Data-driven mechanical property prediction and optimization of hot rolled microalloyed steels
Notice bibliographique
Résumé
The primary objective of this research was to employ data-driven techniques to predict and optimize the mechanical properties of microalloyed steels during the thin slab direct rolling process. The data for this study were sourced from Algoma Steel Inc., located in Ontario, Canada, and the work was divided into two main parts.In the first part, Deep Neural Network (DNN) models were developed to predict the mechanical properties, specifically Ultimate Tensile Strength (UTS) and Lower Yield Strength (LYS), of Nb-based microalloyed hot-rolled strips. To introduce explainability into the DNN models, Game theory-based SHapely Additive exPlanations (SHAP) were utilized. The SHAP values provided insights into the combined effects of chemical composition and thermomechanical processing parameters. The influence of chemical composition was corroborated by physical metallurgy theory, and correlations were established with the empirical relationship of the No-recrystallization temperature (Tnr) from existing literature. Additionally, the data were analyzed using SIMS Mean Flow Stress (MFS) against the inverse temperature, with comparisons across different gauges and compositions to support the model explanations and suggest underlying metallurgical mechanisms. This segment of the study highlighted significant opportunities for optimization of alloy composition, which led to the second part of the research.The second part aimed to develop a data-driven framework for alloy design, considering the processing schedules of the rolling mill. Initially, seven different supervised machine learning (ML) algorithms were employed to model UTS and % Elongation for V-based microalloyed steel. Global feature importance was derived from SHAP values for these models. Model-agnostic conformal predictions were implemented to quantify uncertainty, enhancing the reliability of predictions. Given the challenges of inverse design in industrial contexts—such as multiple objectives, non-unique solutions, and large search spaces—the problem was approached as a multi-objective optimization (MOO) task focusing on the trade-off between strength and ductility, i.e. generating the best combination of strength and ductility. The best performing ML models for UTS and % Elongation were utilized as objective functions in the MOO, with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) employed to derive optimized Pareto front solutions. The thermomechanical processing parameters were integrated as strict constraints in the decision variable space of the NSGA-II. To visualize the solutions, t-distributed Stochastic Neighbor Embedding (t-SNE) was used to map them along with original rolling data into a two-dimensional space, which was then clustered using the K-means algorithm. Select representative solutions from each cluster were chosen to identify unique alloys. This research provides key applications in developing online property prediction tools, enhancing process understanding and aiding in both process control, and alloy design
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».