Understanding Strength-Toughness Combination in the Processing of Engineering Steels: A Perspective
Bibliographic record
Abstract
High strength-high toughness combination and formability has been the primary focus of the author's research over the last two decades, where the attempt was to either develop newer steels or maximize the fracture resistance of engineering steels at specified levels of strength. In this regard, significant success was achieved based on an extended program of basic research at the author's current and former institutions to understand the part played by crystal structure, solute additions, grain size, grain boundary chemistry, texture, and substructural features such as retained austenite, martensite lath, and packet size, and characteristics of other microstructural constituents. Each of these features influences the fracture mode, the degree of plasticity, and the rate of growth of nucleated voids. Important instances include maraging steels, precipitation hardened stainless steels, low alloy steels, interstitial-free steels, microalloyed steels, pipeline steels, and silicon-containing medium carbon steels. Underlying the attempt to maximize toughness through the study of determining role of microstructure were the development of concept of grain boundary segregation maps, application of stereological approach, new alloy design with lean chemistry, and streamlining of processing-related variables. The aforementioned instances of engineering steels provided a means of comprehensively analyzing the relationship of toughness to microstructural features and facilitate the development of high performance steels.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".