Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning

Abstract

Rapid tool wear from milling TC18 (Ti-5Al-5Mo-5V-1Cr-1Fe) leads to increased surface deterioration and manufacturing costs. Here, a real-life tool wear experiment was introduced, and the three stages of tool wear were analyzed in detail according to the tool wear micro-topography and chemical elements. In the initial and normal stage, the tool wear was slow because of the protection of the adhesive titanium layer and dense alumina film. Diffusion wear and oxidation wear occurred until the sever wear stage. Based on the above wear mechanism determination, after acquiring the real time cutting force, the tool wear prediction models were established using a convolutional bi-directional long short-term memory networks (CNN + BILSTM) and a convolutional bi-directional gated recurrent unit (CNN + BIGRU). The results show that the errors of the predicted minimum values are all within 8%, demonstrating that the deep learning method offers a new and promising approach for in monitoring tool wear on-line.