A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning

Yingguang Li*, Changqing Liu, Jiaqi Hua, James Gao, Paul Maropoulos

*Corresponding author for this work

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)487-490
JournalCIRP Annals
Volume68
Issue number1
DOIs
Publication statusPublished - 16 Apr 2019
Externally publishedYes

Keywords

  • Condition monitoring
  • Meta-learning
  • Process control

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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