An algorithm for optimal design and thermomechanical processing of high carbon bainitic steels
Bibliographic record
Abstract
In this paper we present a generic framework of a computationally efficient search algorithm to meet multiple objectives on a multi-dimensional search space. The algorithm is integrated with a penalty-based cost function that enables us to retain the interim optimal solutions. The algorithm has been applied to determine the optimal combination of alloying elements and heat treatment conditions to obtain steels with desired material properties, namely hardness, tensile strength and elongation. In this work, the search space is a combination of eight alloying elements and heat treatment conditions (isothermal temperature and time). To evaluate the quality of the alloying elements and heat treatment combinations, the algorithm is equipped with reduced order models for hardness, tensile strength as well as elongation percentage. The algorithm has been validated with respect to several standard test optimisation problems prescribed in the literature. Subsequently, the validated algorithm has been applied to develop optimal design solutions for steels with desired mechanical properties. In doing so, the framework is tested for three different evaluation functions to showcase the ability of the algorithm to obtain solutions meeting the desired targets/constraints.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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".