Feature Selection Based on Visual Analytics for Quality Prediction in Aluminium Die Casting Digital transformation is regarded as a key enabler for quality management in the modern casting industry. By uncovering parameters that affect the casting part quality, a continuous process improvement could be enabled. In particular, a focus is laid on the explicability of the identified dependencies due to the acceptance of datadriven decisions by workers as well as to initiate tailored improvement measures. For this purpose, in this paper two visual analytics approaches are discussed for uncovering qualityaffecting parameters with the claim for a high degree of explicability xe2x80x93 on the one hand a methodfeatureheatmap that visualizes a multiple backward feature elimination and on the other hand a graphbased visualization of class association rules. An automotive industry use case demonstrates the approaches in the context of gravity die casting of aluminium knuckles.

Improvement of DataDriven 3D Surface Quality Inspection by Deformation Simulation The automated visual inspection of complexly shaped, stamped sheet metal is a challenging task. Modelbased methods are currently state of the art for surface quality inspection based on threedimensional surface measurements. Datadriven models allow the representation of arbitrary threedimensional surface shapes including possible tolerances and variation due to deformation. Providing a sufficient amount of training data while ensuring adequate data quality is a timeconsuming and laborous process. We propose a data driven threedimensional surface model for quality inspection that allows the integration of deformation simulation in order to increase variance in training data. The effects of simulated data to the model quality are evaluated and results are given for data sets of automobile sheet metal parts.