Automated and Accurate Geometry Extraction and Shape Optimization of 3D Topology Optimization Results
Marco K. Swierstra, Deepak K. Gupta, Matthijs Langelaar

TL;DR
This paper introduces an automated method combining level-set geometry extraction and shape optimization to produce smooth, accurate 3D topologies from density-based results, improving integration with CAD tools.
Contribution
It presents a novel fully automated pipeline that extracts and refines 3D geometries from topology optimization results using level-set functions and local analysis.
Findings
Produces highly smooth and accurate geometries
Effective in 2D and 3D examples
Enhances CAD integration of TO results
Abstract
Designs generated by density-based topology optimization (TO) exhibit jagged and/or smeared boundaries, which forms an obstacle to their integration with existing CAD tools. Addressing this problem by smoothing or manual design adjustments is time-consuming and affects the optimality of TO designs. This paper proposes a fully automated procedure to obtain unambiguous, accurate and optimized geometries from arbitrary 3D TO results. It consists of a geometry extraction stage using a level-set-based design description involving radial basis functions, followed by a shape optimization stage involving local analysis refinements near the structural boundary using the Finite Cell Method. Well-defined bounds on basis function weights ensure that sufficient sensitivity information is available throughout the shape optimization process. Our approach results in highly smooth and accurate optimized…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopology Optimization in Engineering · Advanced Multi-Objective Optimization Algorithms · Advanced Numerical Analysis Techniques
