Development of Low Cost Fuselage Frames by Resin Transfer Molding
Bibliographic record
Abstract
This paper presents work on the development of a low cost fuselage C-frame for aircraft primary structure using a Light Resin Transfer Molding (RTM) process. Compared to labor intensive hand layup prepreg technologies, Light RTM offers some substantial advantages by reducing infrastructure requirements such as hydraulic presses or autoclaves. Compared to Prepreg, Light RTM tooling creates two finished surfaces, which is an advantage during installation due to improved dimensional accuracy. The focus of this work was to develop means of achieving high fiber volume fraction structural frames using low cost tooling and a low volume manufacturing strategy. In this case a three piece Light RTM mold was developed using an internal mandrel. To achieve the strength requirements, a combination of crimped and non-crimped fabrics were selected for the reinforcing preform. This made it possible to reduce the number of flat patterns by more than 8 times, saving layup time compared to the prepreg counterpart. The processing parameters were optimized to reduce cycle time. Permeability, in-situ resin cure monitoring and coefficient of thermal expansion tests were used in the numerical simulations. Process induced deformation and resin flow simulations were completed to provide input for the tool design. In addition to all the process improvements, risk mitigation testing was completed to validate the design allowables. This work was a collaborative effort by an integrated product development team consisting of design, stress and materials and processing functions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".