Tool wear estimation and life prognostics in milling: Model extension and generalization. (16th June 2021)

• Record Type: Journal Article
• Authors: Zhang, Yu; Zhu, Kunpeng; Duan, Xianyin; Li, Si
• Is Part Of: Mechanical systems and signal processing. Volume 155(2021)
• Publication Date: 2021-06-16
• Subjects: Milling -- Tool wear modeling and monitoring -- Adjustable coefficients -- Generalization -- Genetic algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107617
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Established a generic tool flank wear model with adjustable coefficients; Identified the relationship of the critical times to the adjustable coefficients in the model; Defined a method to predicate tool life with the wear model; Proposed an intelligent approach for online tool life prognosis. Abstract: Tool wear condition is a key factor in milling which directly affects machining precision and part quality. It is essential to seek a convenient method to model and predict tool states. A generic wear model with adjustable coefficients is proposed and validated in this study. Considering the inner mechanisms of different wear stages, the entire tool life is split into three mainly wear zones by critical time, which correspond to three main types of wear: running-in wear, adhesive wear, and three-body abrasive wear. The wear model is validated based on the experimental data, compared with other celebrated wear models, and then further improved to enhance the adaptability and generalization. It is shown that the generalized wear model can discriminate tool wear ranges accurately. The determination coefficient of the wear model is more than 98% with the experimental data. Based on the proposed model, an approach for tool life prognosing and tool wear condition evaluating is proposed. The predictive real-time monitoring data of tool life and wear can be obtained timely with a genetic algorithm.