Stochastic Geometry Models for Texture Synthesis of Machined Metallic Surfaces: Sandblasting and Milling
This work addresses the need for representative training data in visual surface inspection systems for manufacturing, though it is incremental as it applies existing texture synthesis methods to specific surface types.
The paper tackled the problem of insufficient real training data for defect detection algorithms on machined metallic surfaces by developing stochastic texture models for sandblasted and milled surfaces based on topography measurements, enabling synthetic data generation for a digital twin environment.
Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.