Similar and Dissimilar Ultrasonic Spot Welding of 5754 Aluminum Alloy for Automotive Applications
Bibliographic record
Abstract
Aluminum (Al) alloys are increasingly used in the transportation industry to reduce the weight of vehicles due to their high strength-to-weight ratio. These applications unavoidably involve similar and dissimilar joining of an automotive grade 5754 Al alloy to manufacture multi-material vehicle body structures and parts. Ultrasonic spot welding (USW), an emerging and promising solid-state joining technology, can be suitably applied to join Al alloys. In this study, 5754 Al alloy was welded in similar (Al5754-Al5754) and dissimilar (Al5754-ZEK100 Mg alloy, Al5754-HSLA steel) configurations at varying levels of welding energy. It was observed that USW had a strong effect on the interface microstructure, with fine grains present at the weld interface via dynamic recrystallization in the similar welding, while an interface diffusion layer formed in the dissimilar welding. The tensile lap shear strength increased with increasing welding energy, reached its optimum value, and then decreased with further increasing welding energy. The strength of dissimilar Al5754-ZEK100 and Al5754-HSLA steel joints was about 55% and 88% of that of the similar Al-Al joints, respectively. The dissimilar Al5754-HSLA steel joints exhibited the longest fatigue life due to the reduced stress concentration and additional strengthening arising from the brazing effect of the squeezed-out Al-Zn eutectic structure at the nugget edge.
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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 | 0.000 | 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".