Laser powder bed fusion of bio-inspired metamaterials for energy absorption applications: A review
Bibliographic record
Abstract
A significant amount of research has been done in the last few decades to reduce the risk of injury for occupants and the structures that are subjected to impact loading. Metamaterials have been proven to be useful in energy-absorbing structures to improve a structure's crashworthiness performance by reducing the negative impacts during collision. The metal additive manufacturing industry, especially Laser Powder Bed Fusion (LPBF), has made it easier to produce complex metamaterials with remarkable mechanical characteristics like lightweight, high specific strength, and effective energy absorption. This review paper investigates the transformative potential of bio-inspired metamaterial designs, which are additively manufactured using LPBF machines, for use in protective energy-absorbing structures. First, biomimicry in engineering is briefly discussed. The review focuses on the energy absorption performance of different designs, like thin-walled structures and different bio-inspired metamaterials. It discusses the effects of base metal, process conditions, and manufacturing defects. Optimization methods to enhance the design and crashworthiness of these bio-inspired energy absorbers are investigated. Various characterization methodologies including experimental techniques and numerical simulations, are highlighted, with a particular emphasis on integrating manufacturing defects into simulations. Finally, possible applications and future trends in aerospace, automotive, construction, and medical applications are reviewed. Despite decades of research into energy absorbers, there remains a lack of a comprehensive review on the use of additively manufactured metamaterials as energy absorbers, particularly those inspired by nature. This review paper addresses this gap by examining recent studies in the field, assessing the effectiveness of various bio-inspired metamaterial designs, their crashworthiness, and the associated characterization methods under different loading scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".