Predicting the grinding force of titanium matrix composites using the genetic algorithm optimizing back-propagation neural network model

Abstract

A back-propagation neural network BP model and a genetic algorithm optimizing back-propagation neural network (GA-BP) model are proposed to predict the grinding forces produced during the creep-feed deep grinding of titanium matrix composites. These models consider quantitative and non-quantitative grinding parameters (e.g. up-grinding mode and down-grinding mode) as inputs. Comparative results show that the GA-BP model has better prediction accuracy (e.g. up to 95%) than the conventional regression model and the BP model. Specific grinding energy was calculated against the grinding parameters and grinding modes based on the grinding forces predicted by the GA-BP model.