Two-Scale Topology Optimization with Microstructures
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Abstract (arXiv)
In this paper we present a novel two-scale framework to optimize the structure and the material distribution of an object given its functional specifications. Our approach utilizes multi-material microstructures as low-level building blocks of the object. We start by precomputing the material property gamut -- the set of bulk material properties that can be achieved with all material microstructures of a given size. We represent the boundary of this material property gamut using a level set field. Next, we propose an efficient and general topology optimization algorithm that simultaneously computes an optimal object topology and spatially-varying material properties constrained by the precomputed gamut. Finally, we map the optimal spatially-varying material properties onto the microstructures with the corresponding properties in order to generate a high-resolution printable structure. We demonstrate the efficacy of our framework by designing, optimizing, and fabricating objects in different material property spaces on the level of a trillion voxels, i.e several orders of magnitude higher than what can be achieved with current systems.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2017.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 2; 2025: 2.
10 papers.
- 2021IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structurescited by 19
- 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Reviewcited by 17
- 2022t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learningcited by 9
- 2024Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterialscited by 7
- 2023Neural Metamaterial Networks for Nonlinear Material Designcited by 5
- 2021Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blendingcited by 4
- 2025eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructurescited by 4
- 2021Designing Composites with Target Effective Young's Modulus using Reinforcement Learningcited by 3
- 2024Multi-scale Topology Optimization using Neural Networkscited by 1
- 2025Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Compositescited by 1
- 2025eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures11 Jun 2025
- 2025Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites30 Apr 2025
- 2024Multi-scale Topology Optimization using Neural Networks11 Apr 2024
- 2024Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials24 Jan 2024
- 2023Neural Metamaterial Networks for Nonlinear Material Design15 Sep 2023
- 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Review1 Jul 2023
- 2022t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning21 Feb 2022
- 2021Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending1 Dec 2021
- 2021Designing Composites with Target Effective Young's Modulus using Reinforcement Learning7 Oct 2021
- 2021IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures3 Mar 2021