Research on Tool Wear Detection Based on Genetic Neural Network

Abstract

To improve the accuracy of tool wear detection, this paper proposes a tool wear detection method based on genetic neural network. Firstly, the vibration signals during tool processing are collected, and these signals are preprocessed to eliminate background noise. Then, in addition to the time-frequency analysis, the Ensemble Empirical Mode Decomposition which is more suitable for the processing of non-stationary random signals is also applied to extract tool wear sensitive features from signals. To reduce the computational complexity of the neural network, some minor components in the sensitive features can be omitted by kernel principal component analysis, leaving the principal components as the input of the neural network. Finally, aiming at the shortcomings of the BP neural network, the genetic algorithm is optimized in terms of chromosome coding, setting of control parameters and genetic operation, so that it can obtain better weights and thresholds to improve the BP neural network. The experimental result proves that the accuracy of BP neural network is 86.7% and that of genetic neural network is 96%. The tool wear detection method based on genetic neural network is more suitable for practical use.