Effect of process variables on the performance of glass fibre reinforced composites made by high pressure resin transfer moulding
Bibliographic record
Abstract
The CAFE regulations will require average fuel consumption of cars and light duty trucks to be reduced from 27 mpg to 54.5 mpg by 2025. In order to reach this requirement, automakers have to improve both the vehicle powertrain efficiency and the vehicle weight. Polymer composite materials are a preferred alternative to achieve the needed weight reduction by combining a higher strength to weight ratio than steel and aluminum, superior fatigue and corrosion resistance than metal and very good crashworthiness characteristics. However, high throughput and cost effective composite manufacturing processes are essential for high performance fibre reinforced polymer composites to penetrate the automotive market to their full potential. High Pressure Resin Transfer Moulding (HP-RTM) process is a new process based on the Resin Transfer Moulding (RTM) technology that enables the processing of very reactive resins in very short cycle times (< 5-10 min i.e. >25,000 parts per year) with various types of reinforcement (glass, carbon, natural fibre). With HP-RTM’s unique self-cleaning impingement mixhead, resins that react in less than 1 minute (fast-curing epoxy or polyurethane systems) can still be processed. This process, combining the high mechanical performance of RTM parts with short cure cycle, thus presents a great interest for automotive applications. In this study, the effect of the process parameters, such as the injection flow rate, the vacuum assistance sequence and mould gap control, on the mechanical performance and quality of HP-RTM composite plates was determined and HP-RTM process mapping was established from the obtained mechanical results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".