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.

Geometric Error Modeling and Monitoring of the 3D Surface by Gaussian Correlation Model Geometric error represents the deviation of the actual surface shape of a machined part from its ideal shape, which directly affects the quality of the 3D surface. Modeling and monitoring geometric error can reveal quality information of the 3D surface for evaluating the stability of the production process. Advanced measurement technology allows for a large number of measurement points to express geometric errors in close detail, but results in the problem of spatial correlations. In this study, the Gaussian correlation model is employed to represent the spatial correlations and to model the geometric errors of the 3D surface. A new statistic is established to monitor the surface errors in phase II. The proposed model is applicable to any 3D surface. We use the model to monitor a 3D surface manufacturing process and compare its performance with other existing methods through simulation.