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Abstract Spinodoid architected materials have drawn significant attention due to their unique nature in stochasticity, aperiodicity, and bi-continuity. Compared to classic periodic truss-, beam-, and plate-based lattice architectures, spinodoids are insensitive to manufacturing defects, scalable for high-throughput production, functionally graded by tunable local properties, and material failure resistant due to low-curvature morphology. However, the design of spinodoids is often hindered by the curse of dimensionality with an extremely large design space of spinodoid types, material density, orientation, continuity, and anisotropy. From a design optimization perspective, while genetic algorithms are often beyond the reach of computing capacity, gradient-based topology optimization is challenged by the intricate mathematical derivation of gradient fields with respect to various spinodoid parameters. To address such challenges, we propose a data-driven multiscale topology optimization framework. Our framework reformulates the design variables of spinodoid materials as the parameters of neural networks, enabling automated computation of topological gradients. Additionally, it incorporates a Gaussian Process surrogate for spinodoid constitutive models, eliminating the need for repeated computational homogenization and enhancing the scalability of multiscale topology optimization. Compared to ‘black-box’ deep learning approaches, the proposed framework provides clear physical insights into material distribution. It explicitly reveals why anisotropic spinodoids with tailored orientations are favored in certain regions, while isotropic spinodoids are more suitable elsewhere. This interpretability helps to bridge the gap between data-driven design with mechanistic understanding. To this end, we test our design framework on several numerical experiments. We find our multiscale spinodoid designs with controllable anisotropy achieve better performance than single-scale isotropic counterparts, with clear physics interpretations.
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Computational Synthesis of Large-Scale Three-dimensional Heterogeneous Lattice Structures
This paper describes a methodology for designing the material distribution and orientation of three-dimensional non-uniform (heterogeneous) lattice structures. Recent advances in additive manufacturing enable fabrication across multiple length scales. Homogenization-based design optimization and the subsequent projection of the optimized design facilitate the synthesis of large-scale microstructures that form lightweight bionic designs. The main aspects of this research are (a) the construction, homogenization-based optimization, and projection of two types of lattices with different degrees of anisotropy and (b) the parallelization of the analysis, optimization, and projection framework in order to handle large-scale meshes and obtain high-resolution, heterogeneous lattice structures. Cubic and octet-truss lattices were selected to demonstrate the ability of the framework to design different types of lattices. A quadcopter arm and an internal wing structure were designed using the optimization and projection framework, verifying its capability to synthesize heterogeneous lattice structures for complex design domains. The ability to change the complexity of optimized microlattices using the characteristic parameters of the lattice is discussed. The relationship between the lattice anisotropy and the optimized, smoothed orientation is investigated, and the optimized design for each lattice is compared with those obtained using conventional design optimization procedures.
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- Award ID(s):
- 1847133
- PAR ID:
- 10341318
- Editor(s):
- J.A. Ekaterinaris
- Date Published:
- Journal Name:
- Aerospace science and technology
- Volume:
- 120
- ISSN:
- 1626-3219
- Page Range / eLocation ID:
- 107258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/doi.org/10.1016/j.ast.2021.107258
