Tool wear estimation with a data-driven physics coupling approach. (October 2022)

• Record Type: Journal Article
• Authors: Zhang, Yu; Zhu, Kunpeng
• Is Part Of: Manufacturing letters. Volume 34(2022)
• Page Start: 38
• Page End: 42
• Publication Date: 2022-10
• Subjects: Tool wear -- Data-driven physics coupling -- Machine learning -- Generalization; Manufacturing industries -- Periodicals; Production engineering -- Periodicals; Manufacturing industries; Periodicals; 670
• DOI: 10.1016/j.mfglet.2022.09.005
• Languages: English
• ISSNs: 2213-8463

Abstract

Abstract: It's essential to predict tool wear condition in high-speed milling (HSM), which is a key factor affecting machining precision and part quality. Presently, many physics-based methods and data-driven machine learning methods are applied for tool wear prediction. Inspired by the previously developed generic physical model, a novel data-driven physics coupling approach is established and applied to identify and predict tool flank wear in this study. The proposed ARIMA-WNN model is verified with case studies, in which the determination coefficients are stable at above 95%. It's flexible to choose the position and length of leading time intervals and predict tool wear condition, which is more adaptive and effective than physics-based methods. In addition, it clarifies the specific distribution of each parameter to machining conditions in the generic physical-empirical wear model.