Neural network approach for optimizing machining parameters of aluminium nano composite

Abstract

In the present study an integrated approach based on Taguchi modelling and Artificial Neural Networks (ANN) are used for modelling the experiments and output parameters of Abrasive Water Jet Machining (AWJM). For this process Al7075 nano composite is fabricated by using SiC as reinforcement. Composite is fabricated using ultrasonic cavitation method. The fabricated composite is machined by AWJM based on Taguchi L18 Orthogonal Array (OA) approach. For ANN modelling the data was selected according to the Taguchi L18 orthogonal array i.e., the selected input parameters for ANN are Abrasive Jet Speed, Abrasive Flow Rate, Stand of Distance, Pressure and the outputs from the ANN model are Average KERF width, Material Removal Rate (MRR). Batch Back Propagation is used for Training, Validation and Testing of Artificial neural network. From ANN the correlation coefficient R obtained is above 96% that implies the predicted values are 96% in agreement with the experimental values.