Computational Modeling of Multiple-Phase Transformations in API X70 and X80 Steels
Bibliographic record
Abstract
Continuous cooling transformation (CCT) diagrams for two thermo-mechanically controlled processed (TMCP) steels were produced using a modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, which accounted for the simultaneous transformation of multiple phases under non-isothermal conditions. A basin hopping algorithm was used to sequentially optimize the model parameters for each phase. Samples were prepared using a dilatometer which replicated the deformation and cooling rates experienced during TMCP. Scanning electron microscopy (SEM) and electron back-scattered diffraction (EBSD) were used to identify and quantify the phases present in each steel. CCT diagrams illustrating the start and stop temperatures of each phase were constructed for both steel samples. Through inclusion of the stop temperatures of each phase transformation, the utility of the CCT diagrams were expanded. This was done by introducing the possibility of applying the Scheil additive principle with respect to the beginning and end of each phase transformation. With this modification, the CCT diagrams are now more appropriately suited to predict the phase transformations that occur on the ROT, where non-continuous cooling occurs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".