An artificial intelligence-based optimization framework for the optimal composition and thermomechanical processing schedule for specialized micro-alloyed multiphase steels
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Bibliographic record
Abstract
An artificial intelligence-based heuristic approach is presented to optimize the chemical composition and the thermomechanical processing schedule to obtain specialized micro-alloyed multiphase steels with desired mechanical properties, at minimal manufacturing cost. The optimization framework uses a modified form of genetic algorithm, called the micro-genetic algorithm (μGA), that uses a penalty-based cost function formulation operating on a multi-dimensional search space spanning 15 alloying elements, an average cooling temperature, an austenitizing temperature and eight time–temperature points from the cooling profiles of multiphase steels. With superior search speed and convergence rates to the traditional genetic algorithm, μGA uses a neural network-based reduced-order model to predict hardness. Additional correlation equations are used to determine the corresponding tensile strength and elongation. Microstructural analysis was performed using neurocomputing techniques to further validate the accuracy of the algorithm. The entire computational framework was validated using data from the literature, establishing its utility in steel design.
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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 | 0.000 | 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) | 0.000 | 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 it