An interpretable and reliable framework for alloy discovery in thermomechanical processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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