A Hybrid Approach for Modeling Machining Performance of Aluminum Metal Matrix Composites Reinforced with Industrial Waste Materials

Abstract

This current study is intended to examine the machining behaviour of red mud reinforced aluminum metal matrix composites with different parameters specifically, cutting speed, feed, depth of cut and nose radius. A two hybrid networks model is developed to envisage the performance of machining parameters. Artificial Neural Network (ANN) model is developed by with machining parameters and combined by captured vibration and power signals throughout machining operation. Grey relational grade which is obtained from vibration and power signals is used with the primary machining parameters to construct the hybrid network model. A newly developed grey based neural network (hybrid) model is found to offer better prediction on output responses compared to the neural network model. The predicted result obtained from the based neural network model is good agreement with investigational results.