Li, Yingguang, Liu, Changqing, Hua, Jiaqi, Gao, James ORCID: 0000-0001-5625-3654 and Maropoulos, Paul (2019) A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning. CIRP Annals ‐ Manufacturing Technology, 68 (1). pp. 487-490. ISSN 0007-8506 (doi:https://doi.org/10.1016/j.cirp.2019.03.010)

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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.

Item Type:	Article
Uncontrolled Keywords:	Condition monitoring, Process control, Meta-learning
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	17 Oct 2020 01:38
URI:	http://gala.gre.ac.uk/id/eprint/23360

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