On the Use of the Shear Punch Experiments in Determining Mechanical Properties of Various Dual Phase Steels
Bibliographic record
Abstract
Dual phase steels are being extensively considered as a structural material for automobiles because of the favourable combination of strength and formability. Crashworthiness of these new steels is an area of great importance. High strain rate testing is one approach to measure the ability of materials to absorb energy in a crash situation. The objective of this paper is to examine the effect of the deformation rate on the mechanical properties of dual-phase and multi-phase steels. Shear-punch experiments are conducted both at quasi-static and dynamic rates for this purpose. The ease of preparation of shear punch specimens compared to the tension specimen makes this approach attractive in evaluating key mechanical properties, such as ultimate tensile strength (UTS) and ductility limits, of automotive materials mostly in sheet forms. A qualitative correlation between the quasi-static shear punch and tension experiments results is made, and parallels of this correlation are considered for the dynamic rates of deformation. A servo hydraulic MTS machine is used for performing the quasi-static experiments, with a special fixture for the shear tests, while the high rate experiments are conducted using the shear version of the Split Hopkinson Pressure Bars (SHPB) setup. Dual phase steels DP600/300 and DP600/400 are tested as well as alternate microstructure dual phase steels with different fractions of martensite obtained by changing the intercritical annealing temperature and water quenching.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".