Thermomechanical Processing of a Nb-Microalloyed Steel in a Controlled-Forging Treatment
Bibliographic record
Abstract
Ferrite grain size is one of the most important microstructural parameters in steels which can be appropriately adjusted to cause a significant strengthening effect. Thermomechanical processing is an effective method for ferrite grain refinement in microalloyed steels. Transformation of deformed austenite with a pancaked grain structure to a relatively fine ferrite phase is an important phenomenon occurring during the thermomechanical processing of microalloyed steels. The final microstructure of steels can be optimized by controlling three critical processing parameters, i.e. i) applied strain (constant strain rate), ii) deformation temperature, and iii) cooling rates following the hot deformation stage. In the present study, a new approach (called controlled-forging treatment) consisting of hot deformation of steel at the austenitic temperature range using an upset forging stage was developed for the ferrite grain refinement in a Nb-microalloyed steel. The investigated steel was subjected to a thermomechanical treatment including reheating, hot deformation for two different strain levels, namely 30 and 50% reductions of height, in a single pass hot-forging stage at temperatures below the T NR (no-recrystallization) and above the T R3 (austenite to ferrite transformation) temperatures followed by cooling to room temperature using three different cooling rates. The experimental results obtained from this proposed treatment were more or less similar to those already obtained for the case of controlled-rolling process on Nb-microalloyed steel sheets.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".