The comparison between LR and NN methods for quality assurance prediction of bearing machining

Abstract

The state of a cutting tool is an important factor in bearing cutting process. Several methods developed for monitoring cutting tool and observing the bearing ring quality while prediction error has been attempted. This paper presents logistic regression (LR) and neural network (NN) methods that have been employed in tool life monitoring. We input 88 data total into two models, and resulted in errors of 6.30% and 3.16%, respectively. The results showed that NN is well-suited to the cutting tool condition, obviously which NN method is better than that of logistic regression. Thus, it can be concluded that NN is quite encouraging for future applications in the prediction of bearing cutting area.