Empirical models of mechanical behaviour of Al-Si-Mg cast alloys for high performance engine applications
Bibliographic record
Abstract
Substructure characteristics in hot worked Alalloys are very important for modelingmechanical properties during hot forming,and also in the product. In contrast to simplegrain shape in etched-optical microscopy(EOM), polarized optical microscopy (POM)significantly confirmed subgrain presence inbetter detail than x-ray diffraction (XRD).Transmission electron microscopy (TEM)revealed the dislocations forming subgrainboundaries (SGB) and dispersed betweenthem; TEM in scanning mode (STEM) couldprovide microtextures substantiating XRD.Scanning electron microscopy with backscatteredimage (SEM-EBSI) exhibitedsubstructures more accurately than POM butmuch less detailed than TEM. Finally,orientation-imaging microscopy (OIM)provided microstructures as in SEM-EBSI andalso detailed misorientations; however,omission of very-low angle SGB seen in TEMgave rise to estimates of larger subgrain sizesand misorientations. The field of view is verylimited in TEM, but fairly similar in POM,SEM-EBSI and OIM although highermagnifications are possible in the last two.The various techniques are also affecteddifferently by substructure scale (temperature,strain and rate) and composition thatalso influence specimen preparation.Examination by several techniques is bestassurance of correct interpretation ofmicrostructural characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".