Abstract: In high-speed milling, the cutter undergoes ultra-high-speed milling discontinuously, leading to rapid tool wear or breakage, which is difficult to monitor and will seriously affect machining accuracy and product quality, which underscores the importance of tool wear condition monitoring. Although the vibration method is an effective tool condition monitoring method, the vibration signal contains a variety of components and much noise, which decrease the accuracy of tool wear condition monitoring. To solve this problem, a sparse decomposition method of vibration signal was proposed based on the dual basis pursuit algorithm and morphological component analysis. First, morphological and sparse characteristics of the vibration signals in high speed milling were analyzed, and a dual basis pursuit framework was constructed and solved by an augmented Lagrangian variable splitting, thus separating the impulse components and harmonic components. Subsequently, two feature vectors, including the impulse density and amplitude

Key words: high-speed milling, tool condition monitoring, morphological component analysis, sparse decomposition