Influence of Starting Microstructure on Dilatation Behavior during Tempering of a High Strength Steel
Bibliographic record
Abstract
The aim of this study was to determine the effect of non-isothermal tempering on microstructure evolution in large-size slabs. Using high-resolution dilatometry, three different cooling rates (from 0.08 to 3°C/s) representative of different regions from the surface to the core of the slab were experimentally simulated, and then tempering was carried out for each starting microstructure. A combination of light and electron microscopy and X-ray diffraction analyses were employed to accurately analyze different phenomena occurring during the tempering process, specially, the identification of different microstructures (bainite, martensite and retained austenite), and the determination of the percentage of retained austenite for each experimental condition were considered. Experimental results revealed that the microstructure after the cooling rate of 0.08°C/s consisted of bainite and some retained austenite. For the cooling rate of 0.3°C/s, martensite plus bainite was detected, and when the cooling rate was increased to 3°C/s, a martensitic microstructure was obtained. Analysis of dilatometry curves indicated that tempering behavior varied significantly with the starting microstructure. Martensite tempering was accompanied with a length decrease due to the decomposition of medium-carbon martensite to low-carbon martensite plus carbides. Tempering of bainite and retained austenite resulted in a remarkable length increase.
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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 | 0.001 |
| 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) | 0.001 | 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".