An interpretable and reliable framework for alloy discovery in thermomechanical processing
Bibliographic record
Abstract
In thermomechanical controlled processing, both the alloy composition and the processing strategy shape the mechanical properties of metals. In this study, we present a data-driven approach to discover alloys with optimized strength-ductility trade-off in the thin slab direct rolling process. We evaluate seven different supervised machine learning algorithms to predict two mechanical properties, namely Ultimate Tensile Strength and % Elongation. SHapely Additive exPlanations (SHAP) augments interpretability to the best performing models. NSGA-II, an evolutionary genetic algorithm, is employed with ML models as objective functions to obtain the optimal Pareto Front solutions. Further, we incorporate manifold learning and unsupervised clustering to screen the Pareto Front and to select a few unique solutions which can facilitate added analysis for implementation. Furthermore, we introduce the application of conformal predictions for uncertainty quantification, ensuring reliability of the framework. Overall, the proposed approach enables interpretable and reliable property prediction, thus accelerating alloy design in thermomechanical processing. • Conformal Predictions for Reliability of ML Framework. • Alloy Discovery using Multi-objective Optimization. • Data-driven Post-Pareto Analysis to Choose Unique Alloys.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".