Generative and isoparametric geometric modeling of large-scale and multiscale microstructures
For researchers and engineers in additive manufacturing and geometric modeling, this method addresses the scalability challenge of representing massive geometric details across scales, offering a more compact and consistent representation than existing explicit or implicit methods.
This paper proposes a new geometric modeling method for large-scale and multiscale microstructures that treats modeling as an on-demand generative process, using an extended volumetric Catmull-Clark spline representation (ExVCC) with local refinement and isoparametric cross-scale association. The method enables compact encoding and efficient evaluation of massive geometric details, demonstrated through examples and comparisons.
As additive manufacturing advances toward higher printing resolution and larger build volumes, microstructures can be designed with finer geometric features over larger physical domains. This trend poses a fundamental challenge for geometric modeling: massive geometric details must be represented compactly, while their associations across scales must be maintained consistently.Existing methods cannot scale well to this requirement. Explicit representations suffer from prohibitive memory cost, and implicit representations remain compact only when microstructures admit analytic, periodic, or otherwise concise procedural descriptions. This paper proposes a new geometric modeling method that treats microstructure modeling as an on-demand generative process, rather than requiring the full instantiation of all geometric details. We first develop ExVCC, an extended volumetric Catmull-Clark spline representation that enables local spline refinement to go beyond tensor-product topology. Built on ExVCC, we introduce new shape-coding schemes and refinement rules that compactly encode large-scale geometric details and enable their localized evaluation through on-demand hierarchical refinement. To model geometric details across scales, we further propose an isoparametric representation in which details across scales are defined over a shared parametric domain using the same family of spline bases of ExVCC. This formulation turns the ExVCC's spline refinement hierarchy into a common framework for geometry encoding, on-demand generation, and cross-scale association, allowing geometric modifications to propagate automatically across scales. The effectiveness of the proposed method is demonstrated through a series of examples and comparisons.