Modelling cutting power and tool wear in turning of aluminium matrix composites using Artificial Neural Networks

Abstract

Aluminium matrix composites have been investigated since 1970s because of the high performance of these materials for aerospace, aircraft and automotive industries. This paper builds Artificial Neural Network (ANN) machining models of aluminium matrix composites according to cutting parameters. Feedforward ANN is created and trained using comprehensive data sets tested by the authors, and good performances of networks are achieved. The prediction results show the tool wear and the machining power are highly influenced by the cutting velocity. The increase in the feed leads to moderate decrease in the tool wear and moderate increase in the machining power.