Friction Stir Welding of Al-Li AA2199: Parameters, Precipitates and Post Weld Heat Treatment
Bibliographic record
Abstract
The aerospace industry strives to develop materials allowing an increase in payload and reducing fuel consumption. Al-Li alloys, with their low density and high strength are currently in use for such applications and have potential for additional applications. When compared to composites, utilizing Al-Li alloy products is cost effective for aerospace companies as they do not need to redesign pre-existing fabrication facilities. The joining of these alloys by conventional methods is limited by segregation of alloying elements and the formation of oxides during high temperature exposure. This study focuses on solid state joining method that has the potential to generate low heat and be defect free - Friction Stir Welding (FSW). AA2199 sheets were joined by FSW. Process variables included table force, tool rotation speed and weld travel speed. A post weld heat treatment (PWHT) was applied to improve the mechanical properties by precipitation of strengthening phases. An increase in hardness of the weld zone from 95HV to 125HV upon PWHT was recorded for selected welding conditions. The type and morphology of second phase precipitates is deemed responsible for this effect. It is suggested that the high temperature and high strain levels characteristic of welds with fast tool rotation allow for the dissolution of precipitates during welding. The re-precipitation of these second phases during PWHT allowed the welds to recover strength to the level of the base material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".