On line tool wear monitoring based on auto associative neural network

Authors:
Guofeng Wang;Yinhu Cui
Affiliations:
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China 300072;Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China 300072
Venue:
Journal of Intelligent Manufacturing
Year:
2013

Citing 4
Cited 0

Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns

Journal of Intelligent Manufacturing
Input and output mapping sensitive auto-associative multilayer perceptron for computer interface system based on image processing of laser pointer spot

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks

Advances in Engineering Software
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.