Thermal effects, flow front analysis and demolding characterization ofcarbon-fibre reinforced composites using Wet Compression Molding
Bibliographic record
Abstract
Abstract: Wet Compression Molding (WCM) is an efficient manufacturing process for continuous carbon-fibre reinforced polymers (CFRPs) due to its rapid cycle times, which makes it ideal for automotive and other large volume light-weighting applications. By utilizing WCM, a large complex part can be manufactured in approximately one minute. WCM requires a proper selection of process parameters such as resin temperature (°C), mold temperature (°C), resin set time (s) and cure time (s), to yield an optimally cured part with minimum voids or defects. During initial application and resin impregnation, thermosetting resins experience polymerization which is enhanced by the molding process. Thermal effects and flow front progression impact cure kinetics and ultimately, part quality. When evaluated with uniaxial flexural testing and demolding results, optimized mechanical properties can be achieved when material specifications are requested by industry. A statistical analysis approach was utilized in this study to correlate sample locations to part quality. Multi-factor regression and multi-variate analyses were conducted for 28 demonstrator plaques. Time-dependent temperature profiles were developed by coupling the Fluke® thermal images to the WCM cure cycles. Subsequently, both temperature and pressure profiles were correlated to the uniaxial flexural testing and demolding results. Parameter reduction using Principal Component Analysis (PCA) was invoked to reduce the input variable parameter set and reduce the need for resource-intensive experimental testing. This comparative study can then be used to expand and validate the full effects of processing parameters on sample location based on initial resin placement, wetted and non-wetted areas, and voids analysis. Simultaneously, a non-linear pattern recognition and categorial analysis will be completed using Artificial Neural Networks (ANNs). The objective of this study is to accelerate product development for composites.
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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 | 0.001 | 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) | 0.001 | 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 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".