3D‐Printed Soft and Hard Meta‐Structures with Supreme Energy Absorption and Dissipation Capacities in Cyclic Loading Conditions
Bibliographic record
Abstract
The main objective of this article is to introduce novel 3D bio‐inspired auxetic meta‐structures printed with soft/hard polymers for energy absorption/dissipation applications under single and cyclic loading–unloading. Meta‐structures are developed based on understanding the hyper‐elastic feature of thermoplastic polyurethane (TPU) polymers, elastoplastic behavior of polyamide 12 (PA 12), and snowflake inspired design, derived from theory and experiments. The 3D meta‐structures are fabricated by multi‐jet fusion 3D printing technology. The feasibility and mechanical performance of different meta‐structures are assessed experimentally and numerically. Computational finite element models (FEMs) for the meta‐structures are developed and verified by the experiments. Mechanical compression tests on TPU auxetics show unique features like large recoverable deformations, stress softening, mechanical hysteresis characterized by non‐coincident compressive loading–unloading curve, Mullins effect, cyclic stress softening, and high energy absorption/dissipation capacity. Mechanical testing on PA 12 meta‐structures also reveals their elastoplastic behavior with residual strains and high energy absorption/dissipation performance. It is shown that the developed FEMs can replicate the main features observed in the experiments with a high accuracy. The material‐structural model, conceptual design, and results are expected to be instrumental in 3D printing tunable soft and hard meta‐devices with high energy absorption/dissipation features for applications like lightweight drones and unmanned aerial vehicles (UAVs).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".