Technology Category
Search and Optimization: Mixed Discrete/Continuous SearchConstraint Satisfaction and Optimization: Mixed Discrete/Continuous OptimizationMachine Learning: Feature Construction/Reformulation
Smooth geometry extraction from SIMP topology optimization: Signed distance function approach with volume preservation
🤖 AI Summary
To address the challenge of simultaneously mitigating boundary roughness, volume distortion, and topological inaccuracies in post-processing density-based topology optimization results, this paper proposes a two-stage geometric reconstruction method based on the signed distance function (SDF). In the first stage, an isosurface preserving the original volume fraction is precisely extracted via SDF. In the second stage, radial basis functions (RBFs) are employed for local smoothing of the implicit surface, yielding a high-fidelity, continuously differentiable (C¹) boundary representation. Crucially, the method bypasses intermediate mesh conversion and directly generates implicit geometric models compatible with CAD and manufacturing formats. Experimental evaluation demonstrates that, compared to conventional thresholding, the proposed approach reduces maximum equivalent stress by 18%, significantly enhances C¹ continuity along boundaries, and fully preserves the optimized topological features.
Technology Category
Application Category
📝 Abstract
This paper presents a novel post-processing methodology for extracting high-quality geometries from density-based topology optimization results. Current post-processing approaches often struggle to simultaneously achieve smooth boundaries, preserve volume fraction, and maintain topological features. We propose a robust method based on a signed distance function (SDF) that addresses these challenges through a two-stage process: first, an SDF representation of density isocontours is constructed, which is followed by geometry refinement using radial basis functions (RBFs). The method generates smooth boundary representations that appear to originate from much finer discretizations while maintaining the computational efficiency of coarse mesh optimization. Through comprehensive validation, our approach demonstrates a 18% reduction in maximum equivalent stress values compared to conventional methods, achieved through continuous geometric transitions at boundaries. The resulting implicit boundary representation facilitates seamless export to standard manufacturing formats without intermediate reconstruction steps, providing a robust foundation for practical engineering applications where high-quality geometric representations are essential.
Problem
Research questions and friction points this paper is trying to address.
Extract smooth geometries from topology optimization results
Preserve volume fraction while maintaining topological features
Generate manufacturable boundary representations without intermediate steps
Innovation
Methods, ideas, or system contributions that make the work stand out.
SDF-based geometry extraction from density optimization
Two-stage refinement with RBF for smooth boundaries
Implicit representation enabling direct manufacturing export
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