Study on the Identification Method of Tool Wear State Based on BP Neural Network Optimized by Genetic Algorithm

Zheng Qiang Li; Peng Nie; Shu Guo Zhao; Zhang Shun Ding

doi:10.4028/www.scientific.net/AMM.602-605.2458

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Study on the Identification Method of Tool Wear...

Study on the Identification Method of Tool Wear State Based on BP Neural Network Optimized by Genetic Algorithm

Abstract:

According to the un-stationary feature of the acoustic emission signals of tool wear, a tool wear state identification method based on genetic algorithm and BP neural network was proposed. The method reconstructed the acoustic emission signals and calculated the singular spectrum. And the feature vectors were reconstructed based on the singular spectrum. BP neural network was optimized by genetic algorithm. The weights of BP neural network and the thresholds were optimized originally to get more optimal solutions in solution space. Then the more optimal solutions were put into BP neural network to identify the tool wear state by the optimized classification machine. The study indicated that this method can make an accurate identification of tool wear state and should be widely used.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

2458-2461

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.2458

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Online since:

August 2014

Authors:

Zheng Qiang Li*, Peng Nie, Shu Guo Zhao, Zhang Shun Ding

Keywords:

GA, Neural Network (NN), Tool Wear

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* - Corresponding Author

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