Recent Advances in Friction Stir Welding/Processing of Aluminum Alloys: Microstructural Evolution and Mechanical Properties
Bibliographic record
Abstract
Friction stir welding (FSW), a highly efficient solid-state joining technique, has been termed as “green” technology due to its energy efficiency and environment friendliness. It is an enabling technology for joining metallic materials, in particular lightweight high-strength aluminum and magnesium alloys which were classified as unweldable by traditional fusion welding. It is thus considered to be the most significant development in the area of material joining over the past two decades. Friction stir processing (FSP) was later developed based on the basic principles of FSW. FSP has been proven to be an effective and versatile metal-working technique for modifying and fabricating metallic materials. FSW/FSP of aluminum alloys has prompted considerable scientific and technological interest since it has a potential for revolutionizing the manufacturing process in the aerospace, defense, marine, automotive, and railway industries. To promote widespread applications of FSW/FSP technology and ensure the structural integrity, safety and durability of the FSW/FSP components, it is essential to optimize the process parameters, and to evaluate thoroughly the microstructural changes and mechanical properties of the welded/processed samples. This review article is thus aimed at summarizing recent advances in the microstructural evolution and mechanical properties of FSW/FSP aluminum alloys. Particular attention is paid to recrystallization mechanism, grain boundary characteristics, phase transformation, texture evolution, characteristic microstructures, and the effect of these factors on the hardness, tensile and fatigue properties as well as superplastic behavior of FSW/FSP aluminum alloys.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".