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 under different tool wear conditions were obtained，and the relationship between tool wear quantity 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 were optimized with genetic algorithm which was used to predict the tool wear quantity.The test results show that the method can be effectively used for the prediction of tool wear， and the mean prediction error is 001mm.

Key words: generalized fractal dimensions, BP neural network, tool wear, prediction, genetic algorithm