Recommended Practice for Dynamic Testing for Sheet Steels - Development and Round Robin Tests
Bibliographic record
Abstract
<div class="htmlview paragraph">Tensile properties of sheet steels at dynamic conditions are becoming more important for automotives in recent years due to the positive strain rate effect of steels which significantly improves energy absorption capability during crash events. However, several testing techniques are used by different testing laboratories, no testing standards are available, and the quality of data generated by different laboratories is often not comparable. In order to improve the data quality at high strain rate testing conditions and thus to improve the accuracy of crash simulation results, The International Iron and Steel Institute (IISI) initiated a project to develop the “Recommendations for Dynamic Tensile Testing of Sheet Steels”. The document provides guidelines for key elements of high strain rate testing, testing techniques, input methods, specimen geometry and stress/strain measurement instrumentations. A Round Robin test program was launched afterwards to evaluate the current status of testing quality with 10 laboratories participating from Europe, Japan, Korea and North America. The Round Robin test program showed that not only the equipment used are different, specimen dimensions are also vastly different from different testing laboratories. This paper describes the development of the document, key issues of the high strain rate testing, and Round Robin test results. An example is also given showing how data quality was significantly improved by careful refinement of the testing procedures including specimen geometry.</div>
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.077 |
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".