Ultrasonic Spot Welding of an Aluminum Alloy for Automotive Applications
Bibliographic record
Abstract
To reduce fuel consumption and the resulting environment-damaging, climate-changing, costly, human death-causing emissions, lightweight aluminum alloys have been increasingly used in the transportation industry due to their low density, high specific strength, superior ductility, machinability, recyclability, and environmental friendliness. The structural applications of such aluminum alloys in the automotive industry unavoidably entail welding and joining process. While it is challenging to weld aluminum sheets via the conventional resistance spot welding developed mainly for joining steel sheets, an emerging solid-state joining technology known as ultrasonic spot welding (USW) is promising for joining aluminum alloys. This study was aimed to examine the feasibility of welding a 6022 Al alloy similarly (AA6022-to-AA6022) and dissimilarly (AA6022-to-ZEK100) in relation to welding energy. It was observed that there was a significant change in the interface grain size in the similar welding, exhibiting a characteristic fine-grained “necklace”-like structure along the welding line, while an intermetallic diffusion layer was present in the dissimilar welding of aluminum-to-magnesium alloys, and its thickness increased as the welding energy increased. The tensile lap shear strength first increased, reached its peak value at a certain energy, and then decreased with increasing welding energy. The strength of the dissimilar welded joints was about 55% of that of the similar welded joints. Three different modes of interfacial failure, nugget pull-out, and transverse through-thickness (TTT) crack growth were observed in the similar welded joints, while only interfacial failure was present in the dissimilar welded joints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".