Available online at www.sciencedirect.com Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect Available online ScienceDirect Available onlineatatwww.sciencedirect.com www.sciencedirect.com Procedia Manufacturing 00 (2017) 000–000 Procedia Manufacturing 00 (2017) 000–000 Procedia Manufacturing 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Manufacturing 20 (2018) 271–276 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
2nd International Conference on Materials Manufacturing and Design Engineering 2nd International Conference on Materials Manufacturing and Design Engineering 2nd International Conference on Materials Manufacturing and Design Engineering
Optimization of the surface roughness in ball end milling Optimization of the surface roughness in ball end milling Optimization of the surface roughness in ball end milling of titanium alloy Ti-6Al-4V using the Taguchi Method of titanium alloy Ti-6Al-4V using the Taguchi Method Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30 June of titanium alloy Ti-6Al-4V using the Taguchi Method a,b* 2017, Vigo (Pontevedra), Spain a, A-S. Alghamdib W. Mersnia, M. Boujelbene , S. Ben Salem a b W. Mersniaa, M. Boujelbenea,b* , S. Ben Salem a,b* a, A-S. Alghamdib W. Mersni , M. Boujelbene , A-S. Alghamdi University of Tunis El Manar, ENIT,, S. Ben Salem Ecole Nationale d’Ingénieurs de Tunis, Tunisia.
University ofcapacity Tunis El ENIT, Ecole NationaleHail, d’Ingénieurs de Costing models forUniversity optimization in Industry of Manar, Hail, College of Engineering, Kingdom of Tunis, Saudi Tunisia. Arabia.4.0: Trade-off University of Tunis El Manar, Ecole Nationale de Tunis, Tunisia. University of Hail, CollegeENIT, of Engineering, Hail, d’Ingénieurs Kingdom of Saudi Arabia. College of Engineering, Hail, Kingdom of Saudi efficiency Arabia. betweenUniversity usedof Hail, capacity and operational a
a a
b
b b
Abstract Abstract A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb Abstract The aim of this work is to study the influence of some milling parameters (the cutting speed Vc, the radial depth of cut ae and the The aim of this work is to study the influence of some milling parameters (the cutting speed Vc, the radial depth of cut a e and the a feed per tooth fz) on the 3D average surface roughness Sa. The Taguchi method was applied to find optimum process parameters University of Minho, 4800-058 Guimarães, Portugal The aim of this work is to study the influence of some milling parameters (the cutting speed Vc, the radial depth of cut a e and the ) on the 3D average surface roughness Sa. The Taguchi method was applied to find optimum process parameters feed per tooth f b z in ball end milling of the titanium alloy Ti-6Al-4V with an inclined workpiece angle of 25°. An orthogonal array L9 and a signal Unochapecó, 89809-000 Chapecó, SC, Brazil ) on the 3D average surface roughness Sa. The Taguchi method was applied to find optimum process parameters feed per tooth f z in ball end milling of the titanium alloy Ti-6Al-4V with an inclined workpiece angle of 25°. An orthogonal array 9 and a signal to noise ratio S/N were used to analyze the impact of each cutting parameter on the surface roughness Sa L to select the in ball end milling of the titanium alloy Ti-6Al-4V with an inclined workpiece angle of 25°. An orthogonal array L9and and a signal to noise ratio S/N were used to analyze the impact of each cutting parameter on the surface roughness Sa and to select the optimum levels of the machining parameters. to noise ratio S/N were used to analyze the impact of each cutting parameter on the surface roughness Sa and to select the optimum levels of the machining parameters. optimum levels of the machining parameters. © 2017The Authors. Published by Elsevier B.V. Abstract The Authors. Published by Elsevier B.V. © 2017 Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials © 2018 The Authors. Published by Elsevier B.V. The Authors. Published by Elsevier B.V. © 2017 Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Under the concept of "Industry 4.0", production processes will be pushed to on be Materials increasingly interconnected, Peer-review under responsibility of the scientific committee of the 2nd International Conference Manufacturing and Manufacturing and Design Engineering. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and Design Engineering. Design Engineering. information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization Manufacturing and Design Engineering.
goesKeywords: beyond Ball end milling, 3D Surface roughness; Taguchi method; Signal to noise ratio S/N; Cutting speed the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Keywords: Ball end milling, 3D Surface roughness; Taguchi method; Signal to noise ratio S/N; Cutting speed Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of Keywords: Ball end milling, 3D Surface roughness; Taguchi method; Signal to noise ratio S/N; Cutting speed maximization. The study of capacity optimization and costing models is an important research topic that deserves 1. Introduction contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical 1. Introduction model for capacity management based on different costing models (ABC and TDABC). A generic model has been 1. Introduction High end of shaped parts become more and more common activities in aeronautic developed andspeed itball wasball used to milling analyze idlecomplex capacity andparts to design strategies towards thecommon maximization of organization’s High speed end milling of complex shaped become more and more activities in aeronautic industries where the Titanium alloy Ti-6Al-4V has been used thanks to its high corrosion resistance, its low density value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity High speed ball end milling of complex shaped parts become more and more common activities in aeronautic industries where the Titanium alloy Ti-6Al-4V has been used thanks to its high corrosion resistance, its low density and its high mechanical resistance [1] but it is very difficult to cut because of its poor machinability. Also the optimization might hide operational inefficiency. industries where the Titanium alloy Ti-6Al-4V has been used thanks to its high corrosion resistance, its low density and chemical reactivity of the Titanium leads to the early wear of the cutting tools and damages consequently the finish its high mechanical resistance [1] but it is very difficult to cut because of its poor machinability. Also the © 2017 Authors. Published by Elsevier and its The high mechanical resistance [1] B.V. but it is very difficult to cut because of its poor machinability. Also the chemical reactivity of the Titanium leads to the early wear of the cutting tools and damages consequently the finish surface under [2]. Hence, it’s compulsory to committee optimize of continuously the cutting parameters to select the adequate Peer-review responsibility of the scientific the Manufacturing Engineering Society and International Conference chemical reactivity of the Titanium leads to the early wear of the cutting tools and damages consequently the finish surface [2]. Hence, it’s compulsory to optimize continuously the cutting parameters and to select the adequate machining strategy in high speed milling of Ti-6Al-4V in order to satisfy the surface integrity requirements and to 2017. surface [2]. Hence, it’s compulsory to optimize continuously the cutting parameters and to select the adequate machining strategy in high speed milling of Ti-6Al-4V in order to satisfy the surface integrity requirements and to increase the tool life. machining strategy in high speed milling of Ti-6Al-4V in order to satisfy the surface integrity requirements and to increase the tool life. Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency increase the tool life. * Corresponding author. M. Boujelbene 1. * Corresponding author. M. Boujelbene Introduction E-mail address:
[email protected] * Corresponding author. M. Boujelbene address:
[email protected] E-mail E-mail address:
[email protected]
The cost of idle capacity is a fundamental information for companies and their management of extreme importance in modern production systems. In general, it is defined as unused capacity or production potential and can be measured 2351-9789© 2017 The Authors. Published by Elsevier B.V. 2351-9789© 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and in several ways: tons of production, available hours of manufacturing, etc. The management of the idle capacity 2351-9789© 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and 253 510 761; fax: +351 253 604 741 Design Engineering. * Paulo Afonso. Tel.: +351 Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and Design Engineering. E-mail address:
[email protected] Design Engineering.
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials Manufacturing and Design Engineering. 10.1016/j.promfg.2018.02.040
W. Mersni et al. / Procedia Manufacturing 20 (2018) 271–276 W. Mersni et al. / Procedia Manufacturing 00 (2017) 000–000
272 2
Many researchers have used the Taguchi method to optimize the machining parameters. In fact, Avinash (2013) [3] optimized the surface roughness while milling AISI 1040 MS material and he found that the most influencing parameter on surface roughness is the coolant flow. Hamdan et al. (2011) [4] applied the Taguchi method to optimize the high speed milling parameters of stainless steel using coated carbide tools and they found that the feed is the most influencing parameter on the surface roughness. Amal et al. (2015) [5] studied the machining parameters in end milling of Ti-6Al-4V and found that the feed rate is the most influencing parameter on surface roughness and on the cutting forces. Ashok Raj et al. (2013) [6] optimized the milling parameters of EN8 steel using the orthogonal array L9 of Taguchi and found the cutting speed is the most influencing parameter on the surface roughness. Boujelbene et al. [7] and Choubey et al. [8] optimized the milling parameters of mild steel and they found that the spindle speed has the biggest influence on the surface roughness and that the feed rate is the most influencing parameter on the material removal rate MRR. Bouzid, et al. [9-10] used the L25 orthogonal array to optimize the surface roughness in high speed milling of duplex steel and carbon steel. 2. Problem definition The aim of this work is to analyze the 3D average surface roughness Sa in ball end milling of Ti-6Al-4V with an inclined workpiece angle of 25° in relation with the cutting parameters using the Taguchi method. In this paper, three milling parameters were studied and optimized, which are the cutting speed Vc, the radial depth of cut ae and the feed per tooth fz. Each factor took 3 levels as shown in table 1. The levels of the cutting speed and the feed per tooth were selected based on the recommendations given by the tool manufacturer’s recommendation. Table 1. Factors and levels. Factors
Level 1
Level 2
Level 3
Vc (m/min)
50
100
150
fz (mm/tooth)
0.1
0.15
0.2
ae (mm)
0.3
0.5
0.7
3. Method of analysis 3.1. Experimental procedure The nine experiments were conducted under dry conditions on a Deckel Maho DMU 50 evolution 5-axis CNC milling machine with Siemens control 840D, a maximum spindle speed of 18000 rpm and a maximum power of 16kW (Fig. 1). The tool holder reference is R216F-16A16S-063 and the ball end mill is composed by two uncoated cemented tungsten carbide inserts (z=2) which are manufactured by Sandvik with the reference R216F-16 40 E-L P20A. The diameter of the tool is 16 mm and the axial depth of cut was 0.4 mm.
Fig. 1. Five-axis CNC milling machine
W. Mersni et al. / Procedia Manufacturing 20 (2018) 271–276 W. Mersni et al. / Procedia Manufacturing00 (2017) 000–000
273 3
The workpiece material used is the titanium alloy alpha-beta Ti-6Al-4V. The chemical composition and the physical proprieties of Ti-6Al-4V are shown in table 2 and 3. Table 2. Chemical composition of Ti-6Al-4V. Element
Ti
Al
V
Fe
O
C
N
H
%
balance
6
4
0.3
0.2
0.08
0.05
0.01
Table 3. Physical proprieties of Ti-6Al-4V. Hardness (HRC)
Density (g/cm³)
Modulus E (MPa)
Elongation (%)
Tensile strength (MPa)
Thermal conductivity (W/m.K)
36
4.43
910
0.7
1000
7.3
The workpiece inclination angle was 25° and the cutter orientation was in a single direction vertical upward orientation as shown in fig. 2.
Fig. 2. Vertical upward tool orientation
The average surface roughness Sa was measured using a 3D measurement station STIL Micromeasure 2 which is optimized for surface roughness measurement in the feed and pick feed directions. For each specimen, the surface roughness was measured 3 times and then the average was calculated. 3.2. Method of optimization The optimization method applied in this paper is the Taguchi method which is developed by Dr. Genichi Taguchi. It uses two major tools which are the orthogonal arrays OA and the signal to noise ratio S/N [11]. 3.2.1. Identification of the orthogonal Array OA The minimum number of experiments to be conducted was calculated as: [ L 1 F 1 3 1 3] 1 7 L9 (1) L is the number of levels for each factor and F is the number of factors. 9 experiments were conducted instead of 3³ = 27 experiments. 3.2.2. Signal to noise ratio S/N The signal to noise ratio is used by Taguchi as the quality characteristic of choice to analyze the data. The methods of calculation of S/N ratio are divided into three categories depending on the desired quality characteristics: Larger is the best characteristic:
1 n S/ N 10 log yi 2 (2) n i 1
W. Mersni et al. / Procedia Manufacturing 20 (2018) 271–276 W. Mersni et al. / Procedia Manufacturing 00 (2017) 000–000
274 4
Nominal and smaller are the best characteristics:
1 n S/ N 10 log yi 2 (3) n i 1 In this work, the equation (3) was applied to calculate S/N ratio of the 3D average roughness Sa based on the desired quality characteristic smaller is better. 4. Results and discussion In this work, the nine experimental results with their transformation into signal to noise ratios S/N for the average surface roughness Sa are represented in table 4. All the analysis based on the Taguchi method, was done with MINITAB 18 software. Table 4. Orthogonal Array L9 with the experimental results for surface roughness Sa. Experiments
Vc (m/min)
fz (mm/tooth)
ae (mm)
Sa (μm)
S/N (Sa)
1
50
0.1
0.3
1.45
-3.23
2
50
0.15
0.5
2.04
-6.19
3
50
0.2
0.7
2.56
-8.16
4
100
0.1
0.5
1.85
-5.34
5
100
0.15
0.7
2.35
-7.42
6
100
0.2
0.3
1.46
-3.29
7
150
0.1
0.7
2.19
-6.81
8
150
0.15
0.3
1.38
-2.80
9
150
0.2
0.5
1.92
-5.67
The Figure 3 shows the 3D Maps of the surface topography which give more details compared to the 2D surface roughness profiles. The figure 3.a presents the lowest anisotropy and the best arranged surface topography with the least roughness amplitude for a feed per tooth equal to 0.1 mm/tooth. From the Figure 3.b, we can conclude that a large value of feed (fz = 0.2 mm/tooth) produces higher surface roughness values which requires more polishing time and consequently higher machining costs.
(a) (b) Fig. 3. 3D surface topography feed per tooth; (a) fz = 0.10 mm/tooth and Sa = 1.48 µm, (b) fz = 0.20 mm/tooth and Sa = 1.64 µm.
The results of S/N ratio analysis and the means main effects of Sa in relation with the levels are represented in the table 5 and table 6.
W. Mersni et al. / Procedia Manufacturing 20 (2018) 271–276 W. Mersni et al. / Procedia Manufacturing00 (2017) 000–000
275 5
Table 5. Response for Signal to Noise Ratios (smaller is better) Level
S/N (Vc)
S/N (fz)
S/N (ae)
1
-17.58
-15.38
-9.32
2
-16.05
-16.41
-17.2
3
-15.28
-17.12
-22.39
Delta
2.3
1.74
13.07
Rank
2
3
1
Level
Sa (Vc)
Sa (fz)
Sa (ae)
1
2.02
1.83
1.43
2
1.89
1.92
1.94
3
1.83
1.98
2.37
Delta
0.19
0.15
0.94
Rank
2
3
1
Table 6. Response for Means Main Effects
It can be seen from the figure 4 that an increase in the feed per tooth or in the radial depth is accompanied with an increase in the average surface roughness Sa. However, when the cutting speed increases, the surface roughness decreases. The most influencing factor on surface roughness Sa is the radial depth ae followed by the cutting speed Vc and then the feed per tooth fz.
Fig 4. Main effects Plot for means Sa.
The desired quality characteristic called “smaller is better” means that the lowest value of surface roughness Sa is the best result required. Hence, the largest S/N ratio response would reflect the best response which results in the lowest noise. From the figure 5, the optimum levels of Factors selected to obtain the lowest value of the average surface roughness Sa are: Vc =150 m/min; fz = 0.1 mm/tooth; ae = 0.3 mm.
Fig 5. Signal to noise Ratios for surface roughness Sa.
276 6
W. Mersni et al. / Procedia Manufacturing 20 (2018) 271–276 W. Mersni et al. / Procedia Manufacturing 00 (2017) 000–000
The equation of the average surface roughness in relation with the three cutting parameters was obtained by the multiple linear regression method using Minitab 16 software (Eq. (4)). �� = 0.702 − 0.00187 �� + 1.50 �� + 2.34 � (4) The calculation of surface roughness using the equation (4) with the optimum levels found below gives a value of roughness Sa = 1.27 μm which is lower than all the experimental results found in table 4. 5. Confirmation test A last experiment was conducted using the optimum levels for the surface roughness Sa selected by S/N ratio analysis to confirm the efficiency of the results found by the Taguchi method. The surface roughness measurement gave a value of 1.24 μm which is very close to the result obtained by the equation (4). This result shows an improvement of 10.15% of surface roughness compared with the lowest value of Sa and an improvement of 51.56% compared with the highest value of roughness found during the nine experiments. 6. Conclusion The Taguchi method is an efficient method for optimizing the surface roughness with a small number of experiments. In this paper, the milling parameters were optimized using the Taguchi method for better surface finish in ball end milling of a Ti-6Al-4V inclined workpiece. A L9 orthogonal array is used with a total nine experiments, three factors with three different levels. The results were analyzed by the signal to noise analysis S/N. The most influencing factor on the surface roughness is the radial depth ae followed by the cutting speed Vc and then the feed per tooth fz . 7. References [1]
A. Daymi, M. Boujelbene, A. Ben Amara, E. Bayraktar, D. Katundi, Surface integrity in high speed end milling of titanium alloy Ti-6Al4V, Materials Science and Technology, 27 (2011) 387-394. [2] M. B. Mhamdi, M. Boujelbene, E. Bayraktar, A. Zghal, Surface integrity of Titanium alloy Ti-6Al-4V in ball end milling, Physics Procedia, 25 (2012) 355-362. [3] A. Thakre, Optimization of milling parameters for minimizing surface roughness using Taguchi’s Approach, International Journal of Emerging Technology and Advanced Engineering, 3/1 (2013) 226-230. [4] A. Hamdan, A. D. Sarhan, M. Hamdi, An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish, The International Journal of Advanced Manufacturing Technology, 58 (2011) 81-91. [5] S. T. Amal, V. Vidya, V. Abraham, Machining parameters optimization in end milling if Ti6Al4V using Taguchi method, International Journal of Research in Engineering and technology, 3 (2015) 31-40. [6] R. Ashok Raj, T. Parun, K. Sivaraj, T. T. M. Kannan, Optimization of milling parameters of EN8 using Taguchi methodology, International Journal of mechanical Engineering and Robotics Research, 2 (2013) 202-208. [7] M. Boujelbene, P. Abellard, E. Bayraktar, S. Torbaty, Study of the milling strategy on the tool life and the surface quality for knee prostheses. Journal of Achievements in Materials and Manufacturing Engineering, 31/2 (2008) 610-615. [8] A. Choubey, V. Chaturvedi, J. Vimal, Optimization of process parameters of CNC milling machine for mild steel using Taguchi design and Single to Noise ratio Analysis, International Journal of Engineering Research and Technology, 1 (2012) 1-12. [9] W. Bouzid, A. Zghal, L. Sai, Taguchi Method for design optimization of milled Surface Roughness, Materials Technology, 19:3 (2004) 159-162. [10] W. Bouzid Sai, M. Boujelbene, M. Ben Amar, M. Ncib, Influence of 3 and 5 axis finishing milling on surface characteristics, Materials Technology, 21/3 (2006) 169-173. [11] C.C. Tsao, Grey-Taguchi method to optimize the milling parameters of aluminum alloy, The International Journal of Advanced Manufacturing Technology, 40 (2009) 41-48.
