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In this paper, a prediction model based on adaptive particle swarm optimization (APSO) algorithm and least squares support vector machine (LS-SVM) algorithm is proposed for the recognition of drill wear. Cutting force signal and vibration signal are used for tool wear monitoring. And these signals are preprocessed through wavelet threshold de-noising algorithm. Multiple signal feature extraction methods are carried out to process the sample data related to drill wear status. 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