Experimental investigation and optimization of machining parameters on Wire EDM using O2 steel 1Selvam
M, Palani S, Harish K A and Shanmugan S
1Assistant
Professor, Department of Mechanical Engineering, Vel Tech Multitech, Avadi, Chennai-62 Professor, Department of Mechanical Engineering Vel Tech Multitech, Avadi, Chennai-62 4Assistant Professor, Department of Physics, Vel Tech Multitech, Avadi, Chennai-62
2,3Associate
Corresponding author e-mail:
[email protected] has been witnessed. New developments in the field of material science have led to new engineering metallic materials, composite materials and high tech ceramics having good mechanical properties and thermal characteristics as well as sufficient electrical conductivity so that they can readily be machined by spark erosion. Non-traditional machining has grown out of the need to machine these exotic materials. The machining processes are non-traditional in the sense that they do not employ traditional tools for metal removal and instead they directly use other forms of energy. The problems of high complexity in shape, size and higher demand for product accuracy and surface finish can be solved through non-traditional methods. Currently, non-traditional processes possess virtually unlimited capabilities except for volumetric material removal rates, for which great advances have been made in the past few years to increase the material removal rates. As removal rate increases, the cost effectiveness of operations also increase, stimulating ever greater uses of nontraditional process. Kapil Kumar et al (2012) there are many standard workholding devices such as jaw chucks, machinevises, drill chucks, collets, etc. which arewidely used inworkshops and are usuallykept in stock for general applications. Fixturesare normally designed for a definite operationto process a specific workpiece and aredesigned and manufactured individually [1].Huang et al.(1999) investigated experimentally the effect of various machining parameters on the gap width, SR and the depth of white layer on the machined workpiece (SKD11alloy steel) surface. They adopted the feasible direction non-linear programming method for determination of the optimal process settings [2]. Miller et al. (2004) investigated the effect of spark on-time duration and spark on-time ratio on the material removal rate (MRR) and surface integrity of four types of advanced material; porous metal foams, metal bond diamond grinding wheels, sintered Nd-Fe-B magnets and carbon– carbon bipolar plates. Regression analysis was applied to model the wire EDM MRR. Scanning electron microscopy (SEM) analysis was used to investigate effect of important EDM process parameters on surface finish [3].
ABSTRACT Wire electrical discharge machining process is a highly complex, time varying & stochastic process. The process output is affected by input variables. Therefore a suitable selection of input variables for the wire electrical discharge machining (WEDM) process relies heavily on the operator's technology & experience because of their numerous &diverse range. WEDM is extensively used in machining of conductive materials when precision is of prime importance. Rough cutting operation in wire EDM is treated as challenging one because improvement of more than one performance measures viz. Metal removal rate (MRR), surface finish & cutting width are sought to obtain precision work. Using Taguchi's parameter design, significant machining parameters affecting the performance measures are identified as pulse off time, peak current, pulse on time, flow rate and wire tension. The effect of each control factor on the performance measure is studied individually using the plots of signal to noise(S/N) ratio. The study demonstrates that the WEDM process parameters can be adjusted so as to achieve better metal removal rate, surface finish, wear ratio and dimensional deviation. Key Words: WEDM; precision; Metal removal rate; surface finish 1. Introduction The history of EDM Machining Techniques goes as far back as the 1770s when it was discovered by an English Scientist. However, Electrical Discharge Machining was not fully taken advantage of until 1943 when Russian scientists learned how the erosive effects of the technique could be controlled and used for machining purposes. When it was originally observed by Joseph Priestly in 1770, EDM Machining was very imprecise and riddled with failures. Commercially developed in the mid 1970s, wire EDM began to be a viable technique that helped shape the metal working industry we see today. In the mid 1980s.The EDM techniques were transferred to a machine tool. This migration made EDM more widely available and appealing over traditional machining processes. The new concept of manufacturing uses non-conventional energy sources like sound, light, mechanical, chemical, electrical, electrons and ions. With the industrial and technological growth, development of harder and difficult to machine materials, which find wide application in aerospace, nuclear engineering and other industries owing to their high strength to weight ratio, hardness and heat resistance qualities
Rajkamal Singh Banga et al (2014) found that various design and analysis methods in the context of to improve the life of fixture; different fixture geometries are compared experimentally and are selected. The proposed eccentric shaft fixture will fulfilled researcher Production target and enhanced the efficiency, fixture reduces operation time and increases productivity, high quality of operation. Decreases expenditure on quality control of machined parts as fixtures facilitate uniform
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quality in manufacturing, Widens the technology capacity of machine tools and increases the versatility of machining operations to be performed, either fully or partly automates the machine tool [4]. Sarkar et al. (2005) performed experimental investigation on single pass cutting of wire electrical discharge machining of γTiAl alloy. The process was successfully modelled using additive model. Both surface roughness as well as dimensional deviation was independent of the pulse off time. The process was optimized using constrained optimization and Pareto optimization algorithm [5].Sivakiran et al. (2012) studied the influence of various machining parameters Pulse on, Pulse off, Bed speed and Current on metal removal Rate (MRR). The relationship between control parameters and Output parameter (MRR) was developed by means of linear regression. Taguchi’s L16 (4*4) Orthogonal Array (OA) designs had been used on EN-31 tool steel to achieve maximum metal removal rate [6]. 3. Experimentation Work Materials OHNS - Oil Hardened Non Shrinking steel. The American Iron and Steel Institute (AISI) specification is 02. It is prehardened cold work pieces of die steel. It gives good results in hardening and produces small dimensional changes. 02 have good abrasion resistance and sufficient toughness. When properly annealed, 02 has a machinability rating of 90 when compared to 1% Carbon Steel rated at 100.When oil quenched from the proper hardening temperature, this grade can be expected to expand approximately .039 mm per mm. The Composition of work piece material OHNS steel grade 02 is tabulated in Table 1
The properties of work material are tabulated in Table 2. Figure 1 shows the experimental set up of wire EDM process. Figure 1: Experimental set up of wire EDM process Table 3: The specification of the machine tool
Table 1: Composition of work piece material OHNS steel grade 02
Table 2: properties of work material
Wire EDM uses demonized water as the dielectric compared to Vertical EDM's that use oil. The dielectric system includes the water reservoir, filtration system, deionization system, and water chiller unit. During cutting, the dirty water is drained into the unfiltered side of the dielectric reservoir where the water is then pumped and filtered through a paper filter and returned to the clean side of the dielectric tank.Following filtration, the clean water is measured for conductivity,
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and if required passes through a vessel that contains a mixed bed of anion and cation beads. This mixed bed resin (the ion exchange unit) controls the resistivity of the water to set values automatically. The clean water fills the clean side of the dielectric reservoir and proceeds to the cutting area.A water chiller is provided as standard equipment to keep the dielectric, work piece, worktable, control arms, and fixtures thermally stable. During the cutting process the chips from the material that is being eroded, gradually changes the water conductivity level. Resistivity levels of the water are set according to the cutting requirements of the work piece material being machined. 4. Taguchi method on WEDM
By this, the process parameters which influence the products are separated into two main groups: control factors and noise factors.Five important machining parameters were used as control factors and each parameter was designed for four levels and L16 orthogonal array was chosen for the experiments and the results are presented in above Table 4.
In this study the relationship between control factors & responses like Material Removal Rate, Surface finish and Dimensional deviations are established. The Taguchi method is used to formulate the experimental layout, to analyze the effect of each parameter on the machining characteristics, and to predict the optimal choice for input. Various important input combinations as formulated by Taguchi, is worked out in a wire electric discharge machine. Calculated results and lab results are tabulated as shown below, so that the best output's can be found. L16 Orthogonal array (313levels
Design of experiments Design of experiments is a powerful analysis tool for modeling and analyzing the influence of process variables over some specific variable, which is an unknown function of these process variables. The most important stages in the design of experiment lie in the selection of the control factors. Combining the experiment design theory and the quality loss function concept proposed by Taguchi in the 1960s is widely used to solve and improve industrial product quality and reliability. The experimental layout for the machining parameters using the L16 orthogonal array was used in this study. This array consists of five control parameters and four levels. An orthogonal array gives a more reliable estimate of the factor effects with fewer tests compared to traditional methods. The process parameters along with their values at levels are given in Table 5.
4 Parameter and 4
The Taguchi L16 orthogonal array of five factors, four levels are shown in the tabular column.The number of degrees of freedom was calculated from the number of parameters identified and their number of levels of variation. Using the full factorial design (4x4x4x4) reduced a total of 1024 sets of experiments to 16, thereby decreasing the cost, and effort. The array along with the factor assigned to the columns is presented in table, where L16 trails considered for the test.In the Taguchi method, most all of the observed values are calculated based on 'the higher the better' and 'the smaller the better'. Thus in this study, the observed values of material removal rate, surface roughness, were set to maximum, intermediate and minimum respectively.In this study, Taguchi method, a powerful tool for parameter design of performance characteristics was used to determine optimal machining parameters for minimum surface roughness and maximum MRR in WEDM.
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Formula used Changes in the electrode weight, material weight and elapsed time were recorded after each machining test. The MRR and the WWR were evaluated for each cutting condition by measuring the average amount of material removed and the required cutting time.
The relative importance of the machining parameters with respect to the MRR and the surface finish was investigated to determine more accurately the optimum combinations of the machining parameters by using ANOVA. Statistically, Ftest provides a decision at some confidence level as to whether these estimates are significantly different. Larger F-value indicates that the variation of the process parameter makes a big change on the performance characteristics. F-values of the machining parameters are compared with the appropriate confidence table. The results of ANOVA for the machining outputs are presented in Table 8 and Table.9.
5. Results and Discussion Higher on- time means more number of sparks can be issued persisted, it will be diluted and the rate of material removal rate will reduce. Roughness decreases with rise in on —time as intensity of spark gets progressively reduced, penetration capacity is reduced and the roughness decreases.Off time increase initially increase MRR as charging helps in quality sparking in intensity. Further rise in offtime reduces MRR than original as effective number of spark gets reduced in pulse duration due to increased Off-time.The roughness initially decreases which was positive for the experiment, but subsequently roughness increases due to heating effect.
Percent contribution indicates the relative power of a factor to reduce variation. For a factor with a high percent contribution, a small variation will have a great influence on the performance. According tothis analysis, among the input parameters selected, statistically the most effective parameter with respect to MRR pulse off time(71.97%) followed by wire speed (14.74%). Supply voltage (12.86%) and pulse on time (0.43%) were less and least significant respectively.
Wire tension helps increase MRR. A stiff wire vibrates more. Hence MRR and roughness will increase for this condition. Wire tension if increases the roughness value increases. Sparks issued are not uniformly scattered around. Hence MRR increases Ra increases. Higher wire speed initially increases roughness but at higher levels it reduces the roughness values.Voltage increase increases MRR slightly for the range selected.As voltage rises the roughness value rises rapidly, keeps an almost straight line- slope relationship. Since rise in voltage does not remove material efficiently, (or) slow, the excess energy of potential in voltage helps to increase heat and this worsens surface finish.
From Table 9, for the surface finish, pulse on time was found to be the major factor affecting the surface finishes (38.16%) whereas supply voltage is significant second ranking factor (24.62%), followed by pulse off time (20.49%).The Wire speed is the least significant
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(16.72%).
by33.71%.The experimental results confirmed the validity of the used Taguchi method for enhancing the machining performance and optimizing the machining parameters (MRR and surface finish). Regression analysis The regression analysis was carried out and the equation obtained to predict MRR is given in Eq. 1 and the roughness in Eq. 2
Confirmation experiment
MRR=0.780 + 0.0046 A-0.0588 B - 0.0274 C - 0.0278D (1)
The confirmation experiment is the final step of the Taguchi's design of experiment process after selecting the optimal parameters. The purpose of the confirmation experiment is to validate the conclusions drawn during the analysis phase. In this study, after determining the optimum conditions and predicting the response under these conditions, a new experiment was designed and conducted with the optimum levels of the machining parameters. The final step is to predict and verify the improvement of the performance characteristic.
With, S = 0.0310947 R-Sq = 97.64% R-Sq(adj) = 88.22% Ra=3.63 + 0.080 A - 0.065 B - 0.127 C - 0.012 D (2) With S = 0.0628369 R-Sq = 78.2% R-Sq(adj) = 73.0% Wire EDM is a stochastic process with Rsq. are sufficient value the above two equations can be used to predict output without actually machining it.Regression analyses for MRR in graphspresented in Figure 2. Figure 2:Normal probability plot, randomness, experimental run and histogram for MRR The normal probability plot obtained shows all the points are close to the straight line fit. This indicates close agreement with fit. The deviations are very small from the straight line. The scatter plot indicates randomness in nature. This implies the readings are not biased or one sided distribution. The sampling output was evenly distributed. The histogram must be similar and bell shaped. Careful observation shows it approximately normally distributed. The experimental runs with random experimental order while conducting experiments may yield this the graph of MRR versus experimental run (16), show experiment14 has lowest MRR and experiment 16 has highest MRR.
Improvement in MRR =25.11%
Improvement in surface finish = 33.71%
The comparison made the predicted MRR with theactual MRR is using optimal machining parameters. The MRR is increasedby 25.11%.So; MRR is improved by using this approach. Table 9 shows the comparison of the predicted surface finish with the actual by use of the optimal machining parameters. The surface roughness is decreased
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Figure 3: Normal probability plot, randomness, experimental run and histogram for Ra
machining parameters identified for higher wear ratio are TON13111S, TOFF 53gs. wire speed 4mm/min,supply voltage 19v.
From the plot of MRR and roughness with the experimental run, it is seen that they are opposite in nature, when MRR is maximum roughness is minimum and vice versa. The distribution also indicates the fluctuating distribution. We do see the experimental run drops output after a series of upward trend, followed by a downward trend. It implies the observations are widely distributed and are valid.
References
6. Conclusions The effect of various machining parameter such as pulse on time, pulse off time, wire feed, flow rate and peak current on the quality of machining has been studied through the machining of OHNS-02 Steel. The level of importance of the machining parameters on the MRR, surface finish, wire wear ratio and dimensional deviation are determinedby using S/N Ratio. Responses in each output parameter with respect to changes made in input. MRR Increase in pulse on time (TON) value initially decreases the MRR, but if TON is increased further, MRR increases gradually to a higher level.MRR becomes very unstable and remains low for most of the TOFF(pulse off) values.Increase the peak current increases the MRR to a certain peak level. The MRR increases as the dielectric fluid is flushed through thepark gap.The material removal rate decreases with increase in wire tension.The optimal machining parameters identified for high MRRare TON122gs, TOFF43gs,wire speed lmm/min,supply voltage 10v. Surface roughness slightly increases with increase in TONand TOFF. The surface roughness value shows a wavy nature with increase in peak current. The surface roughness decreases with a wavy pattern withincreasing the dielectric fluid pressure. The surface roughness first increases with the increase in wire tension and gradually decreasesThe optimal machining parameters identified for lesser surfaceroughness are TON 122tis, TOFF 43tis, wire speed lmm/min, supplyvoltage 10v. Wear ratio increases with increase in TON and TOFF.With increase in wire tension and peak current, the wear ratioshows an irregular pattern. In both these relations, there are peaksas well as lows. Wear ratio decreases with increase in flow rate of dielectric fluid. The optimal machining parameters identified for higher wear ratioare TON 12811S, TOFF 50tts, wire speed 3mm/min, supply voltage16v. Dimensional deviation is the output parameter which showed leastresponse to the varying inputs.Varying TOFF and flow rate recorded a minimum dimensionaldeviation of 0.07mm. The optimal
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[1] Kapil Kumar & Sanjay Agarwa, “Multi-objective parametric optimization on machining with wire electric discharge machining,” Int J Adv Manuf Technol, vol. 62, pp.617–633, 2012 [2] Huang, J.T., Liao, Y.S., Hsue, W.J., Determination of finish-cutting operation number and machining parameter setting in wire electrical discharge machining,” Journal of Materials Processing Technology, vol. 87, pp. 69–81, 1999. [3] Miller, S. F., Shih, A. J., Qu, J., “Investigation of the spark cycle on material removal rate in wire electrical discharge machining of advanced materials,” International Journal of Machine Tools & Manufacture, 44, pp.391–400, 2004. [4] Rajkamal Singh Banga, Mukesh Verma, “Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Analytic Hierarchy Process,” Int. Journal of Engineering Research and Applications, vol. 4(10), pp. 83-89, 2014 [5] Sarkar, S., Mitra, S., Bhattacharyya, B.. “Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy,” Journal of Materials Processing Technology, 159: pp. 286–294, 2005 [6] Sivakiran S, Bhaskar Reddy and Eswara Reddy C, “Effect Of Process Parameters on MRR In Wire Electrical Discharge Machining Of En31 Steel,” International Journal of Engineering Research and Applications (IJERA), vol. 2 (6), pp. 12211226, 2012
