Influence of the as quenched state and tempering temperature on the final microstructure and hardness of a high strength medium carbon steel
Bibliographic record
Abstract
The present study aimed to investigate the impact of the as-quenched microstructure and tempering temperature on the final microstructure and hardness of a medium-carbon, low-alloy steel using dilatometry, Electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM). The results revealed substantial differences in the continuous heating stage of tempering in bainitic and martensitic samples, primarily attributed to the auto-tempering process during cooling. Tempering was carried out at 550 and 620 °C, and dilatometry results, along with microstructure analysis, indicated incomplete decomposition of retained austenite (RA) at both temperatures during a 30-min hold in the bainitic sample. The results show that non-decomposed RA, following the tempering of bainite, transformed into blocky fresh martensite, while no evidence of fresh martensite was observed in the martensitic sample. A new approach using EBSD and SEM images revealed that the decomposition of M/A (martensite/austenite constituent) zones in the bainitic sample resulted in the formation of a chain of aggregated chromium carbide zones at the grain boundaries. In contrast, the martensitic zone exhibited a uniform distribution of carbides in the microstructure. The stability of the phases was examined using the TCFE10 (thermodynamics) and MOBFE5 (mobility) modules of the DICTRA Themo-Calc software. Hardness measurements on all samples indicated decreases by about 18–24 % in the martensitic sample after tempering, while the bainitic sample exhibited a 5 % increase in hardness after tempering, attributed to secondary hardening and fresh martensite formation.
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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) | 0.001 | 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".