An accurate tool wear prediction method under different cutting conditions based on network architecture search

Tool wear prediction is of significance in advanced manufacturing industries, as it aims to ensure the quality of parts, improve machining efficiency and reduce machining costs. Existing tool wear monitoring and prediction methods mainly adopt neural network model with fixed architecture, which rely on the researchers’ experience and cannot guarantee accuracy under different cutting conditions. This paper proposes a tool wear prediction method based on network architecture search. which can learn a suitable network structure under different cutting conditions. Experiments shows sufficient improvement in the accuracy of predicting tool wear compared with existing methods.