Tool wear monitoring based on localized fuzzy neural networks for turning operation

Authors:
Hongli Gao;Mingheng Xu;Xiaohui Shi;HaiFeng Huang
Affiliations:
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, China;School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, China;School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, China;School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Year:
2009

Citing 2
Cited 0

Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks

PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Fuzzy estimation of feed-cutting force from current measurement-a case study on intelligent tool wear condition monitoring

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

On-line tool wear monitoring is essential to automatic machining process. In order to predict tool wear accurately and reliably under different cutting conditions, a novel tool wear monitoring system (TWMS) is proposed by using localized fuzzy neural networks(LFNN) in this study which may improve classification accuracy of tool states and the computing speed compared with BPNN and normal fuzzy neural networks in the process of turning. By analyzing cutting forces signals and acoustic emission signals in time domain, frequency domain, and time-frequency domain, a series of features that sensitive to tool states were selected as inputs of neural networks according to synthesis coefficient. The nonlinear relations between tool wear and features were modeled by using integrated neural network (INN) that constructed and optimized through LFNN trained by an adaptive learning algorithm. The experimental results show that the monitoring system based on LFNN is provided with high precision, rapid computing speed and good multiplication.