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

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 discretization, while maintaining the computational efficiency of coarse mesh optimization. Our approach can reduce maximum equivalent stress values compared to conventional methods. This reduction is 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. • Novel SDF-based method extracts smooth geometries from SIMP topology optimization. • Generated geometries have smooth boundaries and preserve volume precisely. • Proposed approach works across irregular discretizations and element types. • Implicit boundary representation enables direct CAD/manufacturing format export. • Method source codes, benchmarks and examples are made publicly available.