Effect of Loading Angle on the Behavior of Fillet Welds
Bibliographic record
Abstract
The fillet weld design equation in the American and Canadian steel design standards is based upon research on the behavior of SMAW test specimens, therefore, the significant increase in weld strength recognized by these design standards as the loading angle increases might not be suitable for low toughness welds made with other welding processes such as FCAW, which is more commonly used for production welding. An experimental program was conducted to investigate the effect of filler metal toughness on fillet weld behavior. The first phase of this test program included only transverse fillet welds. The variables in the first phase included filler metal classification (both filler metals with a toughness requirement and some without were tested), electrode manufacturer, fabricator, weld size, root notch orientation, and test temperature. A reliability analysis of the test results (102 test specimens) indicated that the current design equation provides a safety index greater than 4.5 for transverse welds. The second phase, which formed the basis of the work presented in this paper, examined the effect of filler metal classification and toughness on the strength and ductility of fillet welds loaded at different angles with respect to their longitudinal axes. The results of this phase were obtained from 30 lap spliced specimens with nominally 12.7 mm (0.50 in.) fillet welds. Twenty-seven test specimens were prepared using the FCAW process. The specimens were loaded in three different directions with respect to the weld axis: 0°, 45° and 90°.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".