High Rate Characterization of Three DP980 Steels
Why this work is in the frame
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Bibliographic record
Abstract
Advanced high strength steels (AHSS) are used extensively in the automotive industry in the ongoing effort to reduce vehicle weight. Their increased strength allows for the reduction of sheet thickness, and thus a reduction in mass, while offering formability and cost advantages when compared to other metal alloys typically considered for lightweight applications. DP980 steels are AHSS being considered for structural energy absorbing components; however, there is a lack of published information on their high rate behaviour. This paper presents the results of an experimental program that characterized three production DP980 steels from three different manufacturers at strain rates of 0.001, 1, 10, 100 and 1,000 s -1 . An electro-mechanical frame was used for the quasi-static tests, the 1, 10, and 100 s -1 tests were carried out using a fast hydraulic apparatus and the 1,000 s -1 experiments were carried out using a tensile split Hopkinson bar. The quasi-static hardening response at strains higher than the uniform elongation of about 7% was obtained by using a shear test, thus avoiding the use of inverse modelling techniques. The results indicate that the DP980 steels are moderately rate sensitive, with one of the materials showing higher sensitivity than the others. One of the materials exhibited a yield point phenomenon that appears to affect the behaviour of the material at 100 and 1,000 s -1 , however, the reasons for this behaviour remain an open question. The data was fit to modified Johnson-Cook and Cowper-Symonds model to account for rate sensitivity. The results presented in this paper provide a tool for modelling the dynamic behaviour of DP980 steels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.006 | 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 it