Experimental Analysis and Modeling Investigation of Precipitation Kinetics and Hardening in two Al-Zn-Mg-Cu Alloys
Bibliographic record
Abstract
The effects of various thermal processing routes on the precipitation hardening behavior and microstructural characteristics of AA7075 and a developmental AA7xxx alloy (D-7xxx) are investigated using multi-scale characterization and modeling techniques. For the AA7075 alloy, two general thermal processing histories are investigated: (a) solutionizing and water-quenching (WQ), or (b) die-quenching (DQ) or forced-air quenching (FAQ) process, all of which were followed by either natural aging or multi-step aging treatments. The multi-step aging treatments include natural aging, followed by intermediate-temperature aging, to achieve pre-aged tempers prior to the final artificial aging step. To investigate natural aging, the strengthening behavior of the water-quenched D-7xxx alloy and the natural aging of water-quenched and pre-aged AA7075 are also studied. The primary precipitation process during the natural aging of the as-water-quenched AA7075 alloy is the nucleation of natural aging Zn-Mg precipitates. The pre-aging process, prior to natural aging, reduces the capacity for precipitate formation and hardening rate of the AA7075 alloy during the room-temperature holding period. Similarly, the die-quenching process applied to AA7075 results in slower kinetics of subsequent natural aging and higher hardness in the as-quenched state compared to the WQ and FAQ conditions. These changes in material behavior are related to the effects of pre-aging precipitation or the presence of dislocations formed during the die-quenching process, which affect the rate of nuclei formation at room temperature. A modeling methodology is introduced to analyze the precipitation kinetics and yield strength evolution during the natural aging of variously processed Al-Zn-Mg-(Cu) alloys. The analysis of the combined modeling and experimental results for the multi-step aging treatments of the AA7075 alloy in DQ, FAQ, and WQ tempers suggests that dislocations formed during the die-quenching process enhance the hardening response of the DQ alloy after a pre-aging treatment (DQ+PA) compared to the similarly aged material after water-quenching or forced-air quenching. After the final stage of aging, the material in the DQ+PA condition exhibits a lower hardness value than the similarly aged WQ and FAQ samples. The recovery of dislocations and the interactions between solutes, vacancies, and fine precipitates with dislocations reduce the hardening response of the alloy in the DQ+PA condition during the subsequent aging treatment. The kinetics of precipitation hardening during the final aging step is also highly affected by dislocation-enhanced precipitation. Microstructure-strength modeling relationships are introduced to predict the evolution of microstructure and the strengthening response of the AA7075-WQ alloy in pre-aged conditions, as well as during subsequent artificial aging treatments. These modeling approaches are further expanded to include the effects of dislocation-enhanced precipitation and dislocation recovery on the kinetics of precipitation and the strengthening behavior during the artificial aging treatment of the alloy in the DQ+PA condition. The validity of these models is verified by the good agreement between the model predictions and the results from the experimental investigations.
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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 | 0.001 | 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".