Computational Approach for Optimizing Resin Flow Behavior in Resin Transfer Molding with Variations in Injection Pressure, Fiber Permeability, and Resin Sorption DOI Open Access
Pavan Hiremath,

Krishnamurthy D. Ambiger,

Priyadarshini Jayashree

et al.

Journal of Composites Science, Journal Year: 2025, Volume and Issue: 9(3), P. 129 - 129

Published: March 11, 2025

Resin transfer molding (RTM) is a key process for manufacturing high-performance fiber-reinforced composites, in which resin infiltration dynamics play critical role efficiency and defect minimization. This study presents numerical experimental analysis of flow biaxial noncrimp carbon fiber reinforcement using FormuLITE 2500A/2401B epoxy. A model based on Darcy’s law sorption effects was developed to investigate the influence injection pressure (15–25 kPa), permeability (350 × 10−12 m2 0.035 m2), porosity (0.78–0.58), viscosity (0.28–0.48 Pa·s), radius (0.001–0.003 m) flow-front progression. The results show that higher increased depth by 30% at 250 s, while 100× reduction reduced 75%. slowed ~18%, lower progression 15%. validation demonstrated relative error <5% between predictions measured data. provides insights into RTM optimization uniform impregnation

Language: Английский

Computational Approach for Optimizing Resin Flow Behavior in Resin Transfer Molding with Variations in Injection Pressure, Fiber Permeability, and Resin Sorption DOI Open Access
Pavan Hiremath,

Krishnamurthy D. Ambiger,

Priyadarshini Jayashree

et al.

Journal of Composites Science, Journal Year: 2025, Volume and Issue: 9(3), P. 129 - 129

Published: March 11, 2025

Resin transfer molding (RTM) is a key process for manufacturing high-performance fiber-reinforced composites, in which resin infiltration dynamics play critical role efficiency and defect minimization. This study presents numerical experimental analysis of flow biaxial noncrimp carbon fiber reinforcement using FormuLITE 2500A/2401B epoxy. A model based on Darcy’s law sorption effects was developed to investigate the influence injection pressure (15–25 kPa), permeability (350 × 10−12 m2 0.035 m2), porosity (0.78–0.58), viscosity (0.28–0.48 Pa·s), radius (0.001–0.003 m) flow-front progression. The results show that higher increased depth by 30% at 250 s, while 100× reduction reduced 75%. slowed ~18%, lower progression 15%. validation demonstrated relative error <5% between predictions measured data. provides insights into RTM optimization uniform impregnation

Language: Английский

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