The optical perception of high precision, fine grinded surfaces plays a major role, especially in various consumer goods. The very complex manufacturing process of many of these products consists of a variety of parameters such as feed rate, cutting speed, grinding disc, cutting fluid, contact force or process time. The change of a parameter setting has a direct effect on the surface topography. By varying the process parameters of the high precision fine grinding process, a variety of cutlery samples with different surface topographies are manufactured. Surface topographies of grinded surfaces are measured by the use of classical methods (roughness measuring device, gloss measuring device, spectrophotometer). To improve the conventional methods, a new image processing analysis approach is needed to get a faster and more cost-effective analysis of produced surfaces.
In this study, the image analysis of the fine grinded surface is performed. For this reason, a customized test rig is designed in the first step in order to photograph surface images. The gathered image material serves as the training data for the Machine Learning (ML) analysis. The image of each grinded sample is analysed concerning the measured roughness (Ra and Rz) subjected to the corresponding production parameters and related to Computer Vision (CV) techniques. Based on ML algorithms, rules for identification and classification of similar surfaces are established.