Vision-Based Surface Roughness Evaluation System For End Milling

This paper presents a vision-based surface roughness evaluation system for end-milled metals, addressing digital reconstruction and calibration of inspected surfaces, and quantitative and qualitative evaluation of surface texture. Specimens with different levels of surface roughness are machined, and a comparison between stylus-based and vision-based measurements is performed while using standard roughness parameters. The vison-based results vary among 9% and 11% compared to the stylus-based ones, which is a minor error to trade-off for faster measurements. Furthermore, surface texture evaluation is performed by detecting the generated cusp lines and tool marks on the machined surface. The tool marks' distribution is analysed in order to determine whether the machining is performed under optimal cutting conditions. Results show that under optimal cutting conditions, the detected tool marks are normally distributed along the feed direction and the distance between two consecutive tool marks does not vary significantly. Based on the proposed methods software is implemented that enables the three-dimensional reconstruction, calibration and evaluation of the inspected surface.