Enhancing plasticity by increasing tempered martensite in ultra-strong ferrite-martensite dual-phase steel
Bibliographic record
Abstract
The present study focuses on the influence of tempered martensite (TM) on the mechanical properties and fracture mechanisms of an ultra-strong dual-phase (DP) steel. The steel was subjected to intercritical annealing combined with an aging process. The high fraction of martensite (above ∼70 vol%) results in a high strength level above 1300 MPa, and the presence of TM ensures a good ductility up to ∼10% total elongation. The microstructures of the tested steels were analyzed by the scanning electron microscopy (SEM), transmission electron microscopy (TEM) and electron backscatter diffraction (EBSD). Results showed that a higher fraction of TM significantly improves ductility, mainly due to its beneficial effects on damage tolerance. More specific, the damage behavior alters from martensite cracking to ferrite-martensite interface decohesion, with increasing TM fractions. This results in higher post uniform elongation (PUE) values. With the increase of intercritical annealing temperature, the yield strength (YS) increases, but both the ultimate tensile strength (UTS) and strain hardening rate decrease. The strain hardening rate was discussed based on the influence of carbide precipitates and decreasing geometrically necessary dislocation (GNDs). Besides, we found that the aging process could significantly increase the volume fraction of TM and improve the plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".