Investigation of the high thermal ageing resistance of the 2219 aluminium alloy
Bibliographic record
Abstract
The 2219 aluminium alloy is widely used in high-temperature applications, particularly in aerospace. Thermal ageing during duty can lead to the coarsening of θ’ hardening precipitates, which are responsible for the mechanical properties of the alloy. In this study, temperature gradient heat treatments were employed to evaluate its resistance to thermal ageing over a continuous temperature range of 165 °C to 245 °C for up to 10,000 h. Despite its composition being primarily that of an Al-Cu binary alloy, hardness mappings revealed a limited decrease in mechanical properties. Microstructural characterisation using high-throughput scattering techniques (SAXS & WAXS) showed that this stability is due to a remarkable resistance of the θ’ precipitates to coarsening and limited θ’ to θ transformation, as validated by a precipitate hardening model. This stability suggests that minor alloying elements play a crucial role in thermal resistance. Atom probe tomography (APT) analyses showed that this stability may arise from mechanisms similar to those observed in recently developed ACMZ alloys, as Mn segregations were observed at the interfaces of the θ’ precipitates. However, unlike ACMZ alloys, at 300 °C, the stabilising mechanism is insufficient to preserve mechanical properties, suggesting a strong sensitivity of these mechanisms to compositional variations.
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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 | 0.000 | 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".