Induction Hardening of a 0.40 % C Novel Microalloyed Steel: Effects of Heating Rate on the Prior Austenite Grain Size
Bibliographic record
Abstract
This work explores the effect of heating rate on the prior austenite grain size and hardness of a thermomechanically processed novel niobium-microalloyed 0.40 % carbon low-alloyed steel intended for use in induction hardened slurry pipelines. The aim was to identify the heating rates that lead to the maximum hardness, for high wear resistance, and minimum prior austenite grain size, for high toughness. For this purpose, a Gleeble 3800 machine has been employed to simulate the induction hardening process and provide dilatometric phase transformation data. The prior austenite grain structure has been reconstructed from the EBSD results using a MatlabR script supplemented with MTEX texture and crystallography analyses. Heating rates ranged from 1 to 50 °C/s and the cooling rate was 50 °C/s. The results show that the prior austenite grain size greatly depended on the heating rate: compared to the lower heating rates, the maximum heating rate of 50 C/s produces remarkably fine prior austenite grains and a fine final martensitic microstructure after quenching. In addition, a relation between the heating rate and the deviation from equilibrium temperature has been established.
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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".