments must have to be introduced (opened) in NGSC so as to standardize their manufacturing process.  All new operations must have a written method or procedures and the operator must be instructed in that method.  Investigations must be made into the amount of repairs found in finishing department.  Majority of supervisors and almost all operators have no professional education in garment manufacturing; their knowledge is based on their work experience. Therefore, it is extremely important that the company’s management should teach them a more professional approach to supervision in controlling production for supervisors and some training for operators to upgrade their skill.  Production studies must be completed wherever poor performance is observed.  The production capacity per production line is mainly affected by the number of operator and SAM of the product. It is better the management to thinking that the operators are the heart of the system and should give an attention to the absenteeism of the operator and up-dating the SAM of products with the reference to the international bench mark. REFERENCES [1] Rahel Sorri, “Performance and improvement of Ethiopia garment industry,” Master’s Thesis: Addis Ababa University, Addis Ababa, Ethiopia, (Unpublished), 2011. [2] R. Bheda, A.S. Narag, and M.L. Singla, “Apparel manufacturing a strategy for productivity improvement,” Journal of Fashion Marketing and Management, vol. 7, no.1, pp. 12-22, 2003. [3] M.B. Lucy Daly and N. Towers, “Lean or Agile: A solution for supply chain management in the textile and clothing industry,” International Journal of Operations & Production Management, vol. 24, no. 2, pp. 151-170, 2004. [4] M.I. Shahidul and S.T. Syed Shazali, “Dynamics of manufacturing Productivity: Lesson learnt from labor intensive industries,” Journal of Manufacturing Technology


Management, vol. 22, no. 5, pp. 664-678, 2011. [5] T. Shahram and M. Cristian, “The impact of lean operations on the Chinese manufacturing performance,” Journal of Manufacturing Technology Management, vol. 22, no. 2, pp. 223-240, 2011. [6] A.G. Kelly, “Adapting lean manufacturing principles to the textile industry,” Master’s Thesis: Raleigh, North Carolina (Unpublished), 2007. [7] J. Drew, M. Blair, and R. Stefan, Journey to lean: Making operational change stick. Palgrave Macmillan, Gordonsville, VA, USA, pp. 5-25, 2004. [8] L. Wilson, How to implement lean manufacturing. McGraw-Hill Professional Publishing, New York, pp. 29-214, 2009. [9] A.M. Nash, S.R. Poling, and S. Ward, Using lean for faster six sigma results a synchronized approach. Productivity Press, New York, USA, p. 17, 2006. [10] Vorne Industries Inc. (2010). Itasca, IL USA, 877-767-LEAN, http://www.leanproduction.com [11] T.T. Burton S.M. Boeder, Lean extended enterprise: Moving beyond the four walls to value stream excellence. Boca Raton, FL, USA: J. Ross Publishing Inc. p. 122, 2003. [12] National technical and vocational education and training strategy. Ministry of Education, Ethiopia, 2008. [13] W.J. Stevenson, Production and operational management. 10th edition McGraw-Hill, International Companies Inc., New York, 2009. [14] S.A. Kumar, Production and operations management. New Age International, Daryaganj, Delhi, India, 2008. Feven Demeke, “Operational management standard of articles,” Adama Garment Industry, Adama (Unpublished), 2010

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Additive Manufacturing of Tooling Element with Conformal Cooling Channel Fisseha Legesse

K.P. Karunakaran

Mechanical Engineering Department Indian Institute of Technology Bombay Mumbai, India E-mail: [email protected]

Mechanical Engineering Department Indian Institute of Technology Bombay Mumbai, India E-mail: [email protected]

Abstract: Additive Manufacturing (AM) gives engineers a new freedom to build parts that have thus far proved difficult to manufacture using conventional machining. However, the surface finish and accuracy of AM parts are lower than those of conventionally machined parts. A process combination of additive and subtractive techniques is currently being developed in order to overcome this problem. A novel hybrid approach uses gas metal arc welding (GMAW) as an additive and CNC milling as a subtractive technique, thereby exploiting the advantages of both processes. This process is called Hybrid Layered Manufacturing (HLM). Compared to other deposition processes, GMAW is the most economic way of depositing metals. In this paper, the initial results of the process development and the characterization of the parts fabricated by this process are reported.

The energy sources for weld deposition may be a laser, an electron beam or an electric arc. The laser has been the most popular due to its ‘precision’. However, it has very poor energy efficiency in this application (2 – 5%) [10]. The combination of welding as an additive and milling as a subtractive process can provide distinctive advantages over conventional machining. Firstly, if a large volume must be removed, the fabrication of a near-net-shape part using the additive method and the subsequent surface finishing can offer a competitive approach in terms of fabrication time. In addition, if the material is difficult to machine, the additive fabrication of a near net shape part offers an economic way to machine it because of less tool wear [11]. Secondly, features that are either impossible or difficult to machine can be manufactured using the hybrid approach. Such features include deep and narrow slots as well as arbitrary internal structures such as conformal cooling channels (CCC) [12]. Thirdly, the combined process permits fabricating accurate parts with various materials, depending on the functional requirements [13], is called Functionally Gradient Materials (FGM). Figure 1 shows some metallic objects realized using different AM processes. As is obvious from these figures, all of them produce only near-net shapes and rough surfaces. These cannot be used in many applications unless they undergo postmachining. Therefore, there are no major differences among laser, electron beam and arc welding in terms of the finish and material integrity. Arc welding has the added advantages of higher deposition rates, lower costs and safer operations. The deposition rate of laser or electron beam is of the order of 2–10 g/min, whereas 50– 130 g/min have been reported in arc-deposition and it can reach as much as 800 g/min with proper heat management [14]. Thus, laser or electron beam may be overkill for the tooling and

Keywords: Additive manufacturing, Hybrid manufacturing, Conformal cooling channel, GMAW.

layered

I. INTRODUCTION Additive Manufacturing (AM) process draws its appeal from its ability to allow parts to be built directly from 3D CAD descriptions, without any tooling. With an increasing demand for metallic prototypes and tools [1], several direct AM techniques such as 3D Welding [2], Selective Laser Sintering (SLS) [3], Laser-Engineered Net Shaping (LENS) [4], Directed Light Fabrication (DLF) [5], Direct Metal Deposition (DMD) [6], Controlled Metal Buildup (CMB) [7], Shape Deposition Manufacturing (SDM) [8], and more recently, Precision Metal Deposition (PDM) [9] have been developed. The feature common to all of these approaches is that metals, either in the form of powder or wire, are melted directly with an arc or laser beam. Due to the complete melting, however, the accuracy and surface quality of the parts are generally lower than those of machined parts.

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component applications barring a few exceptions such as the realization of miniature objects.

Figure 1: Comparison of surface quality of rapid manufacturing processes using laser, electron beam and arc sources As a new hybrid AM process, Gas Metal Arc Welding (GMAW) is combined with conventional 3 axis milling to directly fabricate metallic objects [15-16]. This process is under development in IIT Bombay and it is called Hybrid Layered Manufacturing (HLM). This report focuses on the identification of the basic process characteristics and results of the fabricated parts in order to see whether the process can be successfully applied to manufacture functional parts, especially tooling’s & die parts. II. PROBLEM DEFINITION There are many methods of metal deposition, but none of them are able to produce tools/components with satisfactory surface finish. Even methods which employ lasers, electron beam welding, which have the best accuracy of all, still requires finish machining; thus making Gas Metal Arc Welding based deposition the most attractive process in terms of overall deposition rate and cost. A GMAW welding unit for metal deposition is intrinsically integrated onto a CNC machine and metal deposition for creation of near-net shape can be archived using the CNC controller and axes itself. This idea can be well accepted in the future if it is applied for tool steel and aerospace materials. Tool steel materials are very expensive and difficult to machine. The costs of these materials are determined by their weight. So if it is possible to

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produce a near net shape the cost can be reduced dramatically. The techno economic analysis is done by K. P. Karunakaran et.al [11] shows cost reduction of HLM process for aluminum alloy material. This analysis has not been proved using materials like mild steel, H13 tool steel and aerospace materials. The other problems encountered in the area of plastic injection and die casting moulding is frequent variation of temperature. This variation in temperature encounters frequent thermal fracture of the die parts. For keeping constant temperature of the tool cooling must be done. The conventional cooling channels are not effective enough compared to the tooling produced by AM with conformal cooling channel [17]. A tooling elements encountering frequent temperature variation must be produced with a conformal cooling channel (CCC). CCC is only possible in AM process. In this paper two cooling channels are compared based on ease of manufacturability and functionality of the tooling component. III. PROCESS PRINCIPLE EXPERIMENTAL SETUP

AND

In general, the AM process breaks down a 3D CAD model with complex geometry into a set of simpler shapes which are easier to manufacture. These are then fabricated layer by layer until the final part is built. However, the layered approach produces a stair-step effect that limits the accuracy of parts and exponentially increases the total build time for a large sized part. 1. Process Principle The principle of HLM process is based on layer by layer deposition of molten wire material using GMAW, which is the most economic way of depositing molten metal. The process data flow of HLM is depicted in fig. 2. The STL (3D model) file is sliced at a prescribed layer thickness in the Delcam Powershape, shown in fig. 3. However, its beads are of lower quality than laser welding beads with respect to accuracy and surface quality. Since a post-processing step, e.g. surface machining is required for most of the parts built by direct deposition approaches, the relatively low accuracy and surface quality of arc-welded beads is acceptable. GMAW also offers a distinct technical advantage with regard to the possibility of vertical wire feeding: the welding result is


independent from the change of the relative movement between the wire nozzle and the x–y table.

Figure 4: Tool path generated using DelCAM Powermill

Figure 2: Preparation of the process data for hybrid layered manufacturing

Figure 5: Process principle of hybrid layered manufacturing

Figure 3: Slicing of the STL file to the prescribed thickness, DelCAM powershape Following the slicing step, the 2D generated file is imported into the NC post-processing software, Delcam powermill, which generates a NC tool path for filling the inner area as well as the contour of each layer as per the parameters, as shown in fig. 4. For the layer filling technique, one-way and zigzag tool paths are offered.

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First, a layer is built by depositing single beads side by side with bead offset pitch, (P). Depending on the welding parameters such as welding torch speed (Vs) and welding current I. The bead thickness (h) can be varied from 1 to 5mm by varying the welding parameters. The distance between single beads P is an important process parameter determining the overlapping of beads and thereby the layer’s surface thickness. When deposited, the top surface of the layer is machined to a prescribed thickness for further deposition. 2. Experimental Setup The experimental setup for the HLM process is shown in fig. 6. The setup is based on a 3-axes milling machine with a welding gun that is vertically attached to the spindle housing. A simple retrofitting of common 3-axes milling machine is required in order to carry out the process [18], thus eliminating any need to buy special equipment such as linear axes.


IV. EXPERIMENTAL INVESTIGATION AND RESULT ANALYSIS HLM process involves several parameters. A series of experiments were required to refine the HLM process for better efficiency and sound product. A set of preliminary experiments was done to determine the distinct level process parameters. Basic experimental investigation in HLM contains three major steps. The first one is single bead modeling. In this experiment the weld bead width and height will be identified by varying different welding parameters. Thesecond step is multi bead modeling. It is aimed to find the optimum pitch distance (P) between two successive weld beads. The third step is multi-layer weld modeling. In this step the thickness of the layer and the removal of scalp surfaces will be determined. 1. Single bead modeling GMAW is the most popular process for joining in welding application. In HLM, GMAW is used for metal deposition. Some preliminary experiments on welding parameters in deposition were carried out using ER70S6, mild steel material. The nominal composition of ER70S6 is shown in the table 1.

Figure 6: The 3-axis hybrid layered manufacturing machine at IIT Bombay

Figure 7: Single bead weld deposition

Table 1: The nominal composition of ER70S6 Si(%) 0.81.15 Ch .15

C .06-.15

Ni 0.15

Mo .15

Cu .5

S .035

Fe Balance

Mn 1.141.85

P V Others .025 0.03 0.50

In pulsed synergic GMAW, the geometry of weld bead depends on welding torch speed and wire speed. Wire speed can be controlled by controlling the current flow as current and wire speed has a monotonous relationship in synergic control. A pattern shown in the fig. 7 was deposited on a substrate of 100mm x 50mm for various combinations of current and torch speeds. The data’s which are collected through a course of different experimentation are shown in the figs. 8 and 9.

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Figure 8: Feed rate vs weld bead width


The thickness of layer (t) can be denoted in terms of bead height (h), step over increment or pitch (p) and width (w). 𝑝 2

𝑡 = ℎ[1 − (𝑤) ]

(1)

And pitch can be expressed as, p=2/3 w (2)

Figure 9: Feed rate vs weld bead height 2. Multi Bead Modeling In HLM, any object is made by depositing layers by layer of material. So, the main objective here is to make a layer of metal by deposition. The step over increment or pitch between two beads plays very important role in converting the beads into layer. When the pitch is less than the bead width, two weld bead overlaps and thus by several beads one layer is formed. In the fig. 10, the welding is done in a zigzag manner. The pitches between the beads have been increased gradually from 1.5 till 2.5mm.

Figure 11: Layer thickness with step over increment 3. Multi-layer Deposition The layer thickness will be determined based on the pitch distance of the weld bead. After each layer of deposition the irregular surface and oxidized surface will be machined and Z increment will be maintained after each layer of deposition. So, the common welding defects are also visible in metal deposition. These are; non homogeneous structures, porosity and anisotropic properties. High porosity, non-uniform hardness and higher surface residual stresses are main problems in HLM. V. CASE STUDY

Figure 10: Multi weld deposition for different pitch The thickness of the layer also depends on the step over increment of two consecutive beads. From fig. 11, it is clearly seen that the step over increment decides the initial layer thickness of the layer. The overlapped material fill the gap between two beads partially or fully [19].

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The 3D CAD model of a part called diffuser was received from Godrej tool room industry (India) for further studies and to manufacture using HLM process. A diffuser is used for die casting process as a part of a big mold component. In moulding dies cooling is the major component of the manufacturing cycle. The cooling channel in the diffuser was suggested shown in the fig. 12a. The design of the cooling channel was doom shaped and its name called a baffle. The dome shape of the part is not feasible to manufacture it on a 3 axis weld deposition. Then new design is suggested to be a bubbler type cooling channel as shown in the fig. 12b and the dome shape is changed to flat surface. These changes are made based on the manufacturability point of view.


selected bubbler cooling channel is efficient for cooling compared to the baffle cooling system.

a) Old Design cooling b) New Design cooling channel channel Figure 12 Sectional views of baffle and bubbler cooling channels consecutively Further analysis has been done on the respective cooling channel. The Autodesk Moldflow Insight gives a comparison of baffle and bubbler cooling channels. In this analysis the basic parameters like coolant type and Reynolds number are taken the same. The initial cooling conditions such as the filling time and molding window are shown in fig. 13 a and b.

a) cooling effect using baffle channel

b)Cooling effect using Bubbler channel Figure 14: Moldflow analyses for baffle and bubbler type cooling channel a) Fill time analysis

b) Molding window Figure 13: Moldflow insight basic analysis The moldflow result shows the baffle type cooling channel gave an incomplete cooling and the time taken is longer, as shown in the fig. 14a. The bubbler type cooling channel gives a complete cooling for the given geometry of a part and takes shorter time compared to baffle cooling channel, as shown in the fig. 14b. This result shows that the

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IV. MANUFACTURING RESULTS

AND

TEST

1. Manufacturing The 3D STL model has been done including the bubbler type cooling channel with appropriate dimension. Slicing and NC program generation is done by DelCAM software. Using the appropriate welding parameters deposition has been done. After each deposition of a layer CNC machining is followed to avoid oxidation and scalp. After each deposition and machining more than 1mm material is left for Z increment. This addition of material gives us a near net shape. Finally to avoid a staircase effect machining has done to get a final surface finish. Partial process step is shown in the fig. 15.


a) 3D CAD model

b) Internal 3D sectional view

e) Internal finished feature

f) Near net shape

g) After machining with the same setup

a) Without water line b) With water line Figure 16: Thermal image of a diffuser B) Pressure Testing: It was conducted by blocking the coolant out-late and allowing 4 bar pressurized water in the in-late direction. Then the part was totally immersed in a water basin and checked for any leakage. The test shows that no leakage was found. C) Checking for the cooling channel: This test is done on the 3 axis CNC machine cooling system. Figure 17a shows the coolant inlet is small diameter hole and goes out in the bigger diameter of the circuit. In the second set up the coolant inlet was from the bigger diameter side and exit in smaller direction, fig. 17b. It is shown clearly the flow rate is high because of the smaller exit diameter. This shows the cooling flow cycle is complete inside the part.

h) Final part

Figure 15: Steps of manufacturing a diffuser 2. Test Results A) Temperature Testing: The diffuser has passed through a temperature of 180oC for one hour. In this time a thermal image was taken to see if there is any defect on cooling line and compared the temperature difference while coolant passed through the line. The result shows that when there was no coolant the maximum temperature measured was 150oC on the surface using thermal camera. When the coolant is passed through the cooling channel the part temperature drops to 99oC. This shows a significant amount of heat was removed with newly designed cooling channel. The result of thermal image is shown in the fig. 16.

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a) Exit from bigger b) Exit from smaller diameter side diameter side Figure 17: Checking for the cooling channel complete circulation IIV. CONCLUSIONS In HLM process, high speed for maximum deposition rate and low energy for minimum consumption of energy, are the main objectives. To utilize the maximum material usage in weld deposition the optimum parameters like torch speed, current and heat input of GMAW should be well understood. Weld deposition has been done for different combinations of current and torch speed with different pitch. So, taking the


combination of optimum current and torch speed as a parameter the weld deposition of layer by layer was completed. Based on the results obtained from mild steel (ER70S6) weld deposition, tool steel (H13) material deposition is under development. REFERENCES 1. A. Rosochowski, A. Matuszak. Rapid tooling: the state of the art. Journal of Materials Processing Technology, 106 (2000) 191–198. 2. P.M. Dickens, M. Pridham, R. Cobb, I. Gibson, G. Dixon, 3D welding, in: Proceedings of the First European Conference on Rapid Prototyping, University of Nottingham, England, pp. 81–93. 3. M. Khaing, J. Fuh, L. Lu. Direct metal laser sintering for rapid tooling: processing and characterization of EOS parts. Journal of Materials Processing Technology 113 (2001) 269–272. 4. M. Griffith et al., Freeform fabrication of metallic components using laser engineered net shaping (LENS), in: Proceedings of the SolidFreeform Fabrication Symposium, University of Texas at Austin, 1996, pp. 125– 131. 5. G. Lewis, J. Milewski, D. Thoma. Properties of near-net shape metallic components made by the direct light fabrication process, in: Proceedings of the Solid Freeform Fabrication Symposium, University of Texas at Austin, 1997, pp. 513–520. 6. J. Mazumder, J. Choi, K. Nagarathnam, J. Koch, D. Hetzner. The direct metal deposition of H13 tool steel for 3D components. Journals of the Minerals, Metals and Materials 1997: 49 (5) 55–60. 7. C. Freyer, F. Klocke, Fast manufacture of high strength tools from steel using CMB, in: Proceedings of SME Conference Rapid Prototyping and Manufacturing, Cincinnati (OH), 14–17 May, 2001. 8. J.R. Fessler, R. Merz, A. Nickel, F. Prinz, Laser deposition of metals for shape deposition manufacturing, in: Proceedings of the Solid Freeform Fabrication Symposium, University of Texas at Austin, 1996, pp. 117–124. 9. J. Rabinovich, Laser precision metal deposition PMD, in: Proceedings of the SME

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Conference Rapid Prototyping and Manufacturing, Cincinnati (OH), 30 April–2 May, 2002. Unocic RR, DuPont JN. Process efficiency measurements in the laser engineered net shaping process. Metallurgical and Materials Transactions B 2004; 35B (1):143–52. Sreenathbabu A, Karunakaran KP, Amarnath C. “Statistical process design for hybrid adaptive layer manufacturing.” Rapid Prototyping Journal 2005; 11(4): 235–48. X. Xu, E. Sachs, S. Allen, M. Cima, Designing conformal cooling channels for tooling, in: Proceedings of the Solid Freeform Fabrication Symposium, University of Texas at Austin, 1998, pp. 131–146. M. Griffith, L. Harwell, J. Romero, E. Schlienger, C. Atwood, J. Smugeresky, Multimaterial processing by LENS, in: Solid Freeform Fabrication Proceedings, University of Texas at Austin, 1997, pp. 387–394. Sreenathbabu A, Karunakaran KP, Amarnath C. Statistical process design for hybrid adaptive layer manufacturing. Rapid Prototyping Journal 2005; 11(4): 235–48. Karunakaran KP, Akula Sreenathbabu, Pushpa Vishal. Hybrid layered manufacturing: direct rapid metal tool-making process. I MECH E Journal of Engineering Manufacture 2004;218(B12):1657–65. Karunakaran KP, Suryakumar S, Bernard A. Hybrid rapid manufacturing of metallic objects. In: 14th European conference on rapid prototyping; 2009. Xiaorongu Xu, Emanuel Sachs, and Samuel Allen. The Design of Conformal Cooling Channels in Injection Molding Tooling. Polymer Engineering and Science, July 2001, Vol.41, No. 7. K. P. Karunakaran & S. Suryakumar & Vishal Pushpa & Sreenathbabu Akula. Retrofitment of a CNC machine for hybrid layered manufacturing. Int J Adv Manuf Technol (2009) 45:690–703 DOI 10.1007/s00170-0092002-2. S. Suryakumar, K.P. Karunakaran, Alain Bernard, U. Chandrasekhar, N. Raghavender, Deepak Sharma; ‘Weld bead modeling and process optimization in Hybrid Layered Manufacturing’; Computer-Aided Design 43 (2011) 331–344.


Integrated Virtual/Augmented Reality and Haptics Feedback Technology for Defence and Industrial Applications Hailu Gebretsadik

A.K. Das

College of Engineering, Defense University Bishoftu, Ethiopia E-mail: [email protected]

Indian Institute of Technology, Guwahati Guwahati-781039, Assam, India E-mail: [email protected]

Abstract: Integration of Virtual Reality with haptic feedback technology has unlimited application in different discipline. However, it is widely used in product evaluation, medical training, military application, education and entertainment with the aim of making human being life safe, comfortable and reducing industrial product design and development process. This paper mainly focuses on the integration of haptic feedback devices with Virtual Reality/Augmented Reality and its applications towards Health, Defence, and Industrial sector. An attempt to show its application in evaluation of virtual prototype of plastics product and mobile handset as a case study is discussed which is extendable to health and Defence application too. Keywords: Defence, haptics, Virtual Reality, Product Design, industry.

manipulate the virtual object. In this work phantom Omni has been used to interact and manipulate the virtual models such as mobile phone, plastic water bottle, oil bottle, assembled bolt and nut. During this process the mechanical properties assigned were similar to the actual object’s properties to be felt by the hand of the user. Tasks were to evaluate and validate virtual object created using 3D CAD software. This process is also equally important for military, medical, education and so on.

I. INTRODUCTION In product design and development, designers undertake various activities related to perception of the conceptual design, analyzing requirements, proposing and evaluating solutions and making decisions. To complete these tasks, it is important to take into account different tools and prior knowledge and experience of previously proven designs. During the product design and development phases, testing, modifying and retesting the product based on the customer/users requirement is critical issue. This critical issue may incur cost on the product’s overall price or investment in the particular project due to repeated prototyping. Therefore it is important to look for solutions that reduce this kind of cost. In this paper the following solutions has been suggested based on experimented/tested case studies using different software libraries such as H3DAPI (Haptic Device Application Interface) which uses OpenGL for graphics and the open source HAPI (Haptics Application Interface) that help simulating interaction between the hardware (represented by proxy) to the virtual environment (virtual solid object with its property). The integration of the haptic/force feedback technology with the virtual environment can be a solution, since haptic/force feedback sensors allows users to interact and

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II. HAPTICS AND ITS APPLICATION IN DIFFERENT DISCIPLINE Haptic is a word that has taken from Greek word “haptikos” meaning touch that refers to the ability to perceive the environment through the sense of touch, and consist of the acquisition of both tactile and kinesthetic information and it is also called tactile-kinesthetic. Haptic senses links to the brain’s sensing position and movement of the body by means of sensory nerves within the muscles and joints as it is shown in Figs. 1 and 2. Tactile perception is felt through pressure against or motion across the skin due to tactile receptors located over the entire body while kinesthetic refers to the information acquired by the sensors in the joints i.e. body movements and muscle feelings [1], [2].

Figure 1: Human motor system [3]


III. VIRTUAL SYSTEMS

Figure 2: Touch sense [3] Haptic Classification Haptics usually classified in to three areas [2], [4] and defined as follows: Human haptics: the study of human being experience touches perception and manipulation (human motor, touch and cognition, human sensory system and haptic anatomy). Machine haptics: concerned with the design, construction, and use of robot arms and hands to replace or augmented human touch and feel their environment (haptic control, robotic inheritance, sensing technology, and actuation technology). Computer haptics: concerned with computer mediated i.e. algorithms and software associated with generating and rendering the touch and feel of virtual object (collision detection, haptic interface modeling, and physics based haptic simulation and collision response). Through haptic interaction system, operator can feel the force related properties from a virtual world including gravity force, inertia force, frictional force, contact force and reaction force. Mainly, this research falls in this category.

Figure 3: Classification of haptics and its integration with VR/AR

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REALITY

AND

ALLIED

Virtual Reality has become a state of art technology in the fields of industrial design, textile, Architectural Design, military, automotive and aerospace design, medical, Education and Entertainment [5-11], for different purposes such as virtual object realization, training, usability evaluation, simulate, analyze and optimize manufacturing processes and identifying issues related to the assembly sequences. According to [12], [13] virtual reality is categorized in three different kinds, first is desktop virtual reality, desktop VR systems uses 3D computer graphics as immersive Virtual Reality systems and which is ease to use, install and most common and least expensive form of virtual reality; second, a semi-immersive virtual reality system that attempts to give the users a feeling of being at least slightly immersed by a virtual environment this means users still can see himself and the other user. Images are displayed based on the main user’s head position and orientation provided by a head tracker etc. and the third form of virtual reality is usually referred to as being fully immersed, immersive virtual environment typically in which a user wears a head mounted display that attempts to isolate the user from the real world in order to increase the realism of the simulation. A. The PHANTOM Omni® haptic device There are different types of force feedback device from different companies such as phantom premium, delta, freedom, Omega, Novient Falcon etc. but in this research it has been used Phantom Omni of 3D system corporation. The PHANTOM Omni depicted in Fig.4, is a force feedback haptic devices from Geomagic (formerly SensAble Technologies) can accurately measure the 3D spatial position (along the x, y and z axes) and the orientation (pitch, roll and yaw) of the handheld stylus.


deforms when poked, and the perceiver’s hand and stylus simultaneously displaced. In this experiment it has been also performed to perceive the curvature/bent of the solid object through the stylus by moving on the surface and inside the model.

Figure 4: PHANTOM Omni force feedback device rotation and applied force directions The devices use motors to create the forces that push back on the user’s hand to simulate touch and interaction with virtual objects. In addition to this the position of user’s fingertip or hand monitors through the optical encoder mounted on the motors. The direction of applied force on the virtual object by the user, feedback force in to the user, rotation direction on each links and joints also shown on Fig.4. Phantom Omni force feedback device has been put to practical use for wide range of purpose to list few of them, simulation for medical training purpose [8], product evaluation [14], [15], [16], [17] as a virtual robot arm to interact with virtual prototypes in a virtual assembly environment by providing kinematic and dynamic models, and defining the properties [18]. B. VR/AR integration with the haptic device During haptic interaction with the computer controlled environment (virtual environment), haptic perception relies on sensory signals arising from computer controlled mechanical signals produced by haptic interface such as contact force, torques, movement of objects, mass or weight of objects, surface texture, stiffness of materials, geometry of objects, etc. To bring/describe the behavior and characteristics of mechanical signals it was employed the laws of physics (dynamics, kinematics). The position of the proxy/stylus representation is sensed and used to programmable forces. Consider the case of a perceiver that uses a stylus to poke into a flexible rubber surface or given virtual object. The physics of the contact between proxy/stylus and surface involves several mechanical variables, among them contact forces and proxy/hand displacements. When poking into the surface, the perceiver experiences the contact forces through the stylus/proxy. The surface

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Figure 5: Experiment conducted to evaluate the stiffness of the water bottle and jar

Figure 6: Experiment conducted to evaluate the form and surface texture of mobile and pen holder IV. APPLICATION OF VR AND AR IN DESIGN EDUCATION AND RELATED FIELDS VR/AR provides the natural and interactive ways to express ideas and overcome the technical gap in the iterative design process by upgrading from traditional computer aided design process to mixed reality aided design space [19]. The uses of VR applications in various design education related fields have improved the productivity of teaching and training by allowing engineers to apply theoretical knowledge to real industrial problems with real time experience [7], [20]. Nowadays applications of AR are widely used. Unlike other computing technologies AR supplements (combines) the real world with virtual objects (i.e. computer-generated) [21]. The combination of AR technology with the educational content creates new type of automated applications which acts to enhance the effectiveness and attractiveness of teaching and learning process for students in real life scenarios. Actually, AR is a new medium which is combining aspects from ubiquitous


computing, tangible computing and social computing. This medium offers unique affordances, combining physical and virtual worlds, with continuous and implicit user control point of view and interactivity [22]. Using AR systems learners interact with the 3D information, objects and events in a natural way. Billinghurst [23] used AR technology in education for support of seamless interaction between real and virtual environments and suggested educator to work with researcher in exploring how this can be applied in school environment.

Figure 7: Augmented reality in education [24] Another interesting application of AR technology is to develop augmented reality textbooks [22], in which books are printed normally but when a webcam is pointed over the book, it brings visualizations and designed interactions on the screen of the device. This is possible by installing special software on a computer or mobile apps on a portable device. This technology allows any existing book to be developed into an augmented reality edition after publication. Through the use of AR in printed book pages, textbooks will became dynamic sources of information. In this way, people can have a rich interactive experience with comparatively less computer knowledge than computer experts. V. APPLICATION OF VR-HAPTIC MILITARY APPLICATION

IN

VR-haptic system is already widely used for military application, for training purpose such as how to fight with enemy in a complicated environment by representing the equipment used during war time and actual environment with

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virtual environment (representation of digital sand table with necessary human’s models, vehicles and fighter aircraft for war game). Injuries may happen during exercise because of the ammunition or due to some other circumstance related to the actual use of machine gun. In this scenarios haptics may play an important role in reducing hazards/injuries and cost of ammunition. This means integration of this haptics with the virtual environment may increase the realism as the actual war field phenomena. In addition to this, the VR-Haptics integration is important for aeronautical, vehicle assembly/disassembly or maintenance training for a number of trainers in a limited space and less cost without deploying physical aircraft or vehicle in a spot.

Figure 8: Digital landscape [25] VI. APPLICATION HEALTH SECTOR

OF

VR-HAPTIC

IN

As it is mentioned at the introduction of this chapter virtual Reality used in the health sector too, for medical doctors training such as surgeons can plan and rehearse by operating on a virtual patient model with life like tissues reaction as it is shown in Fig. 9. Haptic scissors used to simulate the feeling of cutting tissue. It is also used by students as surgical tools, root canal surgery, and dental inlay to get experience through practice. This technology gives the opportunity to avoid use of volunteer patient or dead bodies for training. Furthermore, the system may give the opportunity for appropriate assessment of medical students’ skills during their study; however, this may require increasing the level of realism to achieve.


Figure 9: Virtual reality simulation using Phantom Omni [26]

Figure 10: VR application for dental inlays [27]

Figure 11: Root canal surgery [27] VII. DISCUSSIONS AND CONCLUSIONS Virtual reality and haptic feedback device technologies and their applications in various fields has been briefly discussed in this paper. It also highlight the benefits of integration of haptic feedback device with a virtual reality systems as well as how these integrated systems would help designers to perform realistically in complex computer aided product design process. Furthermore, the paper discusses virtual reality and augmented reality application in military, education and health sectors. VII. CONCLUSIONS

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The process described in this paper may be a perfect solution to evaluate the usability of consumer product’s virtual prototype by integrating it with haptic devices which allow interacting with model and sensing the object with more realism and interactivity prior to physical prototype. This assumption was proved through experimentation, data collection and analysis. Therefore, proper use of Phantom Omni or similar haptic devices may help in reducing the overall cost of product design and development by avoiding repeated physical prototyping. In the case of defence, it has been used for training and instruction purpose that reduce the ammunition and increase the number of trainees in a simulated environment. Its application in health sector is also proven by other researchers as an important tool as illustrated through Figs. 9-11. Therefore, it is feasible to design, manufacture and utilize such type of system for defence, health and other industrial applications. REFERENCES [1] Salisbury Kenneth, Francois Conti, and Federico Barbagli, 2004, Haptic rendering: introductory concepts, Computer Graphics and Applications, IEEE 24.2 : 24-32. [2] El Saddik, A., Orozco, M., Eid, M., & Cha, J. (2011). Haptics: General Principles. In Haptics Technologies (pp. 1-20). Springer Berlin Heidelberg. [3] http://www.hapticfeedback.com/2012/04/sense-of-touch-inhaptic-feedback.html, (accessed in March 10, 2015). [4] Srinivasan, M. A. (1995). What is haptics?. Laboratory for Human and Machine Haptics: The Touch Lab, Massachusetts Institute of Technology. [5] Lele, A., 2013, Virtual reality and its military utility. Journal of Ambient Intelligence and Humanized Computing, 4(1), 17-26. [6] Parsons, T. D., & Trost, Z., 2014, Virtual Reality Graded Exposure Therapy as Treatment for Pain-Related Fear and Disability in Chronic Pain. In Virtual, Augmented Reality and Serious Games for Healthcare 1 (pp. 523-546). Springer Berlin Heidelberg. [7] Abulrub A. G., Alex N. Attridge, and Mark A. Williams, 2011, Virtual reality in engineering education: The future of creative learning,


Global Engineering Education Conference, pp. 751–757. [8] Gosselin, F., Bouchigny, S., Mégard, C., Taha, F., Delcampe, P., & d’Hauthuille, C. (2013) Haptic systems for training sensorimotor skills: a use case in surgery. Robotics and Autonomous Systems, 61(4), 380-389. [9] Onyesolu Moses Okechukwu, and Felista Udoka Eze, 2011, Understanding Virtual Reality Technology: Advances and Applications, Advances in Computer Science and Engineering, Rijeka, Croatia: InTech Open Access Publishers. [10] Onyesolu Moses Okechukwu, Ignatius Ezeani, and Obikwelu Raphael Okonkwo, 2012, A Survey of Some Virtual Reality Tools and Resources, Virtual Reality and Environments, CS Lányi (Ed.). InTech. [11] Teklemariam, H. G., Kakati, V., Das, A. K., (2014). Application of VR Technology in Design Education. In DS 78: Proceedings of the E&PDE 2014 16th International conference on Engineering and Product Design, University of Twente, The Netherlands, pp (117-122). [12] Fallman Daniel, Anders Backman, and Kenneth Holmlund, “VR in education: An introduction to multisensory constructivist learning environments,” In: Conference on University Pedagogy. Umea University, Sweden (1999). [13] Winn, W., “A Conceptual Basis for Educational Applications of Virtual Reality”, University of Washington, Human Interface Technology Laboratory, Washington Technology Center, Seattle, Washington, Technical Publication R-93-9. (1993) [14] Chen, Y.H., Yang, Z.Y., and Lian, L.L.; On the development of a haptic system for rapid product development. Computer-Aided Design, 37, 559569 (2005). [15] Gao Zhan, Jiehua Wang, and Zhengzheng Jiang, 2012, Haptic Perception Evaluation for the Virtual Prototyping of Elastic Hand-Held Product Designs, Virtual and Physical Prototyping 7.2: 117–128. [16] Falcão, C. S., & Soares, M. M.; Application of virtual reality technologies in consumer product usability. In Design, User

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Experience, and Usability. Web, Mobile, and Product Design (pp. 342-351). Springer Berlin Heidelberg (2013). [17] Teklemariam, H. G., & Das, A. K. (2015). A case study of phantom Omni force feedback device for virtual product design. International Journal on Interactive Design and Manufacturing (IJIDeM), 1-12. [18] Chen, C. J., Ong, S. K., Nee, A. Y. C., & Zhou, Y. Q.; Haptic-based interactive path planning for a virtual robot arm. International Journal on Interactive Design and Manufacturing (IJIDeM), 4(2), 113-123 (2010). [19] Ran, Yang, and Zhenbiao Wang, “Virtual and augmented reality applications in industrial design,” 3rd International Conference on Machine Learning and Computing (ICMLC 2011). [20] Pantelidis, Veronica S, “Reasons to Use Virtual Reality in Education and Training Courses and a Model to Determine When to Use Virtual Reality.” Themes in Science and Technology Education 2.1-2 (2010): pp-59. [21] Furht Borivoje, Handbook of augmented reality. Vol. 71. New York: Springer, 2011. [22] Kesim, Mehmet, and Yasin Ozarslan, “Augmented reality in education: current technologies and the potential for education,” Procedia-Social and Behavioral Sciences, vol. 47, no. 222, pp. 297–302, Jan. 2012. [23] M. Billinghurst, Augmented reality in education, New Horizons Learning, 2002. [24] http://www.assignment10ct231.blogspot.in /2012/03/assignment-10-augmentedreality.html, (accessed in April 15, 2015). [25] http://www.archinect.com/news/article/112 581318/touchscreen-landscapes, (accessed in April 15, 2015). [26] http://www.allaboutroboticsurgery.com/for cehapticfeedback.html, (accessed in April 5, 2015). [27] http://www.uea.ac.uk/computing/virtualdentistry, (accessed in April 5, 2015).


Optimization of Sand Casting Process of Aluminum Rod using Taguchi Method: A Case Study at Akaki Basic Metal Industry Solomon Balacha Kebede, Abebaw Mekonnen, Ajit Pal Singh Production Engineering Department College of Engineering, Defense University, Bishoftu, Ethiopia E-mail: [email protected] Abstract: This study demonstrates optimization of the aluminum casting process by using Taguchi’s robust design technique a case study at Akaki Basic Metal Industry, Akaki, Ethiopia. The metal casting process involves a large number of parameters affecting the various casting quality features of the product. Some of the parameters are controllable and some are uncontrollable. In order to optimize the process, six control factors namely, sand type, metal fluidity, binding ratio, sprue size, riser size, and length to diameter ratio, were selected. Each process factor was considered at three levels. One of the noise factors considered was the variation in the pouring temperature of metal in the cavity. The other factors of interest were topography of the mold surface before casting and variation in the rate of melting in the furnace. To capture the variation in temperature gradient within castings and the variation in the topography of the mold surface, two separate pieces of aluminum were cast for each set of control factor levels, and the analysis was carried out. The quality characteristics selected were casting yield, surface defects and casting density. An orthogonal array (L18) was constructed for the six factors undertaken, and performing eighteen sets of experiments with their replicates generated the data. The signal-tonoise (S/N) ratios were calculated based on the design of experiments. A statistical analysis of variance (ANOVA) was performed to see which process parameters are statistically significant. A verification experiment was performed using the identified optimum conditions. The experimental results confirmed the validity of using Taguchi robust design method for enhancing sand casting process and optimizing the sand casting parameters in aluminum casting process. The optimum settings obtained in the study are A1, B1, C1, D2, and E2 and F2 which provides improvement in casting yield by 2.16dB, casting surface defects by 6.96dB, and casting density by 0.36dB. Keywords: Aluminum rod, Analysis of variance, Optimization, Orthogonal array, Sand casting, Signal-to-noise ratio, Taguchi method.

I. INTRODUCTION Foundry industries in developing countries suffer from poor quality and productivity due to involvement of number of process parameters in casting process. Even in a completely controlled process, defects in casting are observed and hence casting process is also known as process of

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uncertainty which challenges explanation about the cause of casting defects [1]. Casting defects analysis is the process of finding the root cause of occurrence of defects in the rejection of casting and taking necessary steps to reduce the defects and to improve the casting yield. Techniques like cause-and-effect diagrams, design of experiments (DoE), casting simulation, if-then rules (expert systems) and artificial neural networks (ANN) are used by various researchers for analysis of casting defects [1-2]. Taguchi introduces his approach in using experimental design for [3-4]:  Designing products/processes so as to be robust to environmental conditions,  Designing and developing products/processes so as to be robust to component variation,  Minimising variation around a target value. Barua et al. [5] used the Taguchi’s method to optimise the mechanical properties of the vacuum casting process. In this paper, they consider the effects of the selected process parameters on the mechanical properties of alloy casting and subsequent optimal settings of the parameters, which are accomplished using Taguchi’s parameter design approach. Syrcos [6] analysed various significant process parameters of the die casting method of aluminum alloy. He made an attempt to obtain optimal settings of the die casting parameters in order to yield the optimum casting density of the aluminum alloy castings. Masters et al. [7] describe a robust design method for reducing cost and improving quality in an aluminum remelting process. An experimental investigation into the process parameter effect is presented to determine the optimum configuration of design parameters for performance, quality and cost. Muzammil et al. [8] made a study for the optimization of a gear blank casting process by using Taguchi’s robust design technique. In this study, they demonstrated that the casting process involves a large number of parameters affecting the


various casting quality features of the product. The reduction in the weight of casting compared to the target weight was taken to be proportional to the casting defects. A. Statement of the Problem In casting process of aluminum, in the case industry, some of the casting problems observed was defects on the surface of the cast, porosity and lower casting yield etc. To avoid or determine the problems a number of test castings and re-melting is inevitable every time a new mold design is changed [9]. This method is costly and results in a lot of waste in terms of time and cost of re-melting and labor. The quality of casting is not depending on the microstructure, but also supported by the good appearance and the casting process defects related to the process parameters in manufacturing. Failure to control one of the process parameters can lead to give an impact in aluminum sand casting processes. In order to overcome this failure, this study was to determine the most influential process parameters and process optimization in aluminum casting processes. B. Objectives of the Study General Objective The objective is to optimize sand casting process of aluminum rod using Taguchi method. Specific Objectives  Identifying the control factors with their alternate levels and noise factors.  Selecting the appropriate orthogonal array.  Conducting the experiment as per the experimental matrix selected.  Computation of analysis of variance.  Performing verification experiment using the optimum value of control parameters obtained.  The quality characteristics to be observed and the objective functions to be optimized in aluminum rod sand casting process.  To study aluminum rod sand casting process in order to determine control parameters settings in which noise has minimum effect on the quality characteristics. II. MATERIALS AND METHODS A. Materials The materials used for this research study are:  Sand (new and reclaimed sand): To prepare the sand mold.

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 Wooden pattern: Having the shape of the desired casting to make an imprint in the sand.  Sand binder chemicals (resin and catalyst): To bind the sand in order to form the mold.  Rectangular metal plate: To support the sand mold until it dries.  Aluminum metal (ingot).  Ladle: To transport the molten aluminum from the furnace and pouring in to the mold cavity.  LPG gas: For heating the ladle.  Resistor furnace: For melting the metal aluminum.  Hammer: To separate the sand from the cast metal after the molten metal solidified.  Weight balance: To measure the weight of the aluminum cast.  Steel ruler: To measure the length of the aluminum cast.  Lathe machine and grinder: To machine the aluminum and separate it from the attachments.  Clamp: To firmly hold the sand mold (the cope and drag). B. Methods Used The term robust design refers to the design of a product that causes no trouble under any conditions and answers the question: What is a good-quality product? As a generic term, quality or robust design has no meaning; it is merely an objective. A product that functions under any conditions is obviously good. Again, saying this is meaningless. All engineers attempt to design what will work under various conditions. The key issue is not design itself but how to evaluate functions under known and unknown conditions. For finding the optimum settings of the control factors, Taguchi’s robust design methodology is applied. This method can be applied by using eight experimental steps that can be grouped into three major categories as follows [3, 4, 10]: (a) Planning the experiment:  Identify the main function of aluminum sand casting processes.  Identify noise factors and the testing conditions for evaluating the quality loss.  Identify the quality characteristics to be observed and the objective function to be optimized.  Identify the control factors and their alternate levels.


 Design the matrix experiment that defines the data analysis procedure.  (b) Performing the experiment:  Conduct the matrix experiment. (c) Analyzing and verifying the experimental results:  Analyzing the data, determining the optimum levels for the control factors, and predicting performance under these levels.  Conducting the verification experiment and planning future actions. The procedure for applying the above steps in the present work to improve the quality and yield of aluminum sand casting process is described as follows. 1) Planning the Experiment The aluminum casting was performed using new, reclaimed and mixtures of both sands mold. Three wooden patterns with three different length and same diameter were prepared. Molds were prepared using, new sand, reclaimed sand and mixture of both sand, and the molten metal (aluminum) was poured into the molds to get the required casting. Thirty-six castings produced were inspected to determine the defects in them. Controlling the casting defects was particularly difficult due to their intermittent occurrence; furthermore, no theoretical model existed to predict the defect formation as a function of the various process parameters; therefore, experimentation was the only way to control the casting defects problem. The molds were kept in open air for one whole day in order to dry them. Six molding boxes were prepared at a time. For the casting process about seventy kilograms aluminum metal was melted. 2) Noise Factors and the Testing Conditions In aluminum sand casting process experiment, a number of noise factors affecting the casting process were identified. Some of these are variation of ambient temperature, humidity, pouring temperature, pouring speed and so forth. One of the noise factors was the variation in the pouring temperature of metal in the cavity, as it took some time to fill the mold and, hence, a thermal gradient existed in the process. The other factors of interest were topography of the mold surface before casting, variation in the rate of melting in the furnace. To capture the variation in temperature gradient within castings, metal flow rate and the variation in the topography of the mold surface, two separate pieces of aluminum bar were

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cast for each set of control factor, levels, and the analysis was carried out. 3) Quality Characteristics and Objective Function Casting yield, surface defects, and casting density were selected as a quality characteristics. Casting yield can be defined as the ratio of the weight of casting to the total weight of casting with attachment (gates and risers etc.). The casting yield and casting density are ‘larger-the-better’ type of the quality characteristic Eq. 1. The surface defect is ‘smaller-the-better’ type of the quality characteristic Eq. 2 [11, 12]. The smaller the number of surface defects, better the casting quality, which implies better process performance [13]. 4) Control Factors and Their Levels In general, for a sand casting process, the following process parameters are important viz., type of the sand, sand grain shape, size and distribution, clay content, moisture content, permeability, ramming, metal composition, pouring temperature, pouring time, pouring height, metal fluidity, running and gating, risering or feeding, and design of castings. A cause-and-effect diagram is constructed to identify the control factors that may affect the aluminum sand casting process. On the basis of cause and effect diagram six control factors were selected, and then their levels were defined as shown in Table 1. 5) Matrix Experiment and Data Analysis Plan In robust design experiment, we vary the settings of control factors simultaneously in a few experimental runs. This efficient way of studying the effect of control factors can be achieved by planning matrix experiment using orthogonal arrays. An orthogonal array for a particular robust design can be constructed from the knowledge of the number of control factors, their levels, and the desire to study specific interactions. In aluminum sand casting process study, there was no particular reason to study specific interactions and no unusual difficulty in changing the levels of any factor. In order to use a standard orthogonal array fitting our requirements the total degree of freedom (DOF) for the present study is determined [14]. Causes are usually grouped into major categories to identify these sources of variation. The categories typically include: People, Methods, Material, Machines, Measurement and Environment.


Figure 1: Cause-and-effect diagram for quality characteristics

The initial settings of the seven control factors are indicated by an underscore in Table 1. Table 1: Control factors and their levels (starting condition are under lined) Control Control Factors Levels Factors 1 2 3 A Sand type New Reclaimed Mixed sand sand sand B Metal fluidity High Medium Low C Sprue size (mm) 400 280 160 D Riser size (mm) 100 80 60 E Sand binder ratio 1 0.75 0.5 F Length to diameter 6:1 4:1 2.1 ratio (L/D)

Consider for instance, in this study:  Six parameters are identified as the most important causes for casting defects and yield.  Three different levels are set for each parameter [15]. Then, the name of the appropriate array can be found from the array selector table by looking at the column and row corresponding to the number of parameters and number of levels respectively. Thus, for six parameters and three levels the appropriate array would be an L18 array. Since the subscript represents the number of experiments, it is required to conduct 18 individual experiments in this study. In the aluminum casting process study with six control factors, each of three levels were found with no specific interaction; based on this observation, DOF were calculated as shown below. Degree of freedom of overall mean=1 DOF of A, B, C, D, E, and F at 3 levels 6  (3-1) =12 Therefore, total degree of freedom for six factors each at these levels is 12. A three-level orthogonal

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array with at least 12 degrees of freedom was required. The orthogonal array was prepared, and 18 runs of the experiments were conducted at three levels as shown in Table 2. To prevent translation error, the entire matrix of Table 2 was translated using the level definitions to create the experimental matrix as shown in Table 3. A. Conducting the Matrix Experiment Eighteen experiments were conducted according to the experimental matrix given in Table 3. Nine experiments were performed at a time. The metal was melted in the resistor furnace for all the experiments. In this scheme, the furnace for the melting of the metal was operated four times to conduct all 36 experiments. The nine molds for the nine experiments were put in descending order of the metal fluidity high, low, and medium. After pouring the molten metal, the castings were allowed to cool for 24 hours and then the cast were taken out of the mold. The cycle was repeated four times to perform all the experiments. Each experiment was done twice. Table 2: Orthogonal array Exp. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

L18 and factor assignment

Column Number and Matrix Assignment 1 2 3 4 5 6 7 8 e A B C D E e F 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 3 3 3 3 3 3 1 2 1 1 2 2 3 3 1 2 2 2 3 3 1 1 1 2 3 3 1 1 2 2 1 3 1 2 1 3 2 3 1 3 2 3 2 1 3 1 1 3 3 1 3 2 1 2 2 1 1 3 3 2 2 1 2 1 2 1 1 3 3 2 2 1 3 2 2 1 1 3 2 2 1 2 3 1 3 2 2 2 2 3 1 2 1 3 2 2 3 1 2 3 2 1 2 3 1 3 2 3 1 2 2 3 2 1 3 1 2 3 2 3 3 2 1 2 3 1

To determine the casting yield, each casting was weighed twice, i.e., before and after removing the gates and risers, etc. After machining of the cast, the weight and size were measured to determine its density. Weight of the casting was measured by a balance and the yield was computed. The castings were carefully inspected visually for any surface defects. The observed data of concerning


casting yield, surface defects, and after machiningdensity for the casting are listed in Table 4. Table 3: Experimental matrix parameters Sand Metal Binder Sprue Riser L/D Type Fuidity Ratio Size Size Ratio A B C (mm) (mm) F D E 1 New sand High 1.00 400 100 6 2 New sand Medium 0.75 280 80 4 3 New sand Low 0.50 260 60 2 4 Reclaimed High 1.00 280 80 2 sand 5 Reclaimed Medium 0.75 260 60 6 sand 6 Reclaimed Low 0.50 400 100 4 sand 7 Mixed High 0.75 400 60 2 sand 8 Mixed Medium 0.5 280 100 6 sand 9 Mixed Low 1.00 260 80 4 sand 10 New sand High 0.5 260 80 6 11 New sand Medium 1.00 280 60 4 12 New sand Low 0.75 400 100 2 13 Reclaimed High 0.75 260 100 4 sand 14 Reclaimed Medium 0.5 400 80 2 sand 15 Reclaimed Low 1.00 280 60 6 sand 16 Mixed High 0.5 280 60 4 sand 17 Mixed Medium 1.00 260 100 2 sand 18 Mixed Low 0.75 400 80 6 sand Table 4: Experimental results of casting yield, surface defects, and casting density Exp. Casting Casting Surface No. Yield (%) Density Defects/Area (gm/cm)3 Exp. No.

Trial 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

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1 70.4 60.1 58.2 51.1 67.7 54.1 49.1 65.3 63.0 66.7 64.0 51.8 63.2 57.6 50.8 60.5 52.0 67.0

Trial 2 66.2 63.8 57.0 55.4 60.4 63.5 53.8 71.5 58.8 70.2 60.3 55.6 60.1 62.7 56.3 64.0 55.3 72.1

1 2.87 2.69 2.44 2.78 2.63 2.74 2.65 2.76 2.69 2.74 2.74 2.73 2.78 2.70 2.64 2.73 2.72 2.69

Trial 2 2.90 2.53 2.65 2.82 2.65 2.54 2.83 2.76 2.85 2.70 2.63 2.60 2.59 2.60 2.66 2.84 2.67 2.71

1 19 23 27 21 34 12 27 10 29 29 12 28 7 19 33 8 25 26

2 31 28 15 5 42 28 19 31 9 4 30 33 16 31 28 14 18 32

B. Analysis and Discussion of the Experimental Results 1) Analysis of the Data The casting yield is ‘larger-the-better’ type of the quality characteristic. The S/N ratio for ‘larger-thebetter’ type was used as per Eq. 1. We have two response values for each experimental condition. As per Table 4 for experiments, the S/N ratio for the casting yield, were computed using Eq. 1 as follows:    1 n 1  2   n  i 1 y  i   

  10 log10 

(1)

Where: yi; for i = 1, 2, ...n are the response values for a trial condition are repeated n times. 1  1 1  y1  10 log10      3.32dB 2 0.6622   2  0.704 1  1 1   y 2  10 log10      4.17dB 2 0.6382   2  0.601

Where,  y1 and  y 2 are S/N ratios for casting yield of the first and second experiment. The same method of calculation was applied to the remaining experiments. The surface defect is ‘smaller-the-better’ type of the quality characteristic. As per Table 4 the S/N ratio for ‘smaller-the-better’ type was used as per Eq. 2 and computed as follows.  1 n 2  (2)    10 log  10

n  

y i 1

i

 

Where: yi; for i = 1, 2, ...n are multiple values of performance characteristics of y. 1  s1  10 log10  (192  312 )  28.20 dB 2  1  s 2  10 log10  (232  282 )  28.17 dB 2   Where, s1 and s2 are S/N ratios for casting’s

surface defects. The same method of calculation was applied to the rest of experiment too. The density of the casting is ‘larger-the-better’ type which is given by Eq. 1 was applied for casting density also. As per Table 4 for experiments the S/N ratio is: 1  1 1  d1  10 log10      9.20dB 2 2 2.532    2.90


1  1 1  d 2  10 log10      8.32dB 2 2.532   2  2.69 Where, d1 and d 2 are S/N ratios for casting

density. The same method of calculation was applied to the rest of experiment too. The results for all 18 experiments are tabulated in Table 5. 2) Analysis of Variance The next step in data analysis is to perform a formal ANOVA to estimate the effect of each control factor on each of the three characteristics of interest. ANOVA was performed by computing the following. (i) Calculation of average S/N ratio (  y ) for casting yield by factor level: For factor A at level1 was calculated as per Eq. 3. Average S/N ratio  by factor level for each factor: mA1  1/6(η1  η2  η3  η10  η11  η12 ) (3) Where m A1 is the average S/N ratio of factor A at level 1. m A1  1 / 6(3.32  7.18  4.76  3.28  4.71  5.49)  4.79 dB

Where, m A1 is the average S/N ratio of factor A at level 1. The average S/N ratio for levels A 2 and A 3 of sand type, as well as those for various levels of the other factors, can be computed in a similar way. Exp. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Table 5: Summary of S/N ratios for each experiment Experimental Factor Casting Casting Surface Levels Yield Density Defects A B C D E F  y (dB)  d (dB) s (dB) 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

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1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 2 3 1 2 3 2 3 1 3 1 2 2 3 1 3 1 2

1 2 3 2 3 1 1 2 3 3 2 1 3 1 2 2 3 1

1 2 3 2 3 1 3 1 2 2 3 1 1 2 3 3 1 2

1 2 3 3 1 2 3 1 2 1 2 3 2 3 1 2 3 1

-3.32 -4.17 -4.76 -5.44 -3.24 -4.62 -5.81 -3.32 -4.31 -3.28 -4.71 -5.49 -4.27 -4.49 -5.50 -4.10 -8.38 -3.17

-9.20 -8.32 -8.66 -8.94 -8.43 -8.72 -8.74 -8.81 -8.89 -8.69 -8.57 -8.51 -8.56 -8.47 -8.46 -8.89 -8.75 -8.63

-28.20 -28.17 -26.79 -23.67 -31.64 -26.67 -27.36 -27.25 -26.63 -26.32 -27.12 -26.67 -25.59 -28.20 -29.72 -21.10 -26.76 -29.29

(ii) Calculation of DOF: Calculation of DOF for each factor is done using Eq. 4.Since factor A has three levels, it has two degrees of freedom. In general, the degrees of freedom associated with a factor are one less than the number of levels. DOF= Number of factor levels 1 (4) DOF=3-1=2 (iii) Calculation of the total sum of squares (SS): As per Eq. 5. n

Total sum of squares 

 (  m ) i

i 1

2

(5)

Where, m the overall mean average of S/N ratio level and i is the response of i th experimental run. Total sum of squares for the casting yield  (3.32  4.57) 2  ...  (3.17  4.57) 2  26.83 (dB) 2 The total sum of squares for the remaining two quality characteristics is obtained in a similar way. (iv) Calculation of sum of squares due to various factors: Sum of squares due to factor A for casting yield can be computed as per Eq. 6. Sum of squares due to various factors = 6m A1  m 2  6m A 2  m 2  6m A3  m 2 (6)  6(4.79  4.57) 2  6(4.59  4.57) 2  6(4.85  4.57) 2  0.3912

For there are six experiments each at levels A1 , A 2 , A 3 consequently each square due to each level should have multiplier equal to the number of experiments for that specific case. (v) Calculation of sum of squares due to error ( S e ): Sum of squares due to error can easily be calculated using Eq. 7. Total sum of squares (TSS) = (Total of the sums of squares due to various factors) + (Sum of squares due to error) (7) For casting yield, (vi) Calculation of mean square: Using Eq. 8, mean square values for each factor can be determined. Using Eqs. 5 and 6 mean square values for each factor can be determined. Mean square=Sum of square  DOF (8) Thus, mean square of factor D for casting yield  4.326  2  2.163 (dB) 2 (vii) Calculation of pooled error sum of squares: In the interest of gaining the most information from a matrix experiment, all or most of the columns should be used to study process or product parameters. As a result, no DOF may be


3) Discussion on Experimental Results Referring to Fig. 2 and Table 9the following observation can be made about the optimum settings for the casting:  Sand type (Factor A): has negligible effect on casting yield. It has large effect on surface defects and negligible effect on casting density is observed. The optimum levels forcasting yield is

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A 2 (reclaimed sand); for surface defect is A1 (new sand) and casting density is A 3 (mixture of both

sands).  Metal fluidity (Factor B): has large effect on casting yield and negligible effect on casting density; and also it has moderate effect on surface defects. Note: *Indicates the sum of squares added together to form the pooled error sum of squares shown in parentheses.

E F

Average

A B C D E F

-4.85

2

0.3192*

0.1596

-4.72

2

4.8900

2.445

27.78

-4.10

2

4.3260

2.163

24.58

-4.71

2

2.2988

1.1499

13.07

-4.69

2

1.3294

0.6647

7.55

-5.73 Error Total (Error)

2 5 17 (7)

13.374 0.2926* 26.83 (0.6118)

6.687 0.0592 1.58 (0.088)

75.98

F

DOF

Mean Square

D

Sand type -4.79 -4.59 Metal fluidity -4.37 -5.52 Binder ratio -5.27 -4.86 Sprue size -4.48 -4.78 Riser size -4.90 -4.65 L/D ratio -3.64 -4.87

3

Table 7: Analysis of casting density  by Factor

8.74

2

0.0642*

0.0321

8.65

2

0.2100

0.105

1.31

8.71

2

0.2136

0.1068

1.06

8.63

2

0.0156*

0.0078

8.63

2

0.0720

0.0360

2 5 17 (11)

0.0480* 0.8403* 1.4637 (0.9681)

0.0240 0.1681 0.0861 (0.080)

Level (dB) 1 2 Sand type 8.64 8.60 Metal fluidity 8.82 8.56 Binder ratio 8.79 8.53 Sprue size 8.70 8.66 Riser size 8.75 8.65 L/D ratio 8.69 8.66

F

Mean Square

C

Sum of Square

B

Level (dB) 2

Sum of Square

A

1

Table 6: Analysis of casting yield  by Factor

DOF

Factor

Average

Factor

left to estimate error variance. However, an approximate estimate of the error variance can be obtained by pooling the sum of squares corresponding to the factors having the lowest mean squares. As a rule of thumb, the sum of squares corresponding to the bottom half of the factors (as defined by lower mean square) corresponding to about half of the degrees of freedom be used to estimate the error mean square or error variance. Here also, the lowest sum of squares are noted and then summed. Consequently, pooled error sum of squares for casting yield  0.3192  0.2926  0.6152(dB) 2 Error of variance computed in this way is indicated by parentheses, and the computation is called pooling. By the traditional statistical assumption, pooling gives a biased estimate of error variance. To obtain a better estimate of error variance, a significantly larger number of experiments would be needed, the cost of which is usually not justifiable compared to the added benefit. (viii) Calculation of F ratio: We can calculate this value using previously obtained values of mean square and pooled error mean square. As per Eq. 9, F Ratio=Mean square of each factor ÷ Pooled error mean square (9) F ratio of casting yield, for factor D (sprue size) is calculated as: 1.1499  0.088=13.07 The factor which effects for casting yield, surface defects, and density their respective ANOVA are shown in Tables 6-8 respectively. A summary of the factor effects is tabulated in Table 9, and the factor effects are displayed graphically in Fig. 2 which makes it easy to visualize the relative effects of the various factors on all three characteristics. Note*Indicates the sum of squares added together to form the pooled error sum of squares shown in parentheses.

3

8.69 Error Total (Error)

4.5

 Binder ratio (Factor C): This factor has large effect on casting yield, negligible effect on surface defect, and small effect on casting density.  Riser size (Factor E): This factor has large effect on casting yield; negligible effect on surface defects and moderate effect on casting density.


A B C D E F

Level (dB) 2 3

Sand type -26.67 27.59 Metal fluidity -25.37 27.65 Binder ratio -27.02 27.65 Sprue size -27.70 -25.63 Riser size -26.87 -26.50 L/D ratio -28.73 -25.33

Mean Square

1

Sum of Square

Table 8: Analysis of surface defects Average  by Factor

DOF

Factor

 L/D ratio (Factor F): This factor has largest effect on casting yield, small effect on surface defect and negligible effect on casting density.

F

-27.63

2

15.39

7.695

2.11

-27.00

2

17.86

8.930

2.45

-27.63

2

7.09*

3.545

-27.29

2

14.42

7.2100

-27.29

2

1.87*

0.935

-26.56 Error Total (Error)

2 5 17 (9)

35.57 18.39* 100.06 (27.35)

17.785 3.6780 5.8858 (3.630)

1.98

Table 9: Summary of factor effects

4.89

(a) For surface defects

(b) For casting yield

(c) For casting density Figure 2: Plots of factor effects for the aluminum rod casting

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As seen from the observations Tables 6-8, the optimum settings for A , B , C, D , E , and F are found to be A 1 , B1 , C1 , D 2 , E 2 , and F2 . The optimum settings of sprue size for casting yield; surface defects are E 2 and for casting density is E1 . Sprue size has large effect on casting yield, small effect on surface defect, negligible effect casting density. In this study of aluminum sand casting process, in deciding for the optimum levels, the following consideration has been taken into account: To avoid any quality problem that can cause rejection and significant scrap, it was decided to take care of the casting yield and the surface defect. Casting Yield

y dB A. Sand type

Surface Defects F

s

Casting Density F

dB

-26.67 A1 -4.79 A2 -4.59 -27.59 A3 -4.85 -27.63 B. Metal fluidity B1 -4.37 -25.37 27.78 -27.65 B2 -5.52 B3 -4.72 -27.00 C. Binder ratio C1 -5.27 -27.02 24.58 C2 -4.86 -25.63 -27.29 C3 -5.63 D. Sprue size -27.70 D1 -5.48 13.07 D2 -4.78 -25.63 D3 -4.71 -27.29 E. Riser size E1 -4.90 -26.87 7.55 E2 -4.65 -26.50 -27.29 E3 -4.69 F. Length to diameter ratio -28.73 F1 -4.64 75.98 F2 -4.78 -25.33 F3 -5.73 -26.56 Note: The starting levels are underlined

d

F

dB

2.11

8.64 8.60 8.74

3.00

8.82 8.56 8.65

1.31

8.79 8.53 8.71

1.06

2.42

8.70 8.66 8.63 8.70 8.66 8.63

5.96

4.50

8.69 8.66 8.69

After deciding the optimum conditions, the next step is to predict the anticipated improvement under the chosen optimum conditions. To do this, first of all, predict the S/N ratio for casting yield, surface defect, and casting density using additive model; computation was done for two conditions: a) For starting condition that is using levels A1 , B 2 , C 3 , D1 , E 3 and F1 ;


b) For optimum conditions, using chosen optimum settings A1 , B1 , C1 , D 2 , E 2 and F2 . Calculation is based on the additive model formula and computed as under: The effect of control factor at level i = the average S/N ratio of factor of interest at level i - Overall average (11) Calculation for factor A1 and B 2 starting condition, for casting yield: A1  m A1  m  4.79  (4.67)  0.12dB B2  m B2  m  5.52  (4.67)  0.85 dB

Calculation for factor A1 and condition, for surface defects:

starting

B2

A1  mA1  m  26.67  (26.89)  0.22dB B2  mB2  m  27.65  (26.89)  0.76 dB

Calculation for factor A1 and condition, for casting density:

starting

B2

A1  mA1  m  8.67  8.64  0.03dB B2  mB2  m  8.67  8.56  0.11dB

The effects of the remaining factors on yield, density, and surface defects for starting and optimum conditions are calculated in similar way. The computed results are displayed in Table 10.

Setting

-0.03 -0.11 0.04 0.03 -0.04 0.02 8.67

A1 B1 C1 D2 E2 F2

-7.40

-30.16

8.58

Density

Density

0.22 -0.76 -0.04 -0.81 -0.04 -1.84 -26.89

Surface defect

Surface defect

A A1 B B2 C C3 D D1 E E3 F F1 Over all mean Total

Yield

Yield -0.12 -0.85 -0.96 -0.81 -0.02 0.03 -4.67

Setting

Factor

Table 10: Prediction using the additive model Starting condition Optimum condition Contribution (dB) Contribution (dB)

-0.12 0.30 -0.60 0.11 0.02 1.03 -4.67

0.22 1.52 -0.13 1.26 0.39 1.56 -26.89

0.03 0.15 0.12 0.11 0.02 -0.02 8.67

-2.30

-22.07

9.08

Referring to the Table 10, it is to be noted that, an improvement has been gained as follows. Casting yield= [2.3  (7.5)]  5.20 dB , Surface defects= [22.07  (29.63)]  5.20 dB , and Casting density= [9.08  8.30]  0.78dB . Anticipated improvement as per Table X is as follows. Casting yield: [2.30  (7.40)]  5.10dB

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Surface defect: [22.07  (30.16)]  8.09dB Casting density: [9.08  8.58]  0.58dB 4) Verification Experiment Verification experiments confirm the degree of improvement realized when the chosen parameter settings are used. Thus, conducting this experiment is an important final step of a robust design project. One important point to be remembered here is that if the observed S/N ratios under the optimum conditions are close to their respective predictions, then we conclude that the additive model on which the matrix experiment was based is a good approximation of the reality. According to Taguchi techniques, to determine the optimal conditions and to compare the results with the expected conditions, it is necessary to perform a confirmation experiment. The summary of the confirmation experiment data are given in Table 11, where S/N ratios are calculated as per Eqs. 1 and 2. The starting condition as per Table 10 and optimum condition as per Table 11 is given in Table 12. Comparisons between predicted results (Table 10) and achieved result are shown in Table 13 and Fig. 3 shows the cast produced at the optimum parameter setting. Table 11: Summary of the data of confirmation experiment Casting Surface defects Casting yield (%) (defect/surface density area) (gm.cm3) Trial Trial Trial 1 2 1 2 1 2 Confirmation 51.5 55.4 21 5 2.7 2.82 experiments 8 S/N ratio -5.24 -23.67 8.94 Table 12: Comparison between the starting and optimum condition Conditions Casting yield Surface Casting (dB) defect (dB) density (dB) Starting -7.40 -30.63 8.58 Optimum -5.24 -23.67 8.94 Improvement 2.16 6.96 0.36 Table 13: Comparison between predicted and achieved results Casting yield Surface Casting defect  s density d y Anticipated by prediction Achieved by confirmation

(dB) 5.10

(dB)

(dB)

8.09

0.58

2.16

6.96

0.36


Figure 3: The aluminum rod obtained at optimum setting.

III. CONCLUSION On the basis of the experiment conducted (Tables 6-8, 11), the following conclusions about the optimum setting can be drawn:  The optimum setting for sand type corresponding to maximum value of the S/N ratio was the new sand ( A1 ). It can be concluded that casting surface defect can be reduced using the new sand.  The high metal fluidity was obtained as an optimum level for pouring molten metal. The high flow ability of the metal resulted in the uniform flow of the molten metal into the mold cavity. Confirmation experiment was conducted at the optimum settings chosen A1 , B1 , C1 , D 2 , E 2 and F2 . Referring to the results of confirmation experiment (Table 11), the following conclusions are drawn:  Agreement to predictions: The results of confirmation experiment have fair agreement with the predictions in case of surface defects and casting density but somewhat less agreement in case of casting yield.  Casting yield: Casting yield has been improved by 2.16dB which mean reduction in yield variability.  Surface defects: The surface defects has shown 6.96dB improvements (i.e. decrease in defects).  Casting density: it is observed that the casting density shows improvement by 0.36dB. This is an improvement from the starting condition. From the analysis, it is proved that, by improving the quality by Taguchi’s method of parameter design at the lowest possible cost, it is possible to identify the optimum levels of signal factors at which the noise factors’ effect on the response parameters is less.

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IV. RECOMMENDATIONS The following recommendations are suggested to the industry based on the result of the current study:  The time taken to pour the molten aluminum to the mold, in case of large parts is a somewhat long so it is better to use double sprue so that the metal fluidity could be maintained at the required level.  There should be some mechanism to maintain pouring height of the ladle there by reducing turbulence and trapped air in the cast  It would be better if the riser is always placed at the heavy section of the part in order to reduce shrinkage of the cast part. REFERENCES [1] H.C. Pandit, Amit Sata, V. V. Mane, Uday A. Dabade, 2012, Novel Webpublished in technical transactions of 60th Indian Foundry Congress, 2 4th March 2012, Bangalore, pp 535 544. [2] Rahul Bhedasgaonkar, Uday A. Dabade, May 2012, Proceedings of 27th National Convention of Production Engineers and National Seminar on Advancements in Manufacturing VISION 2020, organised by BIT, Mesra, Ranchi, India. [3] Ross PJ (1988) Taguchi techniques for quality engineering. McGraw Hill, New York. [4] Taguchi G (1986) Introduction to quality engineering. Asian Productivity Organization, Tokyo, Japan. [5] Barua PB, Kumar P, Gaindhar JL (1997) Optimization of mechanical properties of Vprocess castings by Taguchi method. Indian Foundry J 17–25 [6] Syrcos GP (2002) Die casting process optimization using Taguchi methods. J Mater Process Tech 135:68–74 [7] Masters I, Khoei AP, Gethin DT (1999) The application of Taguchi methods to the aluminium recycling process. In: Proceedings of the 4th ASM Conference and Exhibition on the Recycling of Metals, Vienna, Austria, June 1999, pp 115–124 [8] Muzammil M, Singh PP, Talib F (2003) Optimization of gear blank casting process by using Taguchi’s robust design technique. J Qual Eng 15:351–359


[9] Altan, M., (2010), reducing shrinkage in injection moldings via the Taguchi, ANOVA, and neural network methods, Materials and Design, p.599-604. [10] Padke, M.S. (1989) Quality Engineering Using Robust Design.Prentice Hall, Englewood Cliffs, NJ. [11] George, P.M., Raghunath B.K., Manocha L.M., Warrier A.M., (2002), EDM machining of carbon-carbon composite-A Taguchi approach. [12] Raghunath, N.; Panday, P.M., (2007) Improving accuracy through shrinkage modeling by using Taguchi method in selective laser sintering. International Journal of Machining Tolls & Manufacture. [13] Ghani, J.A., Choudhury, I.A., Hassan, H.H. (2002) Application of Taguchi method in the optimization of end milling parameters. J.Mater Process Tech. [14] Oktem, H.; Erzurumlu, T.; Uzman, (2007), Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Materials and Design, v. 28, p .271-278. [15] Campbell, H.L., (1986), Metal Castings, John Wiley & Sons Inc., New York.

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