Friction stir welded joints in aluminum highway bridge decks: a quality control framework
Bibliographic record
Abstract
Friction stir welding (FSW) has shown considerable promise for highway aluminum bridge deck fabrication but lacks specific quality control guidelines for fit-up defects. This study conducts a performance-based (PB) quality control assessment of butt-lap FSW joints for highway bridge decks. Five FSW conditions were simulated, including a standard control welding condition, fit-up defects (gaps and tool offset), and a welding tool’s rotational direction inversion from the standard condition. A rigorous prequalification process established acceptable tolerance levels for fit-up defects: a 3 mm positive offset, a 1.5 mm negative offset, and a 1.5 mm gap. Subsequently, specimens from real aluminium deck extrusions, incorporating the various welding conditions were subjected to fatigue testing. Results showed that the fatigue strength and failure mode were primarily influenced by the weld root microstructure. The FSW tool’s rotational direction significantly influenced fatigue strength due to its impact on the nucleation of the hooking defect in the weld root area, while the impact of fit-up defects on fatigue strength was comparatively lesser. Furthermore, finite element analysis examined the impact of the geometrical features of the root microstructure and the direction of the initial crack propagation on the stress intensity factor range. These findings have practical implications for setting tolerance levels for fit-up defects in aluminum butt-lap FSW joints and optimizing of the quality and fatigue strength of butt-lap FSW joints.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".