Neural network process modelling for turning of steel parts using conventional and wiper inserts

摘要

In this paper, the effects of insert design in turning of steel parts are presented. Surface finishing has been investigated in finish turning of AIS1 1045 steel using conventional and wiper design inserts. Regression models and neural network models arc developed for predicting surface roughness, mean force and cutting power. Experimental results indicate that lower surface roughness values are attainable with wiper tools. Neural network based predictions of surface roughness are carried out and compared with non-training experimental data. These results show that neural network models are suitable for predicting surface roughness patterns for a range of cutting conditions in turning.