Successful Round Robin Analyses Resulting from the Engineered Residual Stress Implementation Working Group
Bibliographic record
Abstract
Abstract The application of engineered residual stresses (ERSs) on aircraft structure provides an opportunity to significantly extend the total fatigue life of critical components. In order to reach required service life goals within budgetary constraints, the ability to implement ERS into analyses is essential. However, it has been repeatedly demonstrated that in order to properly quantify, apply, and analyze ERSs, sophisticated analytical tools, advanced technical knowledge, and specialized training are required. The ERS implementation (ERSI) working group provides the opportunity for collaborative development of best practices for government, contractors, and engineers supporting the implementation of ERSs into life predictions. The ultimate goal of the working group is to develop a more holistic framework for the implementation of ERS, with validated tools and processes for application to aircraft structures, minimizing expensive test programs, and offering benefits to all stakeholders. The ERSI Fatigue Crack Growth Analysis Method committee has taken the initiative to develop round robin fatigue life predictions for cold expanded holes. An initial round robin effort was completed to quantify the epistemic uncertainties in the prediction of fatigue crack growth, given a fixed set of input data. The results of this round robin are presented, including the variations in the predictions and comparison with test results, as well as lessons learned and best practices.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".