Enhanced Mechanical Strength and Electrical Conductivity of Al–Ni‐Based Conductor Cast Alloys Containing Mg and Si
Bibliographic record
Abstract
The electrical conductivity (EC), mechanical strength, hot tearing susceptibility (HTS), and related microstructure of Al–xNi–0.55Mg–0.55Si conductor alloys (x = 1–4 wt%) are investigated. Adding Mg and Si into Al–Ni‐based alloys, numerous β″/β′ precipitate after T5 and T6 treatments, thus significantly improving the EC and mechanical strength. The HTS of the alloys reduces significantly as the Ni content increases, mainly because of an increase in the eutectic Al–Al3Ni and a reduction in the grain size. Under T5 condition, the tensile strengths increase gradually with the Ni content and reach a medium strength level, with yield strength (YS) of 158–205 MPa and EC of 47.1–50.7% IACS. After applying T6, all alloys achieve a high strength, with YS of 246–287 MPa and EC of 47.7–51.1% IACS. However, the strength decreases with increasing Ni content. In general, the Al3Ni–0.55Mg–0.55Si alloy presents a better trade‐off among HTS, YS, and EC among the four alloys investigated. Due to its excellent properties (EC of 49.4% IACS and YS of 178 MPa in T5, and EC of 49.7% IACS and YS of 250 MPa in T6), the Al3Ni–0.55Mg–0.55Si alloy is a promising material for the fabrication of Al conductor cast alloys.
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 | 0.000 | 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".