Research progress on machine learning-assisted inverse design of triply periodic minimal surface structures

YIN Xingpeng; LI Junhao; TANG Xinzhen; LI Zhou

doi:10.7513/j.issn.1004-7638.2025.05.014

Volume 46 Issue 5

Oct. 2025

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YIN Xingpeng, LI Junhao, TANG Xinzhen, LI Zhou. Research progress on machine learning-assisted inverse design of triply periodic minimal surface structures[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 133-144. doi: 10.7513/j.issn.1004-7638.2025.05.014

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YIN Xingpeng, LI Junhao, TANG Xinzhen, LI Zhou. Research progress on machine learning-assisted inverse design of triply periodic minimal surface structures[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 133-144. doi: 10.7513/j.issn.1004-7638.2025.05.014

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Research progress on machine learning-assisted inverse design of triply periodic minimal surface structures

doi: 10.7513/j.issn.1004-7638.2025.05.014

1.
Zhuzhou Gear Co., Ltd., Zhuzhou 412007, Hunan, China
2.
College of Mechanical and Electrical Engineering, Central South University, Changsha 410017, Hunan, China

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Received Date: 2025-06-13
Accepted Date: 2025-07-07
Rev Recd Date: 2025-07-02
Publish Date: 2025-10-30

Abstract

Abstract

Triply periodic minimal surface (TPMS) structures are a class of bio-inspired porous architectures generated through mathematical implicit functions, known for their continuous smooth surfaces, self-supporting characteristics, high specific strength, and excellent energy absorption capabilities. These features make TPMS structures particularly suitable for lightweight, high-performance materials such as titanium alloys used in aerospace and advanced manufacturing applications. This work provides a comprehensive review of the design methodologies and advancements in the mechanical performance analysis of TPMS metal structures, with particular emphasis on the geometric modeling algorithms and the construction strategies for gradient and combined structural configurations. It further synthesizes the current state of machine learning applications in the design of TPMS metal structure, encompassing forward machine learning approaches for performance prediction and inverse machine learning frameworks for goal-oriented structural design. The review concludes with a critical assessment of existing challenges in inverse design of TPMS metal structures, particularly regarding efficient mapping and dataset generation. Finally, it highlights the urgent need for deep integrating generative artificial intelligence (Generative AI) techniques with inverse modeling strategies to facilitate the practical engineering implementation of TPMS metal structure inverse design.
- triply periodic minimal surface (TPMS),
- machine learning,
- inverse design,
- performance prediction,
- metal porous structures

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References(74)

References

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