Study on prediction of experimental machining response data using artificial neural network

Abstract

ABSTRACT The drilling is one of the most rigorous machining processes in the case of Fibre Reinforced Polymer (FRP) materials due to its promoting characteristic by which components can be assembled easily. As it is common in the case of all composite drilling, delamination that gets induced is directly influenced either by thrust force or twisting force/torque. The present empirical study deals with twisting force/torque attribute obtained based on Taguchi’s Design of Experiments (DOE) leading to an influence of the machining parameters (feed rate, cutting speed, tool material) while drilling hybrid composite laminate and the same was validated using Artificial Neural Network (ANN) Back Propagation algorithm approach on training/testing experimental data-set. The output response obtained through ANN was found to be nearest to the experimental results with the least output error.