Constitutive Modeling of High‐Temperature Deformation Behavior of Nonoriented Electrical Steels as Compared to Machine Learning
Bibliographic record
Abstract
Hot rolling is a critical thermomechanical processing step for nonoriented electrical steel (NOES) to achieve optimal mechanical and magnetic properties. Depending on the silicon and carbon contents, the electrical steel may or may not undergo austenite–ferrite phase transformation during hot rolling, which requires different process controls as the austenite and ferrite show different flow stresses at high temperatures. Herein, the high‐temperature flow behaviors of two nonoriented electrical steels with silicon contents of 1.3 and 3.2 wt% are investigated through hot compression tests. The hot deformation temperature is varied from 850 to 1050 °C, and the strain rate is differentiated from 0.01 to 1.0 s −1 . The measured stress‐strain data are fitted using various constitutive models (combined with optimization techniques), namely, Johnson–Cook, modified Johnson–Cook, Zener–Hollomon, Hensel–Spittel, modified Hensel–Spittel, and modified Zerilli–Armstrong. The results are also compared with a model based on deep neural network (DNN). It is shown that the Hensel–Spittel model results in the smallest average absolute relative error among all the constitutive models, and the DNN model can perfectly track almost all the experimental flow stresses over the entire ranges of temperature, strain rate, and strain.
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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) | 0.000 | 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) | 0.000 | 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".