An M-VCUT level set-based data-driven model of microstructures and optimization of two-scale structures

Abstract

The optimization of two-scale structures can adapt to the different needs of materials in various regions by reasonably arranging different microstructures at the macro scale, thereby considerably improving structural performance. Here, a multiple variable cutting (M-VCUT) level set-based data-driven model of microstructures is presented, and a method based on this model is proposed for the optimal design of two-scale structures. The geometry of the microstructure is described using the M-VCUT level set method, and the effective mechanical properties of microstructures are computed by the homogenization method. Then, a database of microstructures containing their geometric and mechanical parameters is constructed. The two sets of parameters are adopted as input and output datasets, and a mapping relationship between the two datasets is established to build the data-driven model of microstructures. During the optimization of two-scale structures, the data-driven model is used for macroscale finite element and sensitivity analyses. The efficiency of the analysis and optimization of two-scale structures is improved because the computational costs of invoking such a data-driven model are much smaller than those of homogenization.

Graphical abstract

Keywords

two-scale structure / structural optimization / M-VCUT level set / homogenization / radial basis function / data-driven

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 12272144). The authors also would like to thank Krister Svanberg for providing the MMA codes.

Conflict of Interest

Tielin SHI and Qi XIA are members of the Editorial Board of Frontiers of Mechanical Engineering, who were excluded from the peer-review process and all editorial decisions related to the acceptance and publication of this article. Peer-review was handled independently by the other editors to minimize bias.

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2024 Higher Education Press