The Evaluation of Quenching Temperature Effect on Microstructural and Mechanical Properties of Advanced High Strength Low Carbon Steel After Quenching Partitioning Treatment
Bibliographic record
Abstract
The influence of quenching temperature on microstructural and mechanical properties of low alloy steel of the following chemical composition: 0.26 C, 1.70 Mn, 1.42 Si, 1.10 Cr, 1.10 Ni, 0.94 Cu, 0.24 Mo, 0.1 V, Bal. Fe (Wt.%) was investigated after applying a quenching-partitioning (Q-P) treatment. The steel samples were isothermally quenched at 260, 280, and 300 °C, from the austenitizing temperature and then Q-P treated at 340 °C. After the Q-P treatment, the steel showed a multiphase microstructure containing bainite, martensite, and retained austenite. It was determined that the tensile strength and Charpy impact energy increased with a decrease in quenching temperature to 1415 MPa and 43 J, respectively. This effect was attributed to an increase in the volume fraction of austenite/martensite micro blocks that introduces a hard phase mixture strengthening factor and the presence of tempered martensite, which is strengthened by fine particle dispersion and moreover, a decrease in thickness of the bainitic-ferrite subunits that refine the microstructure. The fractographic examination of the Charpy tested specimens showed that the sample quenched at 260 °C contained finer and deeper dimples, which indicates that more energy was spent on the nucleation and growth of ductile fracture microvoids, thus increasing the toughness.
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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".