Machine learning-driven low-velocity impact response prediction and multi-objective optimization of origami metamaterial sandwich

HAN Sihao; LI Chunlei; SU Buyun; JING Lin; HAN Qiang; YAO Xiaohu

doi:10.11883/bzycj-2025-0282

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HAN Sihao, LI Chunlei, SU Buyun, JING Lin, HAN Qiang, YAO Xiaohu. Machine learning-driven low-velocity impact response prediction and multi-objective optimization of origami metamaterial sandwich[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0282

Citation:

HAN Sihao, LI Chunlei, SU Buyun, JING Lin, HAN Qiang, YAO Xiaohu. Machine learning-driven low-velocity impact response prediction and multi-objective optimization of origami metamaterial sandwich[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0282

Citation:

HAN Sihao, LI Chunlei, SU Buyun, JING Lin, HAN Qiang, YAO Xiaohu. Machine learning-driven low-velocity impact response prediction and multi-objective optimization of origami metamaterial sandwich[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0282

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Machine learning-driven low-velocity impact response prediction and multi-objective optimization of origami metamaterial sandwich

doi: 10.11883/bzycj-2025-0282

1.
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, Guangdong, China
2.
College of Aeronautics and Astronautics, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
3.
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Received Date: 2025-08-27
Rev Recd Date: 2025-11-06

Available Online: 2025-11-18

Abstract

Abstract

Inspired by the hybrid topology design that integrates Miura origami and star-shaped honeycomb, this study proposes a novel origami metamaterial sandwich and employs machine learning to predict low-velocity impact response and perform multi-objective optimization. Through drop-weight impact experiments and finite element simulations, the dynamic mechanical response and deformation failure modes of the sandwich under low-velocity impact are systematically investigated. The results demonstrate that the origami-inspired topologies effectively transform the instantaneous complete fracture of traditional honeycombs into progressive crushing failure, thereby significantly enhancing impact resistance. Subsequently, a residual connection-enhanced deep learning model is developed, enabling rapid and precise end-to-end prediction of the complete low-velocity impact response, with computational efficiency substantially surpassing that of finite element simulations. Parameterized analysis based on this model reveals the regulatory mechanisms of key angle parameters on impact response and effective density. Particularly, angle variations induce a load redistribution phenomenon between panel tension-compression deformation and crease bending deformation, allowing the metamaterial to switch between bearing and buffering protective functions. This provides a mechanism basis for actively controlling impact response and failure modes. Furthermore, by integrating reinforcement learning and Pareto front analysis, the trained deep learning model served as a surrogate model to achieve lightweight multi-objective optimization tailored for load-bearing and impact-mitigation protection requirements. At similar effective densities, the metamaterial enables broad-range tuning of peak force, offering significant advantages for developing customized protective structures for diverse scenarios. This research not only establishes a solid foundation for creating customizable high-performance impact protection structures but also advances the field toward a new paradigm of intelligent, on-demand design.
- origami metamaterial,
- sandwich,
- dynamic mechanical response,
- machine learning,
- multi-objective optimization

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References(30)

References

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