Effect of Alloying Elements on Intermetallic Formation during Friction Stir Welding of Dissimilar Metals: A Critical Review on Aluminum/Steel
Bibliographic record
Abstract
The main drawback of friction stir welding (FSW) dissimilar metals is the formation of intermetallic compounds (IMCs), which are brittle and affect the strength of the joint. The formation of these compounds is inevitable due to their low enthalpy of formation; however, their emergence is an indication of metallurgical bonding between dissimilar metals. This means that the determining factors of intermetallics should be optimal to ensure the formation of the joint and, at the same time, the performance of the joint. It is known that various parameters such as welding parameters, joint configuration, and tool geometry have an influence on the formation of these compounds. However, the influence of the base metal is not adequately addressed in the literature. The current review paper focuses on intermetallic formation during the friction stir welding of aluminum/steel (Al/St) alloys to explore how the types of alloys affect the thicknesses and morphologies of the intermetallics. Different structural steels and stainless steels were considered to see how they affect intermetallic formation when welded to different types of aluminum alloys. The thicknesses of the IMCs in the FSW of different aluminum/steel alloys were taken from the literature and averaged to provide insight into the contribution of the elements to IMC formation. Thermodynamic and kinetic analyses were used to explain this effect. Finally, the mechanism of intermetallic formation is explained to provide a useful guide for selecting dissimilar metals for welding using friction stir welding.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".