Investigating the Effect of Heat Treatment on the Microstructure and Hardness of Aluminum-Lithium Alloys
Bibliographic record
Abstract
In this study, the effects of heat treatment on the microstructure and strength (micro-hardness) of an aluminum–lithium (Al-Li) base alloy containing copper (Cu) and scandium (Sc) were investigated, with a view to enhancing the alloy performance for aerospace applications. The heat treatment conditions were investigated to understand the precipitation behavior and the mechanisms involved in strengthening. Aging was carried out at temperatures of 130 °C and 150 °C for aging times of 1 h, 2.5 h, 5 h, 10 h, 15 h, 25 h, 35 h, and 45 h at each temperature for Al-Li alloy and at 160 °C, 180 °C, and 200 °C for aging times of 5 h, 10 h, 15 h, 20 h, 25 h, and 30 h at each temperature for Al-Li-Cu and Al-Li-Cu-Sc alloys. The investigation revealed that both solution heat treatment and artificial aging had a notable impact on strengthening the hardness of the alloy. This effect was attributed to the characteristics of the precipitates, including their type, size, number density, and distribution. The addition of copper (Cu) and scandium (Sc) was observed to have an impact on grain size refinement, while Cu addition specifically affected the precipitation behavior of the alloy. It led to remarkable changes in the number density, size, and distribution of T1 (Al2CuLi) and θ’ (Al2Cu) phases. As a result, the hardness of the alloy was significantly improved after the addition of Cu and Sc, in comparison with the base Al-Li alloy. The best heat treatment process was determined as: 580 °C/1 h solution treatment +150 °C/45 h artificial aging for Al-Li alloy and 505 °C/5 h solution treatment +180 °C/20 h artificial aging for Al-Li-Cu and Al-Li-Cu-Sc alloys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".