Experimental analysis, simulation, and evaluation of process parameters of GFRP composites produced through resin transfer molding
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Bibliographic record
Abstract
Glass fiber reinforced composites are experiencing growing demand across various industries including aerospace, military, and transportation due to their superior mechanical properties compared to traditional materials. A custom Resin Transfer Molding (RTM) setup with a central resin injection system was developed to produce high-quality E-glass chopped strand/polyester composites with different volume fractions and resin injection pressures. Flow visualization techniques were employed to observe resin impregnation into the reinforcement and measure parameters such as filling time, flow front velocity, Reynolds number, permeability, and voids. In this study, three types of composites were fabricated using E-glass chopped strand fiber preforms (with 4, 5, and 6 layers) reinforced with polyester resin at five different resin injection pressures (P1 = 0.2 MPa, P2 = 0.25 MPa, P3 = 0.3 MPa, P4 = 0.35 MPa, and P5 = 0.4 MPa). Simulation studies were undertaken utilizing a control volume-based finite element method, employing commercially available RTM-Worx software to model resin flow behavior and determine Mold filling time. Mold filling times obtained from simulation studies at five selected injection pressures for the three composite types were compared with experimental results. The experimental values closely matched the simulation results with a deviation of only 2.26%. Additionally, impregnation velocities and Reynolds numbers derived from the simulation agreed with experimental results at the specified resin injection pressures. The mechanical properties of the molded laminates, including tensile strength, flexural strength, and impact strength, were evaluated according to ASTM standards. These properties are critical indicators of the composite’s performance in real-world applications. The results revealed that both resin injection pressure and the number of layers significantly affect the composite’s mechanical properties. The findings also highlighted the importance of selecting the appropriate injection pressure to minimize void formation and enhance fiber impregnation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it