MOUTH-LIKE CRACKING IN A HIGH-STRENGTH MULTIPHASE STEEL AND ITS RELATIONSHIP TO FRACTURE TOUGHNESS
Bibliographic record
Abstract
The effect of microstructure on crack growth behaviour in steels has always been a subject of considerable research interest.Based on a quenching and partitioning process (Q&P), and the transformation of a nano-scaled bainite in advanced high-strength steels, a novel quenchingpartitioning-austempering process (Q-P-A) has been developed for manufacturing a multiphase microstructure in a medium carbon steel (55Mn2SiCr).The processing sequence consists of the following steps: austenitizing at 900°C for 0.5 h; controlled quenching and cooling to 200°C, i.e. slightly below Ms (the start temperature for martensite transformation) for 5 s; austempering at 170°C for 5 min; up-heating to 250°C for 120 min; final air cooling to room temperature.An ultimate tensile strength (UTS) above 2 GPa, as well as an acceptable elongation of 3%, is obtained due to a multiphase formation comprising prior martensite (PM), bainitic ferrite (BF), retained austenite (RA) and nanoscaled structure ((BF + RA(+C))nano).Mouth-like cracks are observed on the fracture surface and the crack arrest behavior is investigated.When a microstructural cluster with (BF + RA(+C))nano fully covered PM is formed, a mouth-like crack can be formed and a superior crack resistance can be obtained.The crack initiates from the PM boundary and propagates along the interface between the PM and (BF + RA(+C))nano over a distance of a few millimeters and before being arrested in the (BF + RA(+C))nano.This behaviour is mainly attributed to the uniform distribution of film RA and needle BF with nano-level spacing in the (BF + RA)nano.The stress concentration energy at the crack tip can be absorbed by the martensitic transformation of the film RA.The results are important when designing a multiphase microstructure for a commercial high-strength steel.
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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) | 0.000 | 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 | 0.000 | 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".