Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application
Bibliographic record
Abstract
This study presents a novel and thorough approach to comprehending and simulating the DRX process while hot compressing steel. To achieve this goal, we studied the high-temperature deformation behavior of a medium-carbon steel through hot compression testing on a Gleeble-3800 thermomechanical simulator over a broad range of strains, strain rates, and temperatures. We also employed an artificial neural network (ANN) to model the thermo-visco-plastic behavior with a flow law. The precision of quantifying the DRX volume fraction is dependent on critical conditions, which are essential for both analytical model evaluation and numerical implementation in finite element software. This study proposes a second ANN, serving as a universal approximator, to fit the data required for DRX critical condition calculations, whereas the Johnson–Mehl–Avrami–Kohnogorov (JMAK) model served as an analytical tool to estimate the DRX volume fraction, which underwent validation through experimental measurements. A numerical implementation of the JMAK model was conducted in ABAQUS software and compared against experimental data by means of microstructure analysis. The comparison revealed a strong correlation between the simulation and experiment. The study investigated the impact of temperature, strain, and strain rate on DRX evolution. The findings showed that DRX increases with rising temperature and strain but decreases with increasing strain rate.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".