Automated Surface Patch Extraction for 3D Printing Qualification

Abstract

Surface patches have been utilized to reduce shape complexity in qualification of product geometric quality. However, specifying surface patches case-by-case is impractical for qualifying 3D-printed products with complicated freeform designs. Automating patch extraction must overcome the issue of potentially infinite variety of surface patches. To achieve dimension reduction for automated qualification, this work defines and characterizes surface patches using Laplace-Beltrami (LB) operator and critical points to capture a finite number of patch deviation patterns. The deviation-pattern-driven characterization enables the automated determination of patch centroids through active landmark selection. The patch sizes are determined using the LB operator within a changepoint detection formulation. To verify the finite dimensionality of patch types, patches extracted from product designs are clustered based on geometric dissimilarity that is quantified by wave kernel and curvature signatures. To verify that extracted patches capable of capturing deviation patterns in printed products, we derive patch deviation signatures that are invariant to printing covariates to facilitate product qualification. Analysis of actual 3D-printed freeform products demonstrates the efficacy of the developed methodology by comparing with existing approaches. It also shows the potential of inferring deviation patterns of a new design using the surface patch characterization and extraction approach. Note to Practitioners—Qualification of geometric quality for products with complex geometries relies on specifying regions of interest in the form of features or surface patches. In 3D printing, the potentially infinite variety of product designs requires manual specification of context-dependent surface patches for new and unseen designs. This work establishes an automated framework to reduce the dimensionality of qualification by specifying finite types of surface patches and automating their extraction across an infinite variety of designs. This approach streamlines the qualification process and will support the inference and learning of geometric quality of previously unseen designs.