Artificial neural networks back propagation algorithm for cutting force components predictions

Abstract

Reinforced Poly Ether Ether Ketone with 30% of Carbon Fiber (PEEK CF30) offer several thermo-mechanical advantages over standard materials and alloys which make them better candidates in different applications. However, the hard and abrasive nature of the reinforcement fiber is responsible for rapid tool wear and high machining costs. It is very important to find highly effective ways to machine that material. Accordingly, it is important to predict forces when machining fiber matrix composites because this will help to choose perfect tools for machining and ultimately save both money and time. In this study, Artificial Neural Network (ANN) was applied to predict the cutting force components in turning operations of PEEK CF30 using TiN coated cutting tools under dry conditions where the machining parameters are cutting speed ranges, feed rate, and depth of cut. For this study, the experiments have been conducted using full factorial design experiments (DOEs) on CNC turning machine. The results indicated that the well-trained (ANN) model could be able to predict the cutting force components in turning of Carbon Fiber Reinforcement Polymer (CFRP) composites. Complementary results that were not used during derivation of the ANN model have enabled one to assess the validity of the obtained predictions. © 2014 AFM, EDP Sciences .