Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm

Deng Wan Li; Hong Li Gao; Yun Shou; Peng Du; Ming Heng Xu

doi:10.4028/www.scientific.net/AMR.139-141.2522

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 139-141Tool Condition Monitoring Based on Radial Basis...

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.

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Periodical:

Advanced Materials Research (Volumes 139-141)

Pages:

2522-2526

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.139-141.2522

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Online since:

October 2010

Authors:

Deng Wan Li, Hong Li Gao, Yun Shou, Peng Du, Ming Heng Xu

Keywords:

Genetic Algorithm (GA), Probability Neural Network, Tool Condition Monitoring

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