Design and crashworthiness behaviors of novel 3D printed cutting-type energy-absorbing composite structures
Bibliographic record
Abstract
To simultaneously meet crashworthiness and lightweight requirements, a novel 3D-printed cutting-type energy-absorbing structure, fabricated using short carbon fiber-reinforced polyamide, was proposed. The printed structure, comprising a central cylinder and round bulges, absorbed energy through the extrusion and shearing of the bulges as the cylinder moved through the base under applied stress. The impact of key geometrical parameters on the crashworthiness behaviors of the structure, including the thickness and outer diameter of the round bulge, as well as the spacing between the round bulges, was investigated under quasi-static cutting tests. The parametric study revealed that increasing the round bulge thickness within the permissible range significantly improved the crashworthiness of the structure. Specifically, specimens with higher round bulge thickness exhibited a remarkable 238.8% increase in specific energy absorption compared to specimens with lower round bulge thickness. Additionally, the crushing force efficiency of the printed structure initially increased and then decreased as the round bulge thickness increased, with the highest value reaching 68.56%. Furthermore, appropriately increasing the round bulge diameter and decreasing the spacing between the round bulges could improve the crashworthiness of the structure. The maximum specific energy absorption of the printed structure in this study was 25.14 J/g. Compared with most metal cutting-type energy-absorbing structures reported in the literature, the proposed structure in this study showed great potential as energy absorbers used in different areas for passive safety system design.
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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) | 0.000 | 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".