Steel-Based Composites: Driving Forces and Classifications
Bibliographic record
Abstract
In this overview of steel-based composites, consideration is given to conventional metal-matrix composites, in which steel is combined with another metal, ceramic, or polymer. In addition, we define fully steel composites, in which both components of the structure are developed within the steel. These approaches are integrated by discussing a series of macroscopic, mesoscopic, and microscopic examples. This review provides an integrated view of steel composites and allows modeling of the mechanical response to be considered both at the continuum level and in terms of dislocation mechanisms depending on the length scale and the degree of mechanical contrast between the constituent phases. In the context of fully steel composites, consideration is given to static systems in which the volume fraction of the strengthening phase is constant and the length scale is varied by heat treatment or imposed plastic strain. Moreover, we discuss dynamic systems in which a phase transition occurs concomitantly with plastic strain, resulting in an increase in the density of planar barriers that control the plasticity. A discussion of classical works that describe materials such as Damascus steels is used as a template to consider a variety of ways of producing ultrahigh-strength steel composites. Examples of applications are cited and linked to the important issue of developing appropriate fabrication methods for the production of current and future steel composites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".