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Based on a composited Newton–Cotes formula, this paper proposes a numerical method to predict milling stability considering regenerative chatter and focusing on rate and prediction accuracy. First, the dynamic model of milling motion is expressed as state-space equations considering regenerative chatter, with the tooth passing period divided into a set of time intervals. Second, a composited Newton–Cotes formula is introduced to calculate the transition function map for each time interval. Third, the state transition matrix is constructed based on the above-mentioned transition function, and the prediction stability boundary is determined by the Floquet theory. Finally, simulation analysis and experimental verification are conducted to verify the effectiveness of the proposed method. The simulation results demonstrate that, for the milling model with a single degree of freedom (DOF), the convergence rate and prediction accuracy of the proposed method are higher than those of the comparison method. The experimental results demonstrate that, for the milling model with two DOFs, the machining parameters below the prediction stability boundary can avoid the chatter as much as possible, ensuring the machined surface quality.

This paper proposes a face detection method by combining SVM into the traditional face detection algorithm named AdaBoost. This method adopts SVM classifier instead of traditional AdaBoost classifier based on AdaBoost cascade framework. To get SVM classifier, extract several Haar-like features with the strongest classification ability in the process of training SVM classifier. To ensure the generalization capability of classifier classify validation set, and using the penalty factor setting with focus makes SVM classifier pay more attention to the face samples, while the training samples reached a high goodness of fit. At the same time, this thesis improves the extraction method of Haar-like feature. Finally, using Matlab simulation test the effectiveness of the algorithm. The experiments show that the improved detection algorithm of face with combining AdaBoost and SVM reduces the amount of computation and improve the detection rate.