A comprehensive review of progressive developments and challenges in dissimilar welding of aluminum and magnesium alloy by friction stir welding
Bibliographic record
Abstract
Friction stir welding has emerged as a promising solid state joining technique for aluminum-magnesium alloys, widely used in autmotive, aerospace, and marine industries due to their excellent corrosion and strength-to-weight ratio. This review critically examines the mechanical properties, microstructural evolution of the welded joints. The novelty of this review is the correlation between grain refinement, welding parameters, and tool geometry on microstructural control. Despite these advantages, studies have reported the occurrence of intermetallic compound (IMCs) in FSW of Mg-Al joints, welding parameters, and input heat influence their amount and thickness. It underscores the importance of optimizing weld parameters to minimize the adverse effect of IMCs. While various interfaces in butt joints have been explored, the mechanism of interfacial interaction and ways to enhance joint quality have not been extensively reviewed. The objective of this review paper is to fill that gap by analyzing past research on microstructure interface evolution, joint mechanism, and welding parameters. It was observed that most research has focused on the structural morphology, technological feasibility, and mechanical properties of Al/Mg weldments, with limited attention to environmental degradation and operational failure. Future research must focus on developing strategies for IMC formation, optimizing welding parameters, and enhancing joint efficiency for reliable and sustainable use in industrial applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".