Tool wear estimation and visualization using image sensors in micro milling manufacturing

Abstract

This paper presents a reliable machine vision system to automatically estimate and visualize tool wear in micro milling manufacturing. The estimation of tool wear is very important for tool monitoring systems and image sensors configure a cheap and reliable solution. This system provides information to decide whether a tool should be replaced so the quality of the machined piece is ensured and the tool does not collapse. In the method that we propose, we first delimit the area of interest of the micro milling tool and then we delimit the worn area. The worn area is visualized and estimated while errors are computed against the ground truth proposed by experts. The method is mainly based on morphological operations and k-means algorithm. Other approaches based on pure morphological operations and on Otsu multi threshold algorithms were also tested. The obtained result (a harmonic mean of precision and recall 90.24 (±2.78)%) shows that the machine vision system that we present is effective and suitable for the estimation and visualization of tool wear in micro milling machines and ready to be installed in an on-line system.