Data-driven mechanical property prediction and optimization of hot rolled microalloyed steels
Bibliographic record
Abstract
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 (T nr ) 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 viii
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".