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Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. Wavelet neural network (WNN) is used widely in tool wear detection, but the curse of dimensionality and shortage in the responding speed and learning ability is brought about by the traditional models. An improved WNN algorithm which combines with modified particle swarm optimization (MPSO) is presented to overcome the problems. Based on the cutting power signal, the method is used to estimate the tool wear. The Daubechies-wavelet is used to decompose the signals into approximation and details. The energy and square-error of the signals in the detail levels is used as characters which indicating tool wear, the characters are input to the trained WNN to estimate the tool wear. Compared with conventional BP neutral network, conventional WNN and genetic algorithm-based WNN, a simpler structure and faster converge WNN is obtained by the new algorithm, and the accuracy for estimate tool wear is tested by simulation and experiments.