Development of light composite materials with low coefficients of thermal expansion
Bibliographic record
Abstract
Composite materials based on aluminium are used in different fields where weight, thermal expansion, and thermal stability are key requirements. The aim of the present study was to develop a universal method and scientific approach for evaluating the design of lightweight, Al matrix composites with low coefficients of thermal expansion (CTE) and high dimensional stability, and to produce such composites using the vacuum plasma spray (VPS)process. The methodology is general and could be applied to other composite systems. The VPS-produced Al and Al alloy 6061 based composites were reinforced with a variety of ceramic particles including Si3N4, B4C, TiB2, and 3Al2O3.2SiO2. These composites have low CTE values ((12–13)×10-6 K-1), similar to that of steel, and high dimensional stability (capable of keeping dimensions stable with changes in temperature). They have low porosity (98–99%dense) and a uniform distribution of the strengthening particles. Hot rolling of the VPS-formed composites, followed by heat treatment, resulted in a significant improvement in the mechanical properties. Deformed and heat treated 6061 based composites, containing 20 wt-%TiB2 and 40 wt-%3Al2O3⋅2SiO2, showed excellent mechanical properties (ultimate tensile strength 210–250 MPa, elongation >4%).
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 | 0.000 |
| 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".