Optimization of Process Parameters in Turning Operation of AISI-1016 Alloy Steels with CBN Using Artificial Neural Networks
Journal Title: INTERNATIONAL JOURNAL OF ENGINEERING TRENDS AND TECHNOLOGY - Year 2013, Vol 5, Issue 6
Abstract
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.
Authors and Affiliations
K. Mani lavanya , R. K. Suresh , A. Sushil Kumar Priya , G. Krishnaiah
Keywords
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