Organic Matrix Composites Process-to-Performance, Evaluation, Research and Analysis (OPPERA)
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2424.vid Reliable bonded composite structures have the potential to improve performance, reduce costs, and increase design flexibility in advanced aircraft systems. One promising robust solution is to use 3D woven textiles that are near-net-shape to connect stiffeners to wing skins. This architecture removes the delamination paths of other joint systems by including fiber tows that are integrally woven through the preform to bind the out-of-plane directions together. Manufacturing and processing variations lead to variability in resin content, residual stresses, and porosity defects, which ultimately influence the strength of the bonded structure. In addition, static and fatigue loading damage progression is not well understood in 3D textiles and there are no validated predictive models. An integrated computational materials engineering (ICME) framework is under development to predict the strength of the bonded structure by integrating models to predict (1) fiber bed compaction, (2) material properties, residual stresses, and porosity evolution during cure, (3) damage evolution at the mesoscale and macroscale, and (4) final part capability. This paper describes an overview of current tools and predictive capabilities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.026 |
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".