Available online at http://www.urpjournals.com Advanced Engineering and Applied Sciences: An International Journal Universal Research Publications. All rights reserved ISSN 2320 3927 Original Article Finding Influence Parameters for Metal Removal Rate and Tool Removal Rate during Electro Discharge Machining of EN-31 using Taguchi Method Anil Kumar, Dr.visvesvaran pandurangan, Anil Dhanola, Sunil Faculty of Mechanical Engineering, MVN University, Palwal, Haryana, India Akumar1212.1989@gmail.com,dr.v.pandurangan@mvn.edu.in,anildhanola3698@gmail.com,sunil.tanwar@mvn.edu.in Received 30 May 2016; accepted 24 Novemebr 2016 Abstract The present study analyzed the relation of process parameters in electro-discharge of EN-31alloy steel with novel tool electrode material such as brass and copper, Taguchi method was employed with the help of Minitab-17 software for developing experimental models. Analysis on machining distinctive of electrical discharge machining (EDM) die sinking was made on the formulated models. In this study, Peak Current (I p) amp, Spark on time (T on) µs. Duty cycle (τ), Two Electrode (copper and brass) are considered as in put process parameters. The process performances such as material removal rate (MRR) and tool wear rate (TWR) were standardize Analysis of variance test had also been carried out to check the adequacy of the formulated regression models. The observed optimal process parameter settings based on composite desirability. The optimized factor for MRR is Tool is Copper, Duty cycle (τ) = 9, Spark on time (T on) = 500µs, Peak current =10 amp and For TWR the optimized factors are Tool is Brass, Peak current = 10 amp. Spark on time (T on) = 500 µs, duty cycle (τ) =9 achieving maximum MRR and minimum TWR. 2016 Universal Research Publications. All rights reserved 1. Introduction Electric discharge machining (EDM) is a non- customary machining operating procedure in the manufacture of complex shaped dies, moulds and scathing parts used in automobile, aerospace, surgical and other industrial applications [1] Electrical-discharge machining (EDM) is extensively used for high strength materials. A major advantage of EDM is that the tool and the work piece do not come into contact [2] in this process material is removed by controlled erosion through a series of electric sparks between the tool (electrode) and the work piece. The thermal energy of the sparks leads to intense heat conditions on the work piece causing melting and vaporizing of work piece material. Due to the high temperature of the sparks, not only work material is melted and vaporized, but the electrode material is also melted and vaporized, which is known as electrode wear (EW). The EW process is quite similar to the material removal mechanism as the electrode and the work piece are considered as a set of electrodes in EDM. Due to this wear, electrodes lose their dimensions resulting in inaccuracy of the cavities formed. During EDM, the main output parameters are the material removal rate (MRR), Tool wear ratio (TWR) [3] EN-31 is the most preferred material for forging components consequently; an analysis on the influence of current and pulse duration and duty cycle over MRR and TWR was performed. EDM is now unquestionably recognized as an important precision machine tool forming process for producing internal shapes on work piece, this study present experimental analysis based on four factors and two levels (2 3 ) Orthogonal Array L 8 design. The objective of this research is to study the performance of different electrode materials on EN-31 work piece with EDM process. Studies show that selection of process parameters and fixing the appropriate range of parameters to machine every product decides the quality of the product and in turn the processes requirements [1]. A. A. Khan et.al [3] reported that aluminum and mild steel shows more material removal with increase in current, the highest MRR was obtained during machining of aluminum using a brass electrode, EW increases with increase in current and voltage. Wear of copper electrodes is less than that of brass electrodes. Ali Ozgedik & Can Cogun et.al [4] the variations of edge and front wear characteristics as well as machining performance outputs like work piece removal rate, tool wear rate, relative wear and work piece surface roughness, are effected by various dielectric flushing methods, discharge current and pulse duration settings. K. 58
D. Chattopadhyay & P. S. Satsangi & S. Verma & P. C. Sharma et.al [5] Rotary electrical discharge machining in induced magnetic field produced higher material removal rate and decreases electrode wear rate as compared with machining in a nonmagnetic field and Reduction in EWR, helps in achieving higher geometric trueness, resulting in better dimensional control on the work piece. Jong Hyuk Jung and Won Tae Kwon et.al [6] optimal machining conditions for drilling of a micro-hole of minimum diameter and maximum aspect ratio It was found that the electrode wear and the entrance and exit clearances have a significant effect on the diameter of the micro-hole when the diameter of the electrode is identical. To determine the machining parameters affecting the electrode wear and the entrance and exit clearances, Grey relational analysis was used. The input voltage and the capacitance were found to be the most significant controlling parameters. H. Yan, C. C. Wang [7] work optimizes the cutting of Al 2O 3/6061 Al composite using rotary electro-discharging machining (EDM) with a disk-like electrode by using Taguchi methodology. The Taguchi method is used to formulate the experimental layout, to analyze the effect of each EDM parameter on the machining characteristics, and to predict the optimal choice for each EDM parameter. This work evaluates the feasibility of machining Al 2O 3/6061 Al composite by rotary EDM with a disk-like electrode. Based on the results presented herein, we can conclude the following: 1. the machining process of the Al 2O 3/6061Al composite by rotary EDM with a disk-like electrode is feasible in comparison with other machining process. 2. Rotary EDM with a disk-like electrode is shown from the observed results to reach a higher MRR although the EWR is higher. The overal l advantage still makes this revised technology an acceptable tool. 2.1. Material 2.1.1. Tool Material In such sensitive area of research, the electrode used needs lot of attention. In lot of our analysis, material reduction rate plays an important role. In the experiment, weighing machine having 300 g capacity with a precision of 10 mg has been used. Materials used are copper and brass (figure- 1) and their composition and physical properties are shown in thetable-1 and Table-2 respectively. Table 1.Chemical composition of Electrode materials [8]. Composition In % Copper Brass Copper 99.750 56.700 Aluminum 0.040 0.025 Tin 0.030 0.020 Phosphorous 0.030 0.020 Lead 0.009 3.000 Iron 0.015 0.100 Zinc 0.060 39.850 Nickel 0.010 0.077 Fig.1. Shows Brass,Cupper Electrode(Φ 15mm). 2. Experimental Procedure Table 2.Major Properties of Electrode Materials [3]. Electrode Thermal conductivity Melting point Electrical resistivity Specific heat capacity materials (W/m- K) ( C) (ohm-cm) (J/g- C) Copper 391 1,083 1.69 0.385 Brass 159 990 4.70 0.380 2.1.2. Work Material The chemical composition of the work material EN-31 is shown in Table-3 and its hardness is 263 BHN and figure-2 shows that the work piece before and after the machining. Fig.2. Work Piece Before and After Machining. Table 3.Composition of EN-31[11]. COMPOSITION PERCENTAGE (wt. %) Carbon 1.08% Silicon 0.25% Manganese 0.53% Nickel 0.33% Chromium 1.46% Molybdenum 0.06% Sulphur 0.015% Phosphorus 0.022% 2.2. Die-Sinking EDM Machine The equipment used to improvise the experiments was a die-sinking EDM machine of type SE-35 Electra plus 500 x 300 shown in figure-3. A jet flushing system in order to assure the capable flushing of the EDM process debris from the gap zone is employed. Pressure of the dielectric fluid is focused manually at the beginning of the experiment. The dielectric fluid used for the EDM machine was EDM Oil- 59
30, which is accessible dielectric fluid. Polarity of the electrode is negative and that of the work piece is positive. The process parameters chosen for the experiments are: a) pulse-on time (ton), b) Peak current (Ip), c) Duty factor (τ), d) Two electrodes (Copper, brass). While the response functions are: a) Tool wear rate (TWR) b) Material removal rate (MRR). Table 4. Level values of input Factors Control Factors I Level II Level Peak Current(Ip), amp 5 10 Electrode Copper Brass Spark on time (Ton) µsec. 500 1000 Duty cycle(t) 9 10 The other process parameters - voltage and flushing pressure of the electrolyte have been kept constant throughout the experiments. According to the capacity of the commercial EDM machine available and general recommendations of machining conditions for EN-31 the range and the number of levels of the parameters selected are as given intable-4. A Taguchi design or an orthogonal array the method is designing the experimental process using different types of design like, two, three, four, five, and mixed level. In the study, a four factor two level setup is chosen with a total of eight numbers of experiments to be Fig.3. Electronica SE-35 EDM machine. conducted and hence the OA L 8 was chosen. This design would enable the factor interactions to be evaluated. As a 2.3. Taguchi Method few more factors are tobe added for further research with Traditional experimentation involves one-factor-at-a-time. the same type of material, it was decided to utilize the L 8 The major limitation of this scheme is that it fails to setup, which in turn would reduce the number of correlate any possible interactions among the parameters experiments at the later stage [12]. leading to the observations whereas in Taguchi method, 2.4. Analysis of Variance (ANOVA) one variable is changed while the rest are held constant. It As earlier mentioned, the Taguchi DOE method replaces is a well-known fact that this method is impressive to deal the full factorial experiments with only a simple orthogonal with responses reputation by multi-variables. This method array of eight trials. To determine the significant factors, is not only effectual design of experiments tool, which and optimum combination of factors, an analysis of brings in a configured approach to translate best machining variance (ANOVA) has been utilized in order to offer a parameters compared to the conventional approach to measure of confidence by determining and analyzing data experimentation but also minimizes drastically the number variance. In the ANOVA, the total variation (ST), the sum of experiments that are necessary to model the response of squares of each factor and the percentage contribution functions. With these reasons, this method has been (%) were computed, respectively [13]. adopted during the entire analysis. 2.5. Evaluation of MRR The master effect is the average value of the The material MRR is expressed as the ratio of the material response function at a particular level of a parameter. The removed to the machining time for a particular material effect of a factor level is the deviation it effectuates from size and mass [10]. the overall mean response. This method is devised for MRR= Wjb Wja Whereas Wjb = Weight of work piece process optimization and identification of optimal t collection of factors for given responses. The steps before machining, Wja = Weight of work piece after involved are: machining.t = Machining time. a) Introduce the response functions and the process 2.6. Evaluation of TWR parameters to be evaluated. TWR is expressed as the ratio of the loss of tool weight to b) Determine number of levels for the process parameters the machining time for a particular material size and mass. and possible reciprocation between them. That can be explain this equations [10]. c) Select the apropos orthogonal array and assign the TWR = Wtb Wta Whereas Wtb = Weight of the tool t process parameters to the orthogonal array and manage before machining, Wta = Weight of the tool after the experiments accordingly. machining. t = Machining time d) Analyze the experimental results and select the optimum 3. Results and Discussion level of process parameters. Experiments are performed arbitrarily, according to the L 8 Verify the optimal parameters through a crosscheck orthogonal array as discussed in the section 2.3, on EN-31. experiment. 60
For each experiment, different electrodes have been used. The machining time conveniently fixed 15 minutes for all experiments. MRR & TWR are calculated with a precision of 10mg. The observations for the calculation of TWR, MRR based on L 8 orthogonal array are shown in Table-5. Table 5. Shown Experiment Table and observations S.NO CURREN SPARK ON TOOL DUTY CYCLE. T TIME MATERIAL Wjb Wja Wtb Wta 1 9 5 500 Copper 292.83 292.48 48.25 48.22 2 9 5 1000 Brass 285.69 285.42 42.49 42.39 3 9 10 500 Brass 285.42 284.76 42.39 42.06 4 9 10 1000 Copper 292.48 291.76 48.28 48.17 5 10 5 500 Brass 291.76 291.39 42.06 41.89 6 10 5 1000 Copper 284.76 284.56 48.23 48.15 7 10 10 500 Copper 284.56 283.61 48.26 48.13 8 10 10 1000 Brass 291.18 290.81 41.88 41.62 MRR, TWR, S/N values corresponding to MRR & TWR are tabled below (Table-6). Table 6. Shown MRR, TWR and SN Ratio S.NO. MRR TWR SN MRR SN TWR 1 0.0175 0.0015-35.1392 56.4782 2 0.0135 0.0050-37.3933 46.0206 3 0.0330 0.0165-29.6297 35.6503 4 0.0360 0.0025-28.8739 52.0412 5 0.0185 0.0085-34.6566 41.4116 6 0.0100 0.0010-40.0000 60.0000 7 0.0475 0.0010-26.4661 60.0000 8 0.0185 0.0130-34.6566 37.7211 3.1. Main Effect Plot for MRR The respective graphs for all the parameters and levels are recorded as shown below Fig.4. Shows Main Effect Plot for MRR. 3.2. Effect of Input Factors on MRR The response table for signal to noise ratio for MRR is shown in Table-7 whereas corresponding analysis variances (ANOVA) is shown in Table-8.Delta is obtained by this formula and rank of the factors are decided on the value of delta and accordingly they are ordered. Table 7. Response table for signal-to- noise ratio for MRR LEVEL DUTY CYCLE CURRENT SPARK ON TIME TOOL MATERIAL 1-32.76-36.80-31.47-34.08 2-33.94-29.91-35.23-32.62 DELTA 1.19 6.89 3.76 1.46 RANK 4 1 2 3 61
For the material and the experimentation methods adopted along with the selected parameters under consideration, it is clear that current followed by spark on time, tool material and duty cycle are contributing for influencing the value of MRR. It is also important to have quantitative effect of each parameters so that any necessity for the improvement of the experimental conditions may be proposed. With this intention, the ANOVA table-8 is generated and the data are tabulated as shown below. Table 8. Analysis of Variance for MRR SOURCE DF Seq SS Adj SS Adj MS F P % CONTRIBUTION Duty Cycle 1 2.812 2.812 2.812 0.47 0.544 1.894 Current (Amp.) 1 94.963 94.963 94.963 15.70 0.029 63.969 Spark On Time (Ton.) 1 28.246 28.246 28.246 4.67 0.119 19.027 Tool Material 1 4.288 4.288 4.288 0.71 0.462 2.888 Residual Error 3 18.141 18.141 6.047 ----- ----- 12.220 Total 7 148.450 ---- ---- ----- ----- In the table, it is noticed that factor peak current (Ip) has largest contribution as predicted which 63.96% is. The contribution of other noticeable factor ie., spark-on time (T on) may not also be ignored. The larger the contribution of any factor to the total sum of squares, the larger is the ability of that factor to influence material removal rate (MRR). The effect of tool material and duty cycle is about 3 and 2% respectively which are not only minimal but also may be due to experimental error and the limitations of the statistical analysis. Any suggestions to improve these parameters may not be very effective for the conditions adopted throughout the experimentation. The other uncontrollable parameter which is residual error calculated statistically by this method is about 12% which cannot be ignored. So focus on peak current (Ip) which has prominent and effective contribution on MRR need to be accelerated along with spark on time (T on) which has also shown noticeable influence. At the same time, it is understandable; Ip and T on are inter dependable parameters. By increasing T on will automatically increase the effect of current effectively. Probably this may reduce the residual error and improve the experimental conditions. The other uncontrollable parameter that is the gap between the work piece and the tool material need also be kept in mind. Nevertheless, the proficient worker sets the optimum gap through his experience. 3.3 Main Effect Plot for TWR The respective graphs for all the parameters and levels are recorded as shown below. Fig.5. Shows Main Effect Plot For TWR. 3.4. Effect of Input Factors on TWR The response table for signal to noise ratio for TWR is shown in Table-9 whereas corresponding analysis variances (ANOVA) is shown in Table-10. Delta is obtained by this formula and rank of the factors are decided on the value of delta and accordingly they are ordered. 62
Table 9. Response table for signal-to- noise ratio for TWR LEVEL DUTY SPARK TOOL CURRENT CYCLE ON TIME MATERIAL 1 47.55 50.98 48.39 40.20 2 49.78 46.35 48.95 57.13 DELTA 2.24 4.62 0.56 16.93 RANK 3 2 4 1 For the material and the experimentation methods adopted along with the selected parameters under consideration, it is clear that tool material followed by current, duty cycle and spark on time are contributing for influencing the value of TWR. It is also important to have quantitative effect of each parameter so that any necessity for the improvement of the experimental conditions may be proposed. With this intention, the ANOVA table-10 is generated and the data are tabulated as shown below. Table 10. Analysis of Variance for TWR SOURCE D F Seq SS Adj SS Adj MS F P % Contri- bution Duty Cycle 1 9.996 9.996 9.996 0.58 0.501 1.473 Current (Amp.) 1 42.771 42.771 42.771 2.49 0.213 6.306 Spark On Time 1 0.629 0.629 0.629 0.04 0.861 0.092 (Ton.) Tool Material 1 573.177 573.177 573.177 33.33 0.010 84.519 Residual Error 3 51.591 51.591 17.197 ----- ----- 7.607 Total 7 678.163 ---- ---- ----- ----- In the table, it is noticed that factor tool material has largest contribution as predicted which 84.52% is. The contribution of other noticeable factor ie. Current may not also be ignored. The larger the contribution of any factor to the total sum of squares, the larger is the ability of that factor to influence tool wear rate (TWR). The effect of spark on time and duty cycle is about 0.092 and 1.47 % respectively which are not only minimal but also may be due to experimental error and the limitations of the statistical analysis. Any suggestions to improve these parameters may not be very effective for the conditions adopted throughout the experimentation. The other uncontrollable parameter which is residual error calculated statistically by this method is about 7.60% which cannot be ignored.so focus on tool materials which have prominent and effective contribution on TWR need to be accelerated along with current which has also shown noticeable influence. The other uncontrollable parameter that is the gap between the work piece and the tool material need also be kept in mind. Nevertheless, the proficient worker sets the optimum spark gap through his experience. 4. Conclusion The present work depicts the use of Taguchi method to come out optimal machining parameter. Machining parameters such as peak current (Ip), material, duty cycle (τ) and pulse on time (T on) are optimized to intersect the objective. As a result of the study the following salient points are drawn The study reveals that the primary factor affecting the MRR is peak current subsequently followed by material, pulse on time and duty cycle. The effect of duty cycle and tool material is not contributing effect on MRR, may be ignored. The essential factor affecting TWR is predominantly by material. Noticeably by peak current. However affecting peak current may influence the MRR value which is not to be encouraged. The other factors do not really show any correlation with TWR. a) The optimal factor for MRR is copper as for as material is concerned. The corresponding values: duty cycle (τ) =9, pulse on time=500μs, peak current=10amp. b) Out of all the parameters the effect of material on TWR is prominently visible. Out of copper and brass, brass seems to be better choice and the corresponding other optimal factors: peak current=10amp, pulse on time (T on) =500 μsecond & duty cycle (τ) =9 c) Taguchi parameter design is effectively utilized to obtain optimum condition with lowest cost, minimal number of experiments. Refrences 1. 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