Depth-sensing thermal stability of accumulative fold-forged nanostructured materials
Bibliographic record
Abstract
Accumulative fold-forging (AFF) as a newly developed severe plastic deformation (SPD) process based on the repetitive fold-forging steps is implemented for the production of the layered UFG (~200 nm) AA8006 alloy and AA8006-B4C nanocomposite (~35 nm, 10 vol%) materials from the initial AA8006 alloy foil. The remarkably refined grains and nanoparticles can control metallic materials' mechanical properties, including the strength, strain rate dependency, and thermal stability behavior. In this context, nano-grains' local mechanical response during nanoindentation can vary considerably depending on the testing temperature, and this has yet to be discussed. In this research, after materials characterization of produced nanostructured materials according to the AFF route, the relating depth-sensing thermal stability of them assessed by conducting the nanoindentation testing at different temperatures in the range of 300–523 K. Depth sensing softening behavior is elaborated to identify the low-temperature thermal stability of processed materials. The results enunciated the occurrence of thermal softening by refining the grain structure. However, introducing the reinforcing nanoparticles lead to a pinning action that stabilized the grain boundaries.
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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) | 0.000 | 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 | 0.000 | 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".