Friction Stir Welding of Aluminum for Automotive Closure Panel Applications
Bibliographic record
Abstract
Friction stir welding (FSW) shows advantages for joining lightweight alloys for automotive applications. In this research, the feasibility of friction stir welding aluminum for an automotive component application was studied. The objective of this research was to improve the Friction Stir Spot Welding (FSSW) technique used to weld an aluminum closure panel (CP). The spot welds were made using the newly designed swing-FSSW technique. In a previous study (unpublished), the panel was welded from the thin to thick side using both an 8 mm and a 10 mm diameter tool. The 10 mm tool passed various fatigue tests; however, the target was to improve performance of the 8 mm tool, especially to increase the number of cycle before the first crack appearance during fatigue testing. In this study fatigue tests and static strength was recorded for weld specimens that were welded from thick-to-thin with an 8 mm diameter tool. Coach peel coupons were welded to help optimize weld parameters and determine weld failure type (failure mode). Panels were welded using a multi-articulate robot and were fatigue tested, using a previously developed technique to 100,000 cycles. Fatigue tests showed improved fatigue life than had been previously achieved with 8 mm tool for thin-to-thick panel welding. Energy consumption for swing-FSSW panels was less than 700 J/weld, thus swing-FSSW can help reduce energy consumption and help contribute to Green Manufacturing.
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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) | 0.003 | 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".