De-homogenization of optimal multi-scale 3D topologies
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Abstract (arXiv)
This paper presents a highly efficient method to obtain high-resolution, near-optimal 3D topologies optimized for minimum compliance on a standard PC. Using an implicit geometry description we derive a single-scale interpretation of optimal multi-scale designs on a very fine mesh (de-homogenization). By performing homogenization-based topology optimization, optimal multi-scale designs are obtained on a relatively coarse mesh resulting in a low computational cost. As microstructure parameterization we use orthogonal rank-3 microstructures, which are known to be optimal for a single loading case. Furthermore, a method to get explicit control of the minimum feature size and complexity of the final shapes will be discussed. Numerical examples show excellent performance of these fine-scale designs resulting in objective values similar to the homogenization-based designs. Comparisons with well-established density-based topology optimization methods show a reduction in computational cost of 3 orders of magnitude, paving the way for giga-scale designs on a standard PC.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Oct 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Reviewcited by 17
- 2021De-homogenization using Convolutional Neural Networkscited by 7
- 2022GM-TOuNN: Graded Multiscale Topology Optimization using Neural Networkscited by 3
- 2024Consistent machine learning for topology optimization with microstructure-dependent neural network material modelscited by 3
- 2024Consistent machine learning for topology optimization with microstructure-dependent neural network material models25 Aug 2024
- 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Review1 Jul 2023
- 2022GM-TOuNN: Graded Multiscale Topology Optimization using Neural Networks14 Apr 2022
- 2021De-homogenization using Convolutional Neural Networks10 May 2021