Smooth geometry extraction from SIMP topology optimization: Signed distance function approach with volume preservation

TL;DR

This paper introduces a signed distance function-based post-processing method for topology optimization that produces smooth, volume-preserving geometries with high fidelity, enabling better manufacturing and engineering applications.

Contribution

A novel two-stage SDF and RBF-based approach for extracting high-quality, volume-preserving geometries from density-based topology optimization results.

Findings

Achieved 18% reduction in maximum equivalent stress.

Produced smooth boundaries comparable to finer discretizations.

Facilitated direct export to manufacturing formats.

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.