Abstract    Nowadays, Electrical Discharge Machining (EDM) has become one of the most extensively used non-traditional material removal process. Its unique feature of using thermal energy to machine hard to machine electrically conductive materials is its distinctive advantage in the manufacturing of moulds, dies and aerospace components. Howevere, EDM is a costly process and hence proper selection of its process parameters is essential to reduce production cost and improve product quality. In this study the effect of input EDM parameters on 2312 hot worked steel, widely used in mold manufacturing, is modeled and optimized. The proposed approach is based on statistical analysis on the experimental data. The input parameters are peak current (I), pulse on time (Ton), pulse off time (Toff), duty factor (h) and voltage (V). Surface roughness is the most important performance characteristic in EDM. The experimental data are gathered using Taguchi L36 design matrix. In order to establish the relations between input and output parameters, various regression functions have been fitted on the experimental data. In the last section of this research, genetic algorithm has been employed for optimization of process parameters. Using the proposed optimization procedure, proper levels of input parameters for any desirable output can be identified. A verification test is also performed to verify the accuracy of optimization procedure in determining the optimal levels of machining parameters. The results indicate that the proposed modeling technique and genetic algorithm are quite efficient in modeling and optimization of EDM process parameters.

Keywords    Taguchi technique, Signal to noise analysis (S/N), Electrical discharge machining (EDM), Optimization, genetic algorithm (GA), Analysis of variance (ANOVA).

چکیده    امروزه ماشين‌كاري تخليه الكتريكي به يكي از پركاربردترين فرآيندهاي پيشرفته ماشين‌كاري تبديل شده است. در اين فرآيند به دليل استفاده از انرژي گرمايي براي براده‌برداري، سختي ماده‌، عامل باز دارنده نبوده و مي‌توان مواد سختي كه براي ساخت قالب‌هاي تزريق پلاستيك و قطعات مربوط به صنعت هوا و فضا به کار می­روند را به آساني ماشين‌كاري كرد. از آنجا كه اين فرآيند هزينه‌بر مي‌باشد لذا انتخاب صحيح پارامترهاي ماشين‌كاري در قيمت تمام شده‌ و كيفيت قطعات توليدي مؤثر مي‌باشد. در اين تحقيق تاثير پارامترهای تنظيمی در ماشين‌كاري تخليه الکتريکی فولاد گرم‌كار 2312 مورد استفاده در صنعت قالب‌سازي، مدل­سازی و بهينه­سازی شده است. مدل­سازی فرآيند توسط روش­های آماری و با استناد بر داده­های تجربی انجام يافته است. پارامترهاي ورودي شامل جريان الكتريسيته، ‌زمان‌هاي روشني و خاموشي پالس، فاكتور كار و ولتاژكاري مي‌باشند. همچنين زبري سطح به عنوان مشخصه خروجي فرآيند درنظر گرفته شده ­است. به منظور گردآوری داده­های مورد نياز در انجام اين تحقيق، آزمايشات تجربی با استفاده از طرح تاگوچي L36 انجام شده است. پس از اخذ داده­های مورد نظر، جهت ايجاد ارتباط بين پارامترهاي ورودي و مشخصه‌ خروجي با بکارگيری تابع رگرسيونی، مدل‌ رياضي طراحی گرديده است. در بخش آخر اين تحقيق، با به کار گيري الگوريتم ژنتيك، تابع بهينه­سازی براي زبري سطح به کار گرفته شد. نتايج بهينه­سازی نيز توسط آزمايشات تجربی صحه­گذاری مجدد گرديد. نتايج حاصل از بهينه‌سازي و آزمايشات تجربي نشان داد كه به كارگيري هم‌زمان روش تاگوچي و الگوريتم ژنتيك مي‌تواند به ابزاري كارامد جهت بهينه‌سازي فرآيند تخليه الكتريكي تبديل شود.

References    1.     Salman, Ö. and Kayacan, M. C., "Evolutionary programming method for modeling the edm parameters for roughness", Journal of Materials Processing Technology,  Vol. 200, No. 1, (2008), 347-355. 2.     Lin, J. and Lin, C., "The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics", International Journal of Machine Tools and Manufacture,  Vol. 42, No. 2, (2002), 237-244. 3.     Yang, S.-H., Srinivas, J., Mohan, S., Lee, D.-M. and Balaji, S., "Optimization of electric discharge machining using simulated annealing", Journal of Materials Processing Technology,  Vol. 209, No. 9, (2009), 4471-4475. 4.     Hasçalık, A. and Çaydaş, U., "Electrical discharge machining of titanium alloy (Ti–6Al–4V)", Applied Surface Science,  Vol. 253, No. 22, (2007), 9007-9016. 5.     Fonda, P., Wang, Z., Yamazaki, K. and Akutsu, Y., "A fundamental study on Ti–6Al–4V's thermal and electrical properties and their relation to edm productivity", Journal of Materials Processing Technology,  Vol. 202, No. 1, (2008), 583-589. 6.     Mohri, N., Saito, N., Takawashi, T. and Kobayashi, K., "Mirror-like finishing by EDM(multi divided electrode method)", in Twenty-Fifth International Machine Tool Design and Research Conference. Vol., No., (1985), 329-336. 7.     H. Narumiya, N. Mohri, N. Saito, H. Ootake, T. Takawashi, K. Kobayashi, “Finishing on the large area of work surface by EDM”, Journal of Japan Society of Precision Engineering   Vol. 53, No. 1, (1987), 124-130. 8.     Narumiya, H., Mohri, N. and Suzuki, M., "Surface modification by edm, research and technological development in nontraditional machining", in the Winter Annual Meeting of the ASME, Chicago,USA. (1988), 21-30. 9.     Luo, Y., Zhang, Z., Yu, C. and Zhang, Y., "Mirror surface edm by electric field partially induced", CIRP Annals-Manufacturing Technology,  Vol. 37, No. 1, (1988), 179-181. 10.   Narumiya, H., Mohri, N., Saito, N., Ootake, H., Tsunekawa, Y., Takawashi, T., and Kobayashi, K., "Edm by powder suspended working fluid", in Proceedings of 9th International Symposium on Electromachining (ISEM IX). (1989), 5-8. 11.   N. Saito, N. Mohri, “Improvement of machined surface roughness in large area EDM”, Journal of the Japan Society of Precision Engineering,  Vol. 57, No. 6, (1991), 954-958. 12.   Yan, B.-H. and Chen, S.-L., "Effects of dielectric with suspended aluminum powder on edm", Journal-Chinese Society of Mechanical Engineers,  Vol. 14, (1993), 307-307. 13.   Kiyak, M. and Çakır, O., "Examination of machining parameters on surface roughness in EDM of tool steel", Journal of Materials Processing Technology,  Vol. 191, No. 1, (2007), 141-144. 14.   Rao, G. K. M., Rangajanardhaa, G., Rao, D. H. and Rao, M. S., "Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm", Journal of Materials Processing Technology,  Vol. 209, No. 3, (2009), 1512-1520. 15.   Sarkar, S., Mitra, S. and Bhattacharyya, B., "Parametric optimisation of wire electrical discharge machining of γ titanium aluminide alloy through an artificial neural network model", The International Journal of Advanced Manufacturing Technology,  Vol. 27, No. 5-6, (2006), 501-508. 16.   Kolahan, F., Golmezergy, R. and Moghaddam, M. A., "Multi objective optimization of turning process using grey relational analysis and simulated annealing", Applied Mechanics and Materials,  Vol. 110-116, (2012), 2926-2932. 17.   Vishwakarma, M., Parashar, V. and Khare, V., "Regression analysis and optimization of material removal rate on electric discharge machine for en-19 alloy steel", Vol. 2, No. 11, (2012) 18.   Asal, V. D., Patel, R. I. and Choudhary, A. B., "Optimization of process parameters of EDM using anova method", International Journal of Engineering Research and Applications,  Vol. 3, No. 2, (2013), 1119-1125. 19.   Kumar, A., Kumar, V. and Kumar, J., "Prediction of surface roughness in wire electric discharge machining (WEDM) process based on response surface methodology", International Jl. of Engineering &Technology,  Vol. 2, No. 4, (2012), 708-719. 20.   Kolahan, F., "Modeling and optimization of process parameters in pmedm by genetic algorithm", in World Academy of Science, Engineering and Technology-WCEST, (2008).