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Statistical analysis on electrode wear in EDM of tool steel DIN 1.2714 used in forging dies

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(BQ) Electrical discharge machining (EDM) is a widespread process which works very effectively in machining of difficult-to-cut materials and alloys in die and aerospace industries with high dimensional accuracies. However, this capability could be deteriorated due to electrode wear leading to decrease of process productivity. In this study, the effect of machining parameters of EDM process including on-time, current, voltage, the engaging time between workpiece and electrode, and pre-EDM roughing on electrode wear were experimentally investigated.

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Nội dung Text: Statistical analysis on electrode wear in EDM of tool steel DIN 1.2714 used in forging dies

Journal of Materials Processing Technology 187–188 (2007) 711–714<br /> <br /> Statistical analysis on electrode wear in EDM of tool steel<br /> DIN 1.2714 used in forging dies<br /> H. Zarepour a,∗ , A. Fadaei Tehrani b , D. Karimi c , S. Amini a<br /> a<br /> <br /> Department of Manufacturing Engineering, School of Engineering (2),<br /> Islamic Azad University, Najafabad Branch, Isfahan, Iran<br /> b Department of Mechanical Engineering, Isfahan University of Techology, Isfahan, Iran<br /> c Department of Mechanical Engineering, Tarbiat Modarres University, Tehran, Iran<br /> <br /> Abstract<br /> Electrical discharge machining (EDM) is a widespread process which works very effectively in machining of difficult-to-cut materials and alloys<br /> in die and aerospace industries with high dimensional accuracies. However, this capability could be deteriorated due to electrode wear leading to<br /> decrease of process productivity. In this study, the effect of machining parameters of EDM process including on-time, current, voltage, the engaging<br /> time between workpiece and electrode, and pre-EDM roughing on electrode wear were experimentally investigated. Main effects of factors and<br /> interactions were considered in this paper and regression equation was derived. A L50 (21 × 511 ) Taguchi’s standard orthogonal array was employed<br /> as experimental design. Copper was used as electrode to machine the hot work tool steel 1.2714, which is widely used to make forging dies and<br /> mandrels.<br /> © 2007 Elsevier B.V. All rights reserved.<br /> Keywords: EDM; Electrode wear; Orthogonal array; Statistical analyses<br /> <br /> 1. Introduction<br /> Among the non-traditional methods of material removal processes, electrical discharge machining (EDM) has drawn a great<br /> a deal of researchers’ attention because of its broad industrial<br /> applications. This process is well suited for machining of casting<br /> and forging dies, powder metallurgy and injection molds, and<br /> aerospace parts.<br /> The process is a spark erosion method, eroding the workpiece by high frequency spark discharges [1]. EDM has a high<br /> capability of machining the accurate cavities of dies and molds.<br /> Nevertheless, electrode wear occurs during EDM process leading to a lack of machining accuracy in the geometry of workpiece<br /> [2]. Furthermore, electrode wear imposes high costs on manufacturers to substitute the eroded complicated electrodes by<br /> new ones for die making. In order to increase the machining<br /> efficiency, erosion of the workpiece must be maximized and<br /> that of the electrode minimized in EDM process [1]. Therefore,<br /> <br /> ∗<br /> <br /> Corresponding author. Tel.: +98 312 37 58930; fax: +98 312 37 56900.<br /> E-mail address: h-zare@iaun.ac.ir (H. Zarepour).<br /> <br /> 0924-0136/$ – see front matter © 2007 Elsevier B.V. All rights reserved.<br /> doi:10.1016/j.jmatprotec.2006.11.202<br /> <br /> studying the electrode wear and related significant factors would<br /> be effective to enhance the machining productivity and process<br /> reliability.<br /> A series of investigations have been conducted on electrode<br /> wear in EDM process. Soni and Chakraverti [3,4] studied the surface quality, material removal rate, wear ratio, and dimensional<br /> accuracy in EDM of alloy steels. Singh et al. investigated the<br /> effect of machining parameters on electrode wear in die-sinking<br /> EDM of En-31 tool steel with different electrode materials [5].<br /> Also, Luis et al. have carried out a study on electrode wear<br /> in EDM of silicon carbide using the technique of design of<br /> experiments [6].<br /> In this research, an experimental study is conducted to investigate on electrode wear in die-sinking EDM of DIN 1.2714 hot<br /> work tool steel. The selection of this material was made taking into account its wide range of applications in die and mold<br /> industries such as hammer and hydraulic forging dies. The aim<br /> of this study is to find out from which process parameters (factors) electrode wear is affected and to which factor interactions it<br /> is related. This is usually done by means of analysis of variance<br /> (ANOVA). Furthermore, regression analysis is used to establish the correlation between factors and response (tool wear).<br /> The appropriate degree of the polynomial regression equation is<br /> <br /> 712<br /> <br /> H. Zarepour et al. / Journal of Materials Processing Technology 187–188 (2007) 711–714<br /> <br /> found which is thought to be useful assessment of the predictive<br /> equation [7,8]. Finally, the optimal factor levels are obtained.<br /> 2. Experimentation<br /> 2.1. Experimental design<br /> During EDM experiments, the input parameters (factors)<br /> were on-time (pulse-on duration), peak current, pulsed voltage,<br /> engaging time, and pre-EDM roughing accuracy. The pulse-off<br /> duration was kept constant which could effectively control the<br /> flushing of the debris from the gap, giving machining stability. Therefore, the effect of the pulse-off duration on machining<br /> process was not considered in the present work. Table 1 shows<br /> factors and factor levels assessed in this study.<br /> The design of experiments was performed through Taguchi<br /> approach of experimental design. Accordingly, a L50 standard<br /> orthogonal array was employed. All the main effects of factors<br /> and two-order interactions were desired to be considered in this<br /> study.<br /> 2.2. Experimental equipment and setup<br /> Fifty DIN 1.2714 steel specimens were machined for 50<br /> experimental runs in this work. The holes were created in the<br /> workpieces in five groups of dimensions as roughed cavities<br /> before EDM process. The allowances left for EDM die-sinking<br /> were considered equal to 0.25, 0.5, 0.75, 1, and 1.25 mm (preEDM rouging accuracy) for each group of dimensions. These<br /> dimensions are referred as 1, 2, 3, 4, and 5, respectively, in<br /> Table 1 and analyses. Hardness of the specimens was increased<br /> up to 48–50 HRC, nearly the same as the hardness of forging<br /> dies.<br /> Cylindrical copper rods with 99% purity and 8.98 g/cm3<br /> density were machined with good surface finish and exact<br /> dimensions as tool electrodes. Fig. 1 illustrates a sample of copper electrodes. Both electrodes and workpieces were deburred<br /> carefully providing stable conditions in EDM process. The specimens were machined on a “Pishtazan-Pulse Generator 120”<br /> die-sinking EDM machine which has an iso-pulse generator with<br /> a maximum 120 A current intensity.<br /> The experiments were carried out in petroleum-Behran oil 42<br /> dielectric (10:1, v/v) covering the workpiece by 10 mm. Final<br /> dimensions of machined holes in workpieces were exactly the<br /> same for all experimental runs. The electrode was negative polarity and the specimen was positive polarity during the EDM<br /> process. Weight of electrodes was measured after experiments<br /> <br /> Fig. 1. A sample of copper electrodes and related dimensions.<br /> <br /> using an accurate digital balance and the percentage of electrodes<br /> weight reduction was calculated as tool wear (wt.%).<br /> 3. Data analysis<br /> 3.1. Effect of factors and interactions on electrode wear<br /> Fig. 2 depicts the plot of main effects for electrode wear. Note<br /> that this plot illustrates data means versus factor levels. Based<br /> on this plot, the effect of each factor can be graphically assessed.<br /> Fig. 2a shows that pre-EDM roughing factor has a significant<br /> effect on electrode wear. It can also be seen from this figure that<br /> the effect of this factor is directly proportional to electrode wear.<br /> Also, it can be stated that by increasing the pre-EDM roughing<br /> factor electrode wear increases significantly. Fig. 2b portraits the<br /> effect of current on electrode wear. This figure presents that electrode wear is almost constant when current is increased from 5 to<br /> 10 A. By increasing the current form 10 to 20 A, electrode wear<br /> increases dramatically. Finally, one can interpret that current<br /> has a significant direct impact on electrode wear. Further experiments are needed to consider effect of current on electrode wear<br /> when current fluctuates between 5 and 15 A. Fig. 2c indicates<br /> the effect of on-time on electrode wear. This figure shows that<br /> on-time is reciprocally proportional to electrode wear. Fig. 2d<br /> suggests that voltage has a subtle effect on electrode wear. It<br /> is obvious from this figure that by changing voltage during this<br /> process electrode wear fluctuates insignificantly between 0.3 and<br /> 0.4%. So, it is concluded that the effect of voltage on electrode<br /> wear is almost negligible. Eventually, Fig. 2e shows the effect<br /> <br /> Table 1<br /> Factors and factor levels<br /> Factor<br /> <br /> Level 1<br /> <br /> Level 2<br /> <br /> Level 3<br /> <br /> Level 4<br /> <br /> Level 5<br /> <br /> On-time, A (␮s)<br /> Current, B (A)<br /> Voltage, C (V)<br /> Engaging time, D (s)<br /> Pre-EDM roughing, E<br /> <br /> 200<br /> 5<br /> 35<br /> 1<br /> 1<br /> <br /> 500<br /> 10<br /> 40<br /> 2<br /> 2<br /> <br /> –<br /> 15<br /> 50<br /> 3<br /> 3<br /> <br /> –<br /> 20<br /> 55<br /> 4<br /> 4<br /> <br /> –<br /> –<br /> –<br /> –<br /> 5<br /> Fig. 2. (a–e) Plot of main effects on electrode wear.<br /> <br /> H. Zarepour et al. / Journal of Materials Processing Technology 187–188 (2007) 711–714<br /> <br /> 713<br /> <br /> Table 2<br /> Values of estimated effects<br /> Term<br /> <br /> Effect<br /> <br /> Term<br /> <br /> Effect<br /> <br /> A<br /> B<br /> C<br /> D<br /> E<br /> A×B<br /> A×C<br /> A×D<br /> <br /> −0.1359<br /> 0.4351<br /> 0.0188<br /> −0.0909<br /> 0.2910<br /> −0.2059<br /> 0.0333<br /> −0.0610<br /> <br /> A×E<br /> B×C<br /> B×D<br /> B×E<br /> C×D<br /> C×E<br /> D×E<br /> <br /> −0.1710<br /> −0.0945<br /> 0.0440<br /> 0.2674<br /> 0.0582<br /> −0.0032<br /> 0.0018<br /> <br /> of engaging time between electrode and workpiece on electrode<br /> wear. This figure shows that the engaging time exhibits little<br /> effect on electrode wear. But unlike voltage, by increasing the<br /> engaging time electrode wear first increases and then stalls off<br /> insignificantly.<br /> Factor effects are calculated here to consider effect of factors<br /> on electrode wear more accurately than graphical assessment.<br /> Table 2 shows the magnitude of factor effects. Using this table<br /> one can sort factors in order of their significance. Also, this table<br /> illustrates the proportionality of factor effects to the response<br /> (electrode wear). Positive values of factor effects indicate direct<br /> proportionality of the respective factors, while negative values demonstrate that the corresponding factor is reciprocally<br /> proportional to the response.<br /> Note that two-way interactions are also included in Table 2.<br /> It can be seen from this table that current (B) has the most significant effect on electrode wear and is directly proportional to it.<br /> The interaction between engaging time and pre-EDM roughing<br /> (D × E) has the least effect on electrode wear and is directly proportional to it. One shortcoming to Table 2 is that the inferences<br /> made based on this table are only comparatively valid. To cope<br /> with the problem analysis of variance (ANOVA) is numerously<br /> used by experimenters. But ANOVA is not employed here since<br /> error normality and error independency assumptions are not satisfied. For more information on ANOVA assumptions consult<br /> reference [7].<br /> Normal probability plot of standardized effects is used here<br /> as an alternative to ANOVA approach. Although electrode wear<br /> does not follow the normal distribution, estimated effects can be<br /> assumed to exhibit normal distribution [9].<br /> Normal probability plot of standardized effects is shown in<br /> Fig. 3 to separate significant factors from insignificant factors.<br /> Factors and interactions which have departed considerably from<br /> the straight line have significant impact on electrode wear.<br /> 3.2. Regression analysis<br /> A quadratic polynomial regression equation, in terms of factors (Table 1), is obtained to fit experimental data. Least square<br /> method is used in regression analysis to find the coefficients of<br /> the equation. It is shown as Eq. (1).<br /> Wear (%) = 0.4895 + 0.0007A + 0.0068B − 0.0268C<br /> + 0.08049D − 0.1185E + 0.0016B2 + 0.0003C2<br /> <br /> Fig. 3. Normal probability plot of effects.<br /> <br /> + 0.0026D2 − 0.0037E2 − 0.0001AB<br /> − 0.0000004AC − 0.0002AD + 0.00007AE<br /> − 0.00013BC + 0.0072BD − 0.0022BE<br /> − 0.00072CD + 0.0030CE − 0.0056DE<br /> <br /> (1)<br /> <br /> Note that A2 has been removed from the equation since it is<br /> highly correlated with other variables.<br /> Regression statistics R2 and R2 are obtained equal to 98.7<br /> Adj<br /> and 97.8%, respectively. The R2 value indicates that the predictors explain 98.7% of variance in electrode wear. The R2 value<br /> Adj<br /> accounts for the number of predictors in the model. Both values indicate that the presented model fits the data very well.<br /> The analysis of variance for regression analysis is shown in<br /> Table 3. The p-value shows that the model is significant at α-level<br /> of 0.05.<br /> Model adequacy is checked by means of plot of residuals<br /> versus fits, plot of residuals versus order of the data, and normal<br /> plot of residuals. Fig. 4 shows the plot of residuals versus fits.<br /> It is clearly observed from this plot that residuals have constant<br /> variance. Plot of residuals versus order of the data is illustrated<br /> in Fig. 5. This figure indicates that residuals are independent of<br /> one another.<br /> Also, normal plot of residuals is shown in Fig. 6.<br /> Anderson–Darling statistic (AD) and the p-value calculated<br /> based on it, are shown in this figure. It is easily found out<br /> that residuals are normally distributed. The presented discussion<br /> implies that the predictive model is adequate.<br /> <br /> Table 3<br /> ANOVA table for regression analysis<br /> Source<br /> <br /> DF<br /> <br /> SS<br /> <br /> MS<br /> <br /> F<br /> <br /> p<br /> <br /> Regression<br /> Residual error<br /> <br /> 19<br /> 28<br /> <br /> 4.21979<br /> 0.05659<br /> <br /> 0.22209<br /> 0.00202<br /> <br /> 109.90<br /> <br /> 0.000<br /> <br /> Total<br /> <br /> 47<br /> <br /> 4.27638<br /> <br /> DF: degrees of freedom, SS: sum of Squares, MS: mean squares, F: F-value, p:<br /> p-value.<br /> <br /> 714<br /> <br /> H. Zarepour et al. / Journal of Materials Processing Technology 187–188 (2007) 711–714<br /> <br /> of all compared with effects of noises (uncontrollable factors).<br /> S/N ratio statistic (η) can be obtained by Eq. (2):<br /> η = −10 log10<br /> <br /> 1<br /> n<br /> <br /> n<br /> i=1<br /> <br /> 2<br /> yi<br /> <br /> (2)<br /> <br /> where yi is the ith observation of a treatment combination and n<br /> is the number of replications.<br /> The factor level which produces the largest η is detected as<br /> the factor level which pertains to the optimal condition. Accordingly, the optimal levels of A, B, C, D, and E factors would be<br /> equal to 2, 1, 1, 4, and 1, respectively. The corresponding value<br /> of each factor level can be found out referring to Table 1.<br /> 4. Conclusions<br /> Fig. 4. Plot of residuals vs. fits.<br /> <br /> In this research, an experimental investigation was performed<br /> to consider the electrode wear in EDM process of DIN 1.2714<br /> tool steel and the following results were concluded:<br /> 1. On-time, current, and pre-EDM roughing as factors along<br /> with on-time/current, on-time/pre-EDM roughing, and<br /> current/pre-EDM roughing as interactions, were found to<br /> have significant effect on electrode wear.<br /> 2. The predictive model was derived out of regression analysis.<br /> 3. The adequacy of predictive model was approved.<br /> 4. Optimal level of factors was presented using S/N ratio analysis.<br /> Acknowledgements<br /> <br /> Fig. 5. Plot of residuals vs. the order of the data.<br /> <br /> The authors appreciate Abzaran Co. and Industrial Cooperation Office of Isfahan University of Technology for supporting<br /> this research. Also, a special thanks to M. Pourehsan and M.<br /> Neshatpour for their close cooperation.<br /> References<br /> <br /> Fig. 6. Normal plot of residuals.<br /> <br /> 3.3. Determination of the optimal condition<br /> Optimal condition is detected by means of S/N ratio method.<br /> The rationale behind this method is to find a condition under<br /> which the effect of signals (controllable factors) is the greatest<br /> <br /> [1] C.C. Liu, Microstructure and tool electrode erosion in EDM of TiN/Si3 N4<br /> composites, Mater. Sci. Eng. J. 363 (2003) 221–227.<br /> [2] Y.Y. Tsai, T. Masuzawa, An index to evaluate the wear resistance of the electrode in micro-EDM, J. Mater. Process. Technol. 149 (1–3) (2004) 304–309.<br /> [3] J.S. Soni, G. Chakraverti, Investigative study on metal removal rate and wear<br /> ratio in EDM of high carbon high chromium die steel, J. Ind. Eng. 71 (1991).<br /> [4] J.S. Soni, G. Chakraverti, Effect of electrode material properties on surface<br /> roughness and dimensional accuracy in electro-discharge machining of high<br /> carbon high chromium die steel, J. Ind. Eng. 76 (1995) 46–51.<br /> [5] S. Singh, S. Maheshwari, P.C. Pandey, Some investigations into the electric discharge machining of hardened tool steel using different electrode<br /> materials, J. Mater. Process. Technol. 149 (2004) 272–277.<br /> [6] C.J. Luis, I. Puertas, G. Villa, Material removal rate and electrode wear study<br /> on the EDM of silicon carbide, J. Mater. Process. Technol. 164–165 (2005)<br /> 889–896.<br /> [7] D.C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons,<br /> 2000.<br /> [8] H.M. Raymond, E.W. Ronald, Probability and Statistics for Engineers and<br /> Scientists, Macmillan Publishing Co., Inc., New York, 1978.<br /> [9] R.L. Mason, R.F. Gunt, J.L. Hess, Statistical Design and Analysis of Experiments, John Wiley & Sons, 2003.<br /> <br />
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