Review of the Quench Sensitivity of Aluminium Alloys: Analysis of the Kinetics and Nature of Quench-Induced Precipitation
Bibliographic record
Abstract
For aluminium alloys, precipitation strengthening is controlled by age-hardening heat treatments, including solution treatment, quenching, and ageing. In terms of technological applications, quenching is considered a critical step, because detrimental quench-induced precipitation must be avoided to exploit the full age-hardening potential of the alloy. The alloy therefore needs to be quenched faster than a critical cooling rate, but slow enough to avoid undesired distortion and residual stresses. These contrary requirements for quenching can only be aligned based on detailed knowledge of the kinetics of quench-induced precipitation. Until the beginning of the 21st century, the kinetics of relevant solid-solid phase transformations in aluminium alloys could only be estimated by ex-situ testing of different properties. Over the past ten years, significant progress has been achieved in this field of materials science, enabled by the development of highly sensitive differential scanning calorimetry (DSC) techniques. This review presents a comprehensive report on the solid-solid phase transformation kinetics in Al alloys covering precipitation and dissolution reactions during heating from different initial states, dissolution during solution annealing and to a vast extent quench-induced precipitation during continuous cooling over a dynamic cooling rate range of ten orders of magnitude. The kinetic analyses are complemented by sophisticated micro- and nano-structural analyses and continuous cooling precipitation (CCP) diagrams are derived. The measurement of enthalpies released by quench-induced precipitation as a function of the cooling rate also enables predictions of the quench sensitivities of Al alloys using physically-based models. Various alloys are compared, and general aspects of quench-induced precipitation in Al alloys are derived.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".