Optimization of Process Parameters in Turning Operation of AISI-1016 Alloy Steels with CBN Using Artificial Neural Networks

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-5 Number-6                       
Year of Publication : 2013
Authors : K.Mani lavanya , R.K.Suresh , A.Sushil Kumar Priya , G.Krishnaiah


K.Mani lavanya , R.K.Suresh , A.Sushil Kumar Priya , G.Krishnaiah. "Optimization of Process Parameters in Turning Operation of AISI-1016 Alloy Steels with CBN Using Artificial Neural Networks". International Journal of Engineering Trends and Technology (IJETT). V5(6):294-297 Nov 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


We report the development of a predictive model based on artificial neural network (ANN) for the estimation of Surface roughness of AISI-1016 during orthogonal turning with CBN insert tool. Turning experiments were conducted at different cutting conditions on a PSG-A141 conventional lathe using CBN uncoated insert as tool with ISO designations SNMG - 120408 and AISI-1016 as work piece using full factorial design. Cutting speed (v), feed rate (f), depth of cut (d), were the input parameters of the machining experiment as well as the ANN prediction model while the Surface roughness (Ra) was the output variable. The neural networks with feed-forward and back-propagation learning algorithms were designed using the MATLAB Neural Network Toolbox. An optimal ANN architecture with the Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained using Taguchi method of experimental design. With the optimized ANN architecture, parametric study was conducted to relate the effect of each turning parameters on the surface roughness.The results obtained conclude that ANN is reliable method and it can be readily applied to different metal cutting processes with greater confidence.


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Model, ANN, CBN inserts, Taguchi method, AISI-1016 steel, Turning