IJE TRANSACTIONS C: Aspects Vol. 31, No. 6 (June 2018) 997-1003   

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R. Ramesh, N Manikandan, V Madhava Selvan and K Lakshmi Kala
( Received: November 07, 2017 – Accepted in Revised Form: March 09, 2018 )

Abstract    Electrical discharge machining has the capability of machining complicated shapes in electrically conductive materials independent of hardness of the work materials. This present article details the development of multiple regression models for envisaging the material removal rate and roughness of machined surface in electrical discharge machining of Hastelloy C276. The experimental runs are devised as per Taguchi’s principles and empirical relations are established using multiple regression analysis. Taguchi’s methodology can be applied as a single aspects optimization technique for attaining the best set of possible process parameter for material removal rate and roughness of the machined surface. A statistical tool called analysis of variance is employed for determining the significance of input process variables that influences the desired performance measures such as material removal rate and roughness of the electrically machined surface. The developed multiple regression models are flexible, competent and precise in prediction of desired performance measures. The developed regression models were validated and the predicted results from the evolved regression models are closer with the experimental outcomes.


Keywords    Electrical Discharge Machining, Taguchi’s Design Approach, Hastelloy, Analysis of Variance, Regression Analysis.


چکیده    EDMتوانایی پردازش اشکال پیچیده را در مواد هدایت الکتریکی مستقل از سختی مواد کار دارد. در این مقاله حاضر، توسعه مدل های رگرسیون چندگانه برای پیش بینی میزان حذف مواد و زبری سطح ماشین کاری در 276EDM Hastelloy C. آزمایشهای انجام شده بر اساس اصول Taguchi طراحی شده و روابط تجربی با استفاده از تجزیه و تحلیل رگرسیون چندگانه ایجاد شده است. روش Taguchi می تواند به عنوان تکنیک بهینه سازی تک جنبه برای دستیابی به بهترین مجموعه ای از پارامترهای فرایند ممکن برای میزان حذف مواد و زبری سطح ماشین کاری مورد استفاده قرار گیرد. یک ابزار آماری به نام تجزیه و تحلیل واریانس برای تعیین اهمیت متغیرهای فرآیند ورودی که بر اندازه گیری های مورد نظر از قبیل میزان حذف مواد و زبری سطح ماشین کاری الکتریکی تأثیر می گذارد، استفاده می شود. مدل های رگرسیون چندگانه توسعه یافته در پیش بینی ضوابط عملکرد مطلوب، انعطاف پذیر، صحیح و دقیق هستند. مدل های رگرسیون توسعه یافته اعتبار یافته و نتایج پیش بینی شده از مدل های رگرسیون تکامل یافته با نتایج تجربی نزدیک تر است.


1.     Cai, X., Qin, S., Li, J., An, Q. and Chen, M., "Experimental investigation on surface integrity of end milling nickel-based alloy—inconel 718", Machining Science and Technology,  Vol. 18, No. 1, (2014), 31-46.

2.     Azadi, M., Iziy, M., Marbout, A. and Rizi, M., "Investigation of the heat treatment effect on microstructures and phases of inconel 713c superalloy", International Journal of Engineering-Transactions A: Basics,  Vol. 30, No. 10, (2017), 1538.

3.     Pervaiz, S., Rashid, A., Deiab, I. and Nicolescu, M., "Influence of tool materials on machinability of titanium-and nickel-based alloys: A review", Materials and Manufacturing Processes,  Vol. 29, No. 3, (2014), 219-252.

4.     Yazdani-Rad, R., Rahaei, M., Kazemzadeh, A. and Hasanzadeh, M., "Synthesis and characterization of nanocrystalline ni3al intermetallic during mechanical alloying process", International Journal of Engineering-Transactions C: Aspects,  Vol. 25, No. 2, (2012), 89.

5.     Kuppan, P., Narayanan, S., Rajadurai, A. and Adithan, M., "Effect of edm parameters on hole quality characteristics in deep hole drilling of inconel 718 superalloy", International Journal of Manufacturing Research,  Vol. 10, No. 1, (2015), 45-63.

 6.     Qu, N., Hu, Y., Zhu, D. and Xu, Z., "Electrochemical machining of blisk channels with progressive-pressure electrolyte flow", Materials and Manufacturing Processes,  Vol. 29, No. 5, (2014), 572-578.

7.     Ho, K. and Newman, S., "State of the art electrical discharge machining (edm)", International Journal of Machine Tools and Manufacture,  Vol. 43, No. 13, (2003), 1287-1300.

8.     Moghaddam, M.A. and Kolahan, F., "Improvement of surface finish when edm aisi 2312 hot worked steel using taguchi approach and genetic algorithm", International Journal of Engineering-Transactions C: Aspects,  Vol. 27, No. 3, (2013), 417.

9.     Azadi Moghaddam, M. and Kolahan, F., "Optimization of edm process parameters using statistical analysis and simulated annealing algorithm", International Journal of Engineering,  Vol. 28, (2015).

10.   Kuppan, P., Rajadurai, A. and Narayanan, S., "Influence of edm process parameters in deep hole drilling of inconel 718", The International Journal of Advanced Manufacturing Technology,  Vol. 38, No. 1-2, (2008), 74-84.

11.   El-Hofy, H., "Advanced machining processes: Nontraditional and hybrid machining processes, McGraw-Hill New York, NY,  Vol. 120,  (2005).

12.   Dhanabalan, S., Sivakumar, K. and Narayanan, C.S., "Optimization of machining parameters of edm while machining inconel 718 for form tolerance and orientation tolerance", (2013).

13.   Caiazzo, F., Cuccaro, L., Fierro, I., Petrone, G. and Alfieri, V., "Electrical discharge machining of rené 108 ds nickel superalloy for aerospace turbine blades", Procedia CIRP,  Vol. 33, (2015), 382-387.

14.   Çaydaş, U. and Hascalık, A., "A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method", Journal of materials processing technology,  Vol. 202, No. 1-3, (2008), 574-582.

15.   Neshat, N., "An approach of artificial neural networks modeling based on fuzzy regression for forecasting purposes", International Journal of Engineering-Transactions B: Applications,  Vol. 28, No. 11, (2015), 1259.

16.   Sahoo, A.K. and Pradhan, S., "Modeling and optimization of al/sicp mmc machining using taguchi approach", Measurement,  Vol. 46, No. 9, (2013), 3064-3072.

17.   Ahilan, C., Kumanan, S., Sivakumaran, N. and Dhas, J.E.R., "Modeling and prediction of machining quality in cnc turning process using intelligent hybrid decision making tools", Applied Soft Computing,  Vol. 13, No. 3, (2013), 1543-1551.

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