Mechanical Behavior of Two Ferrite–Martensite Dual-Phase Steels over a Broad Range of Strain Rates
Bibliographic record
Abstract
The present study concerns the deformation and fracture behavior of two ferrite–martensite dual phase steels (FMDP660 and FMDP780) with different phase fractions subjected to different strain rate (0.001 s−1 to 1000 s−1) tensile testing. For both steels, the yield strength (YS) monotonically increased with strain rates, whereas the values of ultimate tensile strength (UTS), uniform elongation (UE) and post-uniform elongation (PUE) were maintained stable at the low strain rate range (0.001–0.1 s−1), followed by a significant increase with strain rate at high strain rate levels (0.1–1000 s−1). The FMDP780 steel with a higher fraction of martensite possessed a stronger strain rate sensitivity of tensile strength and elongation (UE and PUE) values at the high strain rate stage, compared with the FMDP660 sample. The change of UTS and UE with different strain rates and phase fractions was highly related to the strain hardening behavior, which was controlled by the dislocation multiplication in ferrite, as validated by transmission electron microscopy (TEM). The fracture surface of the two steels was characterized by dimpled-type fracture associated with microvoid formation at the ferrite–martensite interfaces, regardless of the strain rates. The change of the dimple size and PUE value of the two steels with strain rates was attributed to the effect of adiabatic heating during tensile testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".