An artificial intelligence-based optimization framework for the optimal composition and thermomechanical processing schedule for specialized micro-alloyed multiphase steels
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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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".