Data-driven multi-objective optimization for lattice-based metamaterials

XIAO Lijun; ZHU Yanlin; SHI Gaoquan; LI Yinan; LI Runzhi; HUI Xulong; ZHANG Ruigang; SONG Weidong

doi:10.11883/bzycj-2025-0288

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Article Navigation > Explosion And Shock Waves > 2026 > Accepted Manuscript

XIAO Lijun, ZHU Yanlin, SHI Gaoquan, LI Yinan, LI Runzhi, HUI Xulong, ZHANG Ruigang, SONG Weidong. Data-driven multi-objective optimization for lattice-based metamaterials[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0288

Citation:

XIAO Lijun, ZHU Yanlin, SHI Gaoquan, LI Yinan, LI Runzhi, HUI Xulong, ZHANG Ruigang, SONG Weidong. Data-driven multi-objective optimization for lattice-based metamaterials[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0288

Citation:

XIAO Lijun, ZHU Yanlin, SHI Gaoquan, LI Yinan, LI Runzhi, HUI Xulong, ZHANG Ruigang, SONG Weidong. Data-driven multi-objective optimization for lattice-based metamaterials[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0288

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Data-driven multi-objective optimization for lattice-based metamaterials

doi: 10.11883/bzycj-2025-0288

1.
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2.
Jinxi Industries Group Co., Ltd, Taiyuan 030027, Shanxi, China
3.
National Key Laboratory of Strength and Structural Integrity, Aircraft Strength Research Institute of China, Xi’an 710065, Shaanxi, China

Received Date: 2025-09-01
Rev Recd Date: 2025-11-24

Available Online: 2025-12-02

Abstract

Abstract

Strut-based lattice metamaterials are a category of ultra-lightweight, load-bearing, and energy-absorbing materials with broad application prospects in fields such as impact protection, aerospace engineering, and lightweight structural design. Benefiting from their unique periodic architectures and adjustable meso-structural parameters, these materials exhibit exceptional mechanical tunability and multifunctional potential. However, due to the extensive parameter space of mesoscopic configurations and the highly nonlinear correlation between the structural geometry and the mechanical response, the optimization of mechanical performance for lattice metamaterials remains a formidable challenge. Based on the meso-structural characteristics of strut-based lattice metamaterials, an efficient rapid digital modeling method was proposed. A Python script coupled with Abaqus software was utilized for the rapid modeling of truss lattice metamaterials and fast calculations about the mechanical properties of the metamaterials. Based on the calculation results, a machine learning dataset was constructed. Three types of truss lattice structures were randomly selected and additively manufactured. Quasi-static compression tests on these three lattice structures were conducted using a universal testing machine to verify the reliability of the dataset. Subsequently, an artificial neural network (ANN) was trained to rapidly predict the mechanical properties of the truss lattice metamaterials. Focusing on the load-bearing capacity, energy absorption capability, and the concurrent optimization of both, a non-dominated sorting genetic algorithm II (NSGA-Ⅱ) was employed. The well-trained ANN served as a surrogate model embedded within NSGA-II. Lattice configurations that exhibited high load-bearing capacity and superior energy absorption characteristics were generated by the optimization process. These configurations also achieved a balance between load-bearing and energy-absorption performance, facilitating the optimization design of truss lattice metamaterials. Additionally, simulation validations confirmed the reliability of the optimization outcomes, demonstrating the effectiveness of integrating ANN with evolutionary algorithms for the advanced design of metamaterials. By integrating machine learning with numerical simulations, the computational cost of optimization design was effectively reduced, offering support for the rapid performance optimization and customized design of complex lattice metamaterials.
- lattice metamaterial,
- machine learning,
- genetic algorithm,
- multi-objective optimization,
- additive manufacturing

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References(27)

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