Synthetic Defect Geometries of Cast Metal Objects Modeled via 2d Voronoi Tessellations
This work addresses the need for high-quality synthetic data in industrial quality control, particularly for non-destructive testing methods, though it is incremental as it builds on existing rule-based approaches.
The paper tackles the problem of generating synthetic training data for automated defect detection in cast metal objects by developing parametric methods to model 3D defect geometries using 2D Voronoi tessellations, enabling the creation of variable and arbitrarily large datasets with pixel-perfect annotations.
In industry, defect detection is crucial for quality control. Non-destructive testing (NDT) methods are preferred as they do not influence the functionality of the object while inspecting. Automated data evaluation for automated defect detection is a growing field of research. In particular, machine learning approaches show promising results. To provide training data in sufficient amount and quality, synthetic data can be used. Rule-based approaches enable synthetic data generation in a controllable environment. Therefore, a digital twin of the inspected object including synthetic defects is needed. We present parametric methods to model 3d mesh objects of various defect types that can then be added to the object geometry to obtain synthetic defective objects. The models are motivated by common defects in metal casting but can be transferred to other machining procedures that produce similar defect shapes. Synthetic data resembling the real inspection data can then be created by using a physically based Monte Carlo simulation of the respective testing method. Using our defect models, a variable and arbitrarily large synthetic data set can be generated with the possibility to include rarely occurring defects in sufficient quantity. Pixel-perfect annotation can be created in parallel. As an example, we will use visual surface inspection, but the procedure can be applied in combination with simulations for any other NDT method.