A Multifeature Approach to Tool Wear Estimation Using 3D Workpiece Surface Texture Parameters

Abstract

This work presents a new way to determine the condition of a cutting tool based on 3D texture parameters of workpiece surface. Recently, a laser holographic interferometer has been developed to rapidly measure a large workpiece surface and generate a 3D surface height map with micron level accuracy. This technique enables online surface measurement for machined workpieces. By measuring and analyzing workpiece surface texture, the interaction between the tool’s cutting edges and the workpiece surface can be extracted as a spatial signature. It can then be used as a warning sign for tool change because the workpiece produced by a heavily worn tool exhibits more irregularities in its surface texture than that produced by a normal tool. Multiple texture parameters such as image intensity histogram distribution parameter, 3D peak-to-valley height, and 3D surface waviness parameter are employed to indicate the onset of severe tool wear. In this work, aluminum (Al308) and compacted graphite iron parts were machined by a polycrystalline diamond insert and a multiphase coated tungsten carbide insert, respectively. After that, multiple 3D surface texture features of workpieces samples under different phases of tool wear were analyzed in order to assess tool wear conditions. The experimental results verify that these surface texture features can be used as good indicators for online tool wear monitoring.