High capacity, adaptive energy absorption under tensile loading conditions utilizing an axial cutting deformation mode
Bibliographic record
Abstract
Conventional tensile energy absorbers are often limited in their efficacy by erratic and unpredictable force responses. Additionally, the published literature on devices of this nature is sparser in comparison to compressive energy absorbers and hence engineers are further limited by a lack of existing designs. Axial cutting is an energy dissipating technology studied extensively under compressive loading with promising results. A novel apparatus was explored in this study to implement axial cutting under tensile loading; this is an application which sees significantly less attention in the open literature. An analytical modeling approach was utilized as a design tool to assess the specimens in this study and to precisely engineer energy absorbers with adaptive force responses. The tests were conducted quasi-statically utilizing a hydraulically powered testing apparatus with a capacity of 300 kN. AA6061-T6 and T4 extrusions were utilized with wall thicknesses ranging from 0.794 mm to 3.175 mm. Force responses with tensile force efficiencies between 85% and 92% were observed. Energy absorption values ranging from 2.2 kJ to 7.7 kJ and specific energy absorption values between 12 kJ/kg to 16 kJ/kg were measured, greatly exceeding the mechanical capabilities of multiple, established tensile energy dissipating solutions. Highly stable and repeatable deformation was observed between consecutive tests within most specimen categories. Numerical models were created utilizing LS-DYNAⓇ and average validation metrics and cumulative errors of approximately 0.90 and 0.09 were calculated, respectively, indicating excellent predictive capabilities.
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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".