Effect of Heating Rate on Microstructure and Corrosion Resistance of Quenched and Tempered 8620 Low Carbon Alloy Steel
Bibliographic record
Abstract
In the process of heat treatment of 8620 low alloy steel, the heating rate is a critical parameter that must be carefully controlled to achieve the desired combination of microstructural features, mechanical properties, and corrosion resistance while simultaneously ensuring process efficiency and cost-effectiveness. This study investigates, for the first time, the microstructural evolution and electrochemical properties of 8620 steel under identical quenching and tempering heat treatment routes with slow-rate (SR) and fast-rate (FR) heating rates. Microstructural analysis revealed martensitic phases for SR, while FR exhibited a dual-phase microstructure containing ferrite. Upon tempering, for both samples, the martensite transformed into tempered martensite, with tempered (Temp) FR exhibiting around 50% smaller ferrite grains. Mechanical testing indicated that SR had 17% higher hardness than FR, although hardness decreased after tempering by 22% (SR) and 17% (FR). All electrochemical tests indicated that the as-quenched SR exhibited significantly superior corrosion resistance than FR. For instance, the polarization resistance of SR was 440 Ω higher than that of the FR samples. Tempering resulted in a considerable decrease in corrosion resistance for Temp SR, whereas Temp FR improved. Electrochemical characterization revealed Temp FR displayed close-to-ideal capacitive behavior and low double-layer capacitance, indicating enhanced overall corrosion resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".