Effect of heat treatments on microstructural and mechanical characteristics of dissimilar friction stir welded 2198/2024 aluminum alloys
Bibliographic record
Abstract
The 3rd generation of aluminum–lithium (Al–Li) alloys provides a desirable combination of high mechanical properties and low density compared to their traditional counterparts, exempt of lithium. Therefore, providing a reliable joint between new and conventional aluminum alloys is crucial for making hybrid structures. The present study focuses on improving the mechanical properties of dissimilar AA2198/AA2024 joints using different heat treatments before and after welding. Tensile tests paired with digital image correlation (DIC) techniques and micro-hardness maps were performed to document the macro-scale and local mechanical behavior of the joints. As-welded joints demonstrated a similar yield strength, 30% lower than that of the base metals in T3 and T8 metallurgical states. As-welded joints failed at the AA2198 side in the heat affected zone (HAZ), parallel to the thermo-mechanically affected zone (TMAZ), region experiencing intense strain concentration and minimal hardness values. Post welding-heat treatments (PWHT) was found to successfully strengthen HAZ on the AA2198 side, without abnormal grain growth in the nugget and impairing the hardness properties on AA2024 side. This improvement in local mechanical properties on the AA2198 side was related to the re-precipitation of dissolved T1 (Al2CuLi) and θ (Al2Cu) during welding as characterized by differential scanning calorimetry (DSC) and microscopy analyses. However, PWHT joint variants demonstrated a reduction in total elongation and ultimate tensile strength due to intense strain localization on the AA2024 retreating side compared to a much more homogeneous strain distribution in the as-welded joints.
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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".