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.


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