Realizing on-machine tool wear monitoring through integration of vision-based system with CNC milling machine

Abstract

The paper systematically realizes a vision-based on-machine Tool Wear Monitoring (TWM) system for integration with a CNC milling machine to identify tool wear states during machining hard materials such as Inconel 718 (IN718). The proposed TWM system consists of a microscope-based image acquisition setup mounted inside the machine and pre-defined programmed motions to capture high-resolution images of worn side cutting edges. The pre-trained Convolutional Neural Network (CNN) model, Efficient-Net-b0, was developed using transfer learning to identify tool wear states utilizing labeled image datasets generated in the machining environment. The labeled datasets were generated systematically by intermittently capturing images during IN718 machining at varying surface speeds. The present study considered four tool wear states, Flank, Flank+BUE, Flank+Face, and Chipping, representing combinations of abrasion, adhesion, diffusion, and fracture wear mechanisms. The effectiveness of the proposed TWM system was evaluated by identifying the wear state for previously unseen test datasets. The results showed that the TWM system can identify tool wear states with an accuracy of 94.11%. Furthermore, the study analyzes reasons for misclassifications using feature maps and classification probability scores to achieve better prediction abilities.