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

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.