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<p>Protective energy-absorbing components must reconcile lightweight design, high dissipation capacity, and reliable performance under diverse loading conditions. This study presents a novel origami unit, whose manufacturability was verified via kinematic analysis and whose baseline quasi-static axial compression performance exceeds that of two benchmark origami geometries by up to 52% in total energy absorption (<i>E</i><sub><i>abs</i></sub>) and 62% in specific energy absorption (<i>SEA</i>). A hybrid optimization framework integrating XGBoost machine learning surrogate modeling and NSGA-II multi-objective algorithms efficiently identified Pareto-optimal geometric parameters, achieving more than 97% prediction accuracy for key performance metrics. Embedding aluminum-foam-filled into the optimized origami cores produced a sandwich panel whose energy absorption (<i>E</i><sub><i>abs</i></sub>), specific energy absorption (<i>SEA</i>), and crush force efficiency (<i>CFE)</i> surged by approximately 619%, 101%, and 349%, respectively, compared to the unfilled structure. Finite-element simulations accurately capture deformation stages and confirm that foam filling yields more uniform hinge formation and markedly enhanced stability. The findings provide a novel design concept and methodology for advanced protective sandwich structures in both civilian and military applications.</p>

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Hybrid-optimized ant-nest origami for high-performance foam-filled energy absorbing sandwich structures under quasi-static and impact loading

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Jiahui Liu,
  • Zhiqiang Zou,
  • Yongkui Wu,
  • Kang Gao,
  • Huiyin Huang

摘要

Protective energy-absorbing components must reconcile lightweight design, high dissipation capacity, and reliable performance under diverse loading conditions. This study presents a novel origami unit, whose manufacturability was verified via kinematic analysis and whose baseline quasi-static axial compression performance exceeds that of two benchmark origami geometries by up to 52% in total energy absorption (Eabs) and 62% in specific energy absorption (SEA). A hybrid optimization framework integrating XGBoost machine learning surrogate modeling and NSGA-II multi-objective algorithms efficiently identified Pareto-optimal geometric parameters, achieving more than 97% prediction accuracy for key performance metrics. Embedding aluminum-foam-filled into the optimized origami cores produced a sandwich panel whose energy absorption (Eabs), specific energy absorption (SEA), and crush force efficiency (CFE) surged by approximately 619%, 101%, and 349%, respectively, compared to the unfilled structure. Finite-element simulations accurately capture deformation stages and confirm that foam filling yields more uniform hinge formation and markedly enhanced stability. The findings provide a novel design concept and methodology for advanced protective sandwich structures in both civilian and military applications.