A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning. Issue 1 (2019)

• Record Type: Journal Article
• Authors: Li, Yingguang; Liu, Changqing; Hua, Jiaqi; Gao, James; Maropoulos, Paul
• Is Part Of: CIRP annals. Volume 68:Issue 1(2019)
• Page Start: 487
• Page End: 490
• Publication Date: 2019
• Subjects: Condition monitoring -- Process control -- Meta-learning; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2019.03.010
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: Monitoring and predicting tool wear is an important issue in dynamic process control under changing conditions, especially for machining large-sized difficult-to-cut materials used in airplanes. Existing tool wear monitoring and prediction methods are mainly based on given cutting conditions over a period of time. This paper presents a novel method for accurately predicting tool wear under varying cutting conditions based on a proposed new meta-learning model which can be easily trained, updated and adapted to new machining tasks of different cutting conditions. Experiments proved a substantial improvement in the accuracy of predicting tool wear compared with existing deep learning methods.