Optimizing Miura Origami for Enhanced Energy Absorption: A Multi-Objective Approach

This research addresses the critical challenge of improving structural energy absorption in crash scenarios. The study investigates the optimization of Miura origami-inspired thin-walled structures for vehicle safety systems. The structures were fabricated using AlSi10Mg aluminum alloy through selective laser melting (SLM) 3D printing technology. A frontal crash test scenario was simulated in ANSYS to assess structural performance under impact loading. The study applied a multi-objective optimization approach, combining the Taguchi method, principal component analysis (PCA), and composite desirability analysis, to evaluate the influence of design parameters such as wall thickness, number of sides, surface diameter, and number of segments. Based on the analysis of variance (ANOVA) results, wall thickness was identified as the most dominant factor, contributing 67 % to energy available (EA), while surface diameter and number of sides contributed 14 % and 7 %, respectively. The number of segments had a minor effect, contributing only 0.5 % to EA. The optimal configuration t3n1d3M2 was further validated through compression testing of 3D-printed prototypes, with deformation behavior analyzed using digital image correlation (DIC). The experimental findings closely matched simulation outcomes, confirming the robustness of the proposed optimization framework. These results provide valuable insights into integrating origami-inspired geometries and advanced additive manufacturing for enhanced crashworthiness in automotive structures.

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Publication Details

Journal
Journal of Engineering Sciences
Published
2025-01-01
DOI
https://doi.org/10.21272/jes.2025.12(2).d3
Citations
1
Primary Topic
Advanced Materials and Mechanics
Type
article
Field-Weighted Citation Impact
0.71
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article

Optimizing Miura Origami for Enhanced Energy Absorption: A Multi-Objective Approach

Anindito Purnowidodo, Redi Bintarto, Moch. Agus Choiron, Imam Kusyairi
1 citations
Journal of Engineering Sciences
Advanced Materials and Mechanics
0.71
article

Optimizing Miura Origami for Enhanced Energy Absorption: A Multi-Objective Approach

Anindito Purnowidodo, Redi Bintarto, Moch. Agus Choiron, Imam Kusyairi
article en
1 citations

Abstract

This research addresses the critical challenge of improving structural energy absorption in crash scenarios. The study investigates the optimization of Miura origami-inspired thin-walled structures for vehicle safety systems. The structures were fabricated using AlSi10Mg aluminum alloy through selective laser melting (SLM) 3D printing technology. A frontal crash test scenario was simulated in ANSYS to assess structural performance under impact loading. The study applied a multi-objective optimization approach, combining the Taguchi method, principal component analysis (PCA), and composite desirability analysis, to evaluate the influence of design parameters such as wall thickness, number of sides, surface diameter, and number of segments. Based on the analysis of variance (ANOVA) results, wall thickness was identified as the most dominant factor, contributing 67 % to energy available (EA), while surface diameter and number of sides contributed 14 % and 7 %, respectively. The number of segments had a minor effect, contributing only 0.5 % to EA. The optimal configuration t3n1d3M2 was further validated through compression testing of 3D-printed prototypes, with deformation behavior analyzed using digital image correlation (DIC). The experimental findings closely matched simulation outcomes, confirming the robustness of the proposed optimization framework. These results provide valuable insights into integrating origami-inspired geometries and advanced additive manufacturing for enhanced crashworthiness in automotive structures.

Journal of Engineering SciencesVol. 12(2)
University of Brawijaya (ID)
Openalex Percentile: Top 27%
Advanced Materials and Mechanics
0.71
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Optimizing Miura Origami for Enhanced Energy Absorption: A Multi-Objective Approach — Anindito Purnowidodo, Redi Bintarto, et al. · Journal of Engineering Sciences (2025) | TGRS Research Map | TGRS