By Topic

Tool wear monitoring based on novel evolutionary artificial neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Hongli Gao ; School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, 610031, China ; Dengwan Li ; Mingheng Xu ; Min Zhao
more authors

In order to improve the accuracy and speed of on-line tool wear monitoring system, an evolutionary neural network using variable string genetic algorithm (VGA) was developed to construct the relations between tool wear and signal features extracted from cutting forces, vibrations, and acoustic emission by different signal processing methods. The system could automatically evolve the appropriate architecture of neural network and find a near-optimal set of connection weights globally. Then the conformable connection weights for model could be found with back-propagation (BP) algorithm, the multi-model finally completed calculation of tool wear. The experimental results show that the system proposed in the paper has higher classification precision and calculating speed.

Published in:

2010 Sixth International Conference on Natural Computation  (Volume:3 )

Date of Conference:

10-12 Aug. 2010