SNAP RTM: a cost-effective compression RTM variant to manufacture composite component for transportation applications
Bibliographic record
Abstract
The Corporate Fuel Average Efficiency (CAFÉ) regulation requires average fuel consumption of cars to increase from 37.8 mpg (6.2 L/100 km) to 54.5 mpg (4.3 L/100 km) by 2025. One solution to help reach this target is vehicle lightweighting. As a reference, a 10% reduction in vehicle weight can result in a 5 – 8% reduction of fuel consumption. Among the lightweight material alternatives, fibre reinforced composite materials are believed to enable car body-weight reductions of 25% to 50%, weight reduction that cannot be obtained with lightweight metals alone. From an industrial point of view, process cycle time and cost are the main barriers to a wider use of fibre reinforced composites in mass produced vehicles, as current high performance composite manufacturing processes do not meet the 2 to 5 minutes cycle time desired by the transportation industry for the production of large series components. In order to benefit from the performance of advanced composites needed to achieve significant reductions in vehicle weight, it is then necessary to develop rapid and cost-effective processing techniques adapted to these materials. In recent years, material suppliers have been reducing the cure time required for thermoset resins that have helped to shorten cycle times targeted by the automotive industry. Among the manufacturing technologies available to produce high performance composites parts, liquid moulding technologies appear to have some potential to successfully introduce those rapid cure resin systems at faster production rates. In this study, a cost effective variant of the Compression-RTM process has been developed to manufacture high performance composite plaques. The injection and compression parameters were studied and compared to traditional RTM moulding: cycle time, part quality and part mechanical performance. Results validated the use of low cost / low pressure injection equipment combined with a static mix head and innovative tool design to manufacture composite plaques within cycle times targeted by the transportation industry.
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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 | 0.000 | 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) | 0.000 | 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".