Determination of Young's modulus in aluminum alloys: Role of precipitates, dispersoids, and intermetallics
Bibliographic record
Abstract
The present work was performed on Al-Si-Mg-Cu and Al-Si-Mg alloys containing measured amounts of Ni (0.4 wt.% and 4 wt.%), Bi (1.0 wt.%), Ca (0.5 wt.%), Sr (0.015 wt.%), 10 vol.%SiC(p), and 20 vol.%SiC(p). After solutionizing treatment, tensile bars (ASTM B108) were aged in the temperature range of 155 °C to 350 °C for up to 100 hours. The results of 700 tensile bars show that although the value of E is the Σ =E1+E2+ E3 +----, where E is a function of interparticle spacing and particle volume fraction of each type of precipitate, E can not be determined using a simple empirical formula due to interference of other factors such as porosity, inclusions, particle/matrix surface reaction, and precision of measuring each of the involved parameters. Considering alloying elements, the addition of a sufficient amount of Ni (Ni/Cu >1), in the T6 condition, produces the highest E value, about 92 GPa (Al 2 Cu, Al 3 Ni, Al 3 NiCu precipitates). Modification of the eutectic Si particles has a moderate improvement in E about precipitation hardening (about 12%). The highest E value was obtained using metal matrix composites (359 alloy + 20 vol.% SiC(p)) in the T6 condition, approximately 42% improvement over that achieved using the base alloy, at 110 GPa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".