Effect of prior austenite characteristics on mechanical properties of thermomechanically processed multiphase TRIP assisted steels
Bibliographic record
Abstract
To develop any multiphase transformation induced plasticity (TRIP) assisted steel through thermomechanical processing routes, the effects of materials and process variables are to be thoroughly identified. Accordingly, the present work was programmed to study the influence of the state of prior austenite on the subsequent transformations and the related final phase combinations. In this framework, using a low carbon TRIP assisted multiphase steel, the compression specimens were subjected to different thermomechanical processing schedules to generate different prior austenite characteristics. The results indicated that the characteristics of martensite/austenite (M/A) microcomponent in the final microstructure were dictated by the state of prior austenite. In fact, increasing prior austenite grain size led to large decrease in the amount of M/A phase and this, in turn, resulted to higher strength without sacrificing the ductility. It was also found that static recrystallisation of austenite brings about more percentage of M/A phase compared with dynamic recrystallisation.
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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 | 0.000 | 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) | 0.002 | 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".