Interference Fit Fasteners: a Finite Element Process Modeling Round Robin
Bibliographic record
Abstract
Abstract Interference fit fasteners (IFFs) have been widely used in the aircraft industry for several decades and have been shown to provide benefit to fatigue performance. However, the interference and potential plastic deformation near the hole create complications when trying to account for the benefits of IFFs in fatigue predictions. This paper documents new and original results from a recent round robin effort regarding finite element (FE) process modeling of IFFs. Submissions were received from eight participants across seven different organizations and included the use of five different FE software packages. The problem statement involved a 2024-T351 aluminum alloy dogbone sample with a centered hole and a steel IFF with three different levels of interference. In addition to the varying levels of interference, applied remote loading was also considered with three different levels. The round robin included a phased approach with increasing complexity. The results provide useful insight into the stress state near an interference fit hole and represent a comprehensive set of analysis data for use in future validation efforts against measurement data from representative experimental tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".