Prediction of fracture strength in Al<sub>2</sub>O<sub>3</sub>/SiC<sub>p</sub>ceramic matrix nanocomposites
Bibliographic record
Abstract
Based primarily on a recent publication [S.M. Choi, H. Awaji, Sci. Tech. Adv. Mater. 6 (2005) 2–10.], where the dislocations around the nano-sized particles in the intra-granular type of ceramic matrix nanocomposites (CMNCs) were modeled, dislocation activities in Al2O3/SiCp CMNCs were discussed in relation to the processing conditions. The dislocations around the nano-sized particles, caused by the thermal mismatch between the ceramic matrix and nano-sized particles, were assumed to hold out the effect of Orowan-like strengthening, although the conventional Owowan loops induced by the movement of dislocations were unlikely in the ceramic matrix at room temperature. A model involving the yield strength of metal matrix nanocomposites (MMNCs), where the Owowan strengthening effect was taken into consideration, was thus modified and extended to predict the fracture strength of the intra-granular type of CMNCs without and with annealing. On the basis of the characteristics of dislocations in the CMNCs, the load-bearing effect and Orowan-like strengthening were considered before annealing, while the load-bearing effect and enhanced dislocation density strengthening were taken into account after annealing. The model prediction was found to be in agreement with the experimental data of Al2O3/SiCp nanocomposites reported in the literature.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".