Refill Friction Stir Spot Welding Al Alloy to Copper via Pure Metallurgical Joining Mechanism
Bibliographic record
Abstract
Abstract The current investigation of refill friction stir spot welding (refill FSSW) Al alloy to copper primarily involved plunging the tool into bottom copper sheet to achieve both metallurgical and mechanical interfacial bonding. Compared to conventional FSSW and pinless FSSW, weld strength can be significantly improved by using this method. Nevertheless, tool wear is a critical issue during refill FSSW. In this study, defect-free Al/copper dissimilar welds were successfully fabricated using refill FSSW by only plunging the tool into top Al alloy sheet. Overall, two types of continuous and ultra-thin intermetallic compounds (IMCs) layers were identified at the whole Al/copper interface. Also, strong evidence of melting and resolidification was observed in the localized region. The peak temperature obtained at the center of Al/copper interface was 591 °C, and the heating rate reached up to 916 °C/s during the sleeve penetration phase. A softened weld region was produced via refill FSSW process, the hardness profile exhibited a W-shaped appearance along middle thickness of top Al alloy. The weld lap shear load was insensitive to the welding condition, whose scatter was rather small. The fracture path exclusively propagated along the IMCs layer of Cu 9 Al 4 under the external lap shear loadings, both CuAl 2 and Cu 9 Al 4 were detected on the fractured surface on the copper side. This research indicated that acceptable weld strength can be achieved via pure metallurgical joining mechanism, which has significant potential for the 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 | 0.000 | 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 | 0.001 | 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".