Abstract
In the milling process of metallic parts, appropriate tool condition is essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states in milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a Tool condition monitoring (TCM) method in milling process based on multi-source pattern recognition and state transfer path. Firstly, improved K-Means clustering method is used to generate multiple patterns of tool wear. Secondly, a multi-source pattern recognition model framework is developed, and the multiple observation windows and the pattern transfer path are considered in multi-source pattern recognition model. Lastly, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.
Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model
