On line tool wear monitoring based on auto associative neural network

This paper presents a new tool wear monitoring method based on auto associative neural network. The main advantage of the model lies that it can be built only by the data under normal cutting condition. Therefore, the training samples of the tool wear status are no longer needed during the training process that makes it easier to be applied in real industrial environment than other neural network models. An averaged distance indicator is proposed to denote not only the occurrence of the tool wear but also its severity. Moreover, the Levenberg-Marquardt (LM) training algorithm is introduced to improve the convergence accuracy of the auto associative neural network. Based on the proposed method, a framework for online tool condition monitoring is illustrated and the cutting force data under different tool wear status are collected to simulate the online modeling and monitoring process for the rough and finish milling respectively. The results show that the proposed indicator can reflect the evolution process of tool wear correctly and the LM algorithm is more accurate in comparison with the gradient descent methods. Therefore, it casts new light on practical application of neural network in the field of on line tool condition monitoring.