Punch in a Punch: Validating FLC and fracture models for severe strain path changes
Bibliographic record
Abstract
While generating experimental linear loading strain paths is still required for the identification of Forming and Fracture Limit Curves, non-linear loading paths are necessary to validate models for industrial applications. Commonly non-linear loading paths are achieved by interrupting oversized uniaxial or biaxial tensile experiments and extracting pre-strained specimens for further forming or fracture testing. Due to the inherent multiple manufacturing steps, this method is challenging to automate, which denies the generation of large datasets for deep analysis. The present study demonstrates that severely non-linear loading paths can be obtained in a high-throughput manner from a single specimen by means of a telescopic forming approach—specifically, a punch-in-a-punch system—within an automated Nakazima setup. Two steels and two aluminium alloys are tested, each using sets of seven Nakazima specimens, subjected to a two-step forming process. The first step is an interrupted Marciniak forming test. The displacement is then stopped and held while a secondary piston is moved out of the Marciniak punch's inner part, effectively generating a second loading path.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".