Secondary bonded pi-joint out of autoclave process
Bibliographic record
Abstract
Composite materials are widely used in the aerospace industry due to their high strength and stiffness properties, as well as the manufacturing possibilities they offer for large components at lower assembly costs.To further lower the manufacturing cost, the use of Out of Autoclave (OOA) process is increasing in popularity.However, mechanically joining parts is a necessary step in the assembly of a large component, driving up the weight of the component and the final assembly cost.A Pi-Joint is one way to offer lower assembly cost through secondary bonding while ensuring the joint's reliability due to the redundancy in the load path.Predicting the failure strength of a bonded joint is essential for the initial stages of aircraft structure design.In this research project, the OOA process is used to manufacture Pi-Joints using pre-impregnated carbon fibre fabric.The Pi-Joint is co-cured with the skin, followed by a secondary bond operation of the web onto the Pi-Joint and skin assembly.To assess the strength of the joint, four different manufacturing techniques are used.In addition, a finite element analysis technique is used to estimate the first mode of failure for the different configurations of the Pi-Joint.The failure strength is correlated with experimental test results to determine the reliability of the manufacturing techniques.Static strength analyses are carried out along with mechanical tests to assess the redundancy of the load path.It is shown in this research that the finite element modelling results are in agreement with the test results.I wish to express my sincere gratitude to my supervisor, Dr. Pascal Hubert,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".