Friction stir welding/processing of metals and alloys: A comprehensive review on microstructural evolution
Bibliographic record
Abstract
The unique combination of very large strains, high temperatures and high strain rates inherent to friction stir welding (FSW) and friction stir processing (FSP) and their dependency on the processing parameters provides an opportunity to tailor the microstructure, and hence the performance of welds and surfaces to an extent not possible with fusion processes. While a great deal of attention has previously been focused on the FSW parameters and their effect on weld quality and joint performance, here the focus is on developing a comprehensive understanding of the fundamentals of the microstructural evolution during FSW/P. Through a consideration of the mechanisms underlying the development of grain structures and textures, phases, phase transformations and precipitation, microstructural control across a very wide range of similar and dissimilar material joints is examined. In particular, when considering the joining of dissimilar metals and alloys, special attention is focused on the control and dispersion of deleterious intermetallic compounds. Similarly, we consider how FSP can be used to locally refine the microstructure as well as provide an opportunity to form metal matrix composites (MMCs) for near surface reinforcement. Finally, the current gaps in our knowledge are considered in the context of the future outlook for FSW/P.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 0.002 |
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".