Paper Title:
Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm
  Abstract

In order to accurately estimate tool life for milling operation, a novel tool condition monitoring system was proposed to improve classifying precision in different cutting condition. Lots of features were extracted from cutting forces signal, vibration signal and acoustic emission signal by different signal processing method, only a few features selected by principal component analysis (PCA) according to contribution rate, and constructed as input vector. The relation between tool condition and features was built by radial basis probability neural network which control parameter of kernel function and hidden central vector were optimized by improved genetic algorithm. The experimental results show that the method proposed in the paper achieves higher recognition rate, good generalization ability and better available practicality.

  Info
Periodical
Advanced Materials Research (Volumes 139-141)
Edited by
Liangchi Zhang, Chunliang Zhang and Tielin Shi
Pages
2522-2526
DOI
10.4028/www.scientific.net/AMR.139-141.2522
Citation
D. W. Li, H. L. Gao, Y. Shou, P. Du, M. H. Xu, "Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm", Advanced Materials Research, Vols. 139-141, pp. 2522-2526, 2010
Online since
October 2010
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Price
$32.00
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