IJE TRANSACTIONS A: Basics Vol. 31, No. 1 (January 2018) 32-37   

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D. Rajeev, D. Dinakaran, N. Kanthavelkumaran and N.Austin
( Received: June 05, 2016 – Accepted in Revised Form: November 30, 2017 )

Abstract    The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on Design of experiments (Response surface methodology). The cutting speed, feed and depth of cut are taken as the inputs and the wear is the output. The results reveal that the ANN provides better accuracy when compared to Regression analysis.


Keywords    AISI4140, ANN, Hard Turning, Regression


چکیده    در چند دهه گذشته ماشین کاری با آلیاژ سخت به واقعیت انجامید. از ابزار پوشش یافته کاربیدی که جایگزین تیغه های سخت مکعبی نیتریتی است استفاده گردید. بهرحال ابزار کاری با آلیاژهای سخت پدیده احتناب ناپذیربوده است. در این تحقیق تخمین برش با استفاده از ابزار پوشش یافته کاربیدی بروش ریگراسیون و روش فازی لوجیک و شیکه عصبی مئرد بررسی قرار گرفت. کار با قطعات آلیاژ استیل AISI4140(47 HRC) مورد بررسی قرار گرفت. تجربیات بدست آمده با برش قطعات و با پردازش دادده های ورودی و خروجی شبکه نتایج نشان میدهد آنالیز داده های شبکه عصبی دقت بهتری در مقایسه با روش های ریگراسیون و فازی لوجیک دارد.


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