Precipitate evolution and related strengthening during long term ageing of 2618A aluminium alloy
Bibliographic record
Abstract
The 2618A alloy is used for aerospace applications where high resistance to long-term thermal ageing is required. Being able to predict the evolution of mechanical properties during long-term ageing is therefore crucial to determine the end-of-life of these products. It requires an in-depth understanding of the evolution of hardening precipitates during extended ageing, which happens along a complex sequence of metastable and stable phases. Here we used a high-throughput experimental methodology to gather systematic microstructure and related mechanical properties data resolved in time and temperature, using samples aged within a temperature gradient up to 10,000 h. Hardness maps were used to monitor the evolution of the mechanical properties along samples aged in this temperature gradient. Local transmission electron microscopy (TEM) with scanning precession electron diffraction (SPED) and atom probe tomography (APT) were used to identify the nano-precipitates for selected conditions, while systematic small angle X-ray scattering (SAXS) was used to evaluate the evolution of sizes and volume fractions of the precipitates along the graded samples. Our results evidence for the first time the important contribution of Si-containing L-phase to the initial strength of the T851. In the first stages of long-term ageing up to 5,000h at 200°C the strength loss is controlled by the partial dissolution of this L phase together with coarsening of S precipitates. Subsequently, the Q phase appeared to form, which destabilised partly the S phase and results in an accelerated loss of mechanical properties.
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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".