Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm
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.
Liangchi Zhang, Chunliang Zhang and Tielin Shi
D. W. Li et al., "Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm", Advanced Materials Research, Vols. 139-141, pp. 2522-2526, 2010