Investigation Of Surface Roughness In Turning of In-situ Al6061-TiC Metal Matrix Composite By Taguchi And Prediction Of Response by ANN

In the present study turning experiments, are carried out on in-situ, developed Al6061-10 Wt % TiC metal matrix composite rod for to find out the optimal combination of process parameters for to achieve optimal surface roughness. The experimental design is planned by using L27 orthogonal array, and the results are analysed by using Taguchi's lower the best signal to noise ratio. The analysis of signal to noise ratios shows that the optimal surface roughness value is obtained at higher cutting speeds and lower feed rate and depth of cut. ANOVA is performed to know the percentage contribution of cutting speed, feed rate and depth of cut. ANOVA results shows that among the three process parameters cutting speed has the most influence on surface roughness. An artificial neural network model is developed to predict the surface roughness. The network is trained by supplying input and output values. The predicted surface roughness values from ANN model are closely near to the experimental results. (C) 2018 Elsevier Ltd. All rights reserved.