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Peer-reviewedCC BYVirtual and Physical Prototyping

Machine learning-aided design of metallic mechanical metamaterials: fabrication-aware modelling, property prediction, and inverse design

  • Heyuan Huang1
  • Gang Xue1
  • Zhicheng Dong1
  • Ben Jia2,1
  • Dong Guo3
  • Yue Wang4,5
  • Junjie Zhou2
5 affiliations
  1. 1Northwestern Polytechnical University
  2. 2Institute of Nuclear and New Energy Technology, Tsinghua University
  3. 3Xi’an ASN Technology Group Co. Ltd
  4. 4Hebei University of Technology
  5. 5Xi’an University of Technology
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Abstract

Machine learning (ML) has emerged as a powerful framework for metallic mechanical metamaterials by helping disentangle the complex interplay among digital architectures, additive manufacturing processes, and as-built defects. By circumventing costly trial-and-error physical prototyping, ML accelerates the design and qualification pipelines, fully unlocking the intrinsic metallic advantages and topology-governed mechanical behaviours of these advanced structures. This review first outlines the major classes of metallic mechanical metamaterials alongside their representative additive manufacturing technologies. Subsequently, we explore how ML bridges the gap between design intelligence and manufacturing reality by actively accounting for nominal topologies, process sensitivities, and fabrication-induced deviations. Specifically, we detail how data-driven approaches facilitate the forward prediction of mechanical responses and the inverse design of complex architectures. Finally, current challenges and future directions are discussed, with emphasis on data quality, validation level, structural representation, and the development of a closed-loop digital-to-physical workflow for metallic mechanical metamaterials.

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Cite

BibTeXRIS
BibTeX
@article{huang2026machine,
  title = {{Machine learning-aided design of metallic mechanical metamaterials: fabrication-aware modelling, property prediction, and inverse design}},
  author = {Heyuan Huang and Gang Xue and Zhicheng Dong and Ben Jia and Dong Guo and Yue Wang and Junjie Zhou},
  journal = {Virtual and Physical Prototyping},
  year = {2026},
  month = may,
  doi = {10.1080/17452759.2026.2668249},
  url = {https://doi.org/10.1080/17452759.2026.2668249},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Machine learning-aided design of metallic mechanical metamaterials: fabrication-aware modelling, property prediction, and inverse design
AU  - Heyuan Huang
AU  - Gang Xue
AU  - Zhicheng Dong
AU  - Ben Jia
AU  - Dong Guo
AU  - Yue Wang
AU  - Junjie Zhou
PY  - 2026
DA  - 2026/05/18
JO  - Virtual and Physical Prototyping
DO  - 10.1080/17452759.2026.2668249
UR  - https://doi.org/10.1080/17452759.2026.2668249
AB  - Machine learning (ML) has emerged as a powerful framework for metallic mechanical metamaterials by helping disentangle the complex interplay among digital architectures, additive manufacturing processes, and as-built defects. By circumventing costly trial-and-error physical prototyping, ML accelerates the design and qualification pipelines, fully unlocking the intrinsic metallic advantages and topology-governed mechanical behaviours of these advanced structures. This review first outlines the major classes of metallic mechanical metamaterials alongside their representative additive manufacturing technologies. Subsequently, we explore how ML bridges the gap between design intelligence and manufacturing reality by actively accounting for nominal topologies, process sensitivities, and fabrication-induced deviations. Specifically, we detail how data-driven approaches facilitate the forward prediction of mechanical responses and the inverse design of complex architectures. Finally, current challenges and future directions are discussed, with emphasis on data quality, validation level, structural representation, and the development of a closed-loop digital-to-physical workflow for metallic mechanical metamaterials.
SN  - 1745-2759
SN  - 1745-2767
ER  -