In sheet metal production, the quality of a cut edge determines the quality of the cut itself. Quality criteria such as the roughness, the edge slope, and the burr height are of decisive importance for further application and quality determination. In order to be able to determine
these criteria analytically, the depth information of the edge must be determined at great expense. The current methods for obtaining the depth information are very time-consuming, require laboratory environments and are therefore not suitable for a fast evaluation of the quality criteria.
Preliminary work has shown that it is possible to make robust and accurate statements about the roughness of a cut edge based on images when using an industrial camera with a standard lens and diffuse incident light, if the model used for this purpose has been trained on appropriate images.
In this work, the focus is on the illumination scenarios and their influence on the prediction quality of the models. Images of cut edges are taken under different defined illumination scenarios and it is investigated whether a comprehensive evaluation of the cut edges on the evaluation criteria
defined in standards is possible under the given illumination conditions. The results of the obtained model predictions are compared with each other in order to make a statement about the importance of the illumination scenario. In order to investigate the possibility of a mobile low-cost
evaluation of cut edges, cheap hardware components for illumination and a smartphone for image acquisition are used.