Identification of Tool Wear Condition Based on Generalized Fractal Dimensions and BP Neural Network Optimized with Genetic Algorithm

Abstract

Based on multi-fractal theory, the generalized fractal dimensions of acoustic emission (AE) signals during cutting process were calculated using improved box-counting method. The generalized dimension spectrums of AE signals for different tool wear condition were gained, and the relation between tool wear condition and generalized dimensions was analyzed. Together with cutting process parameters, the generalized fractal dimensions were taken as the input vectors of BP neural network after normalization. The initial weight and bias values of BP neural network which was used to classify the tool wear condition were optimized with Genetic Algorithm. The test results showed that the method can be used effectively for the identification of tool wear condition.