Hybrid SIO2/AL2O3 Filler Reinforcement in Glass Fiber/Epoxy Composites: Flexural and Tensile Behavior with Physics-Informed Neural Network Validation

Glass fiber-reinforced polymer (GFRP) composites are widely used in structural applications, yet the inherent brittleness of the epoxy matrix limits their mechanical performance. While SiO2 and Al2O3 have individually been shown to improve polymer composite properties, the synergistic effect of their combined use as a hybrid filler on flexural behavior remains insufficiently explored. In this study, five composite formulations were fabricated with a fixed SiO2 content of 1 wt.% and Al2O3 contents of 0, 0.5, 1.0, 1.5, and 2.0 wt.%. Three-point bending tests were conducted at four support spans, and tensile tests were performed; the resulting flexural strength data were further modeled using a Physics-Informed Neural Network (PINN). Flexural strength decreased progressively with increasing support span across all sample groups. The sample containing 1 wt.% Al2O3 exhibited the highest flexural strength and modulus, corresponding to an increase of approximately 21% over the mono-filler reference at the 60 mm span. Tensile strength increased progressively with Al2O3 content up to 1.5 wt.%, corresponding to an improvement of approximately 21% over the mono-filler reference, while mechanical performance under both loading modes declined at 2.0 wt.% due to particle agglomeration. The PINN model achieved high predictive accuracy, with R2 values of 0.98 and 0.96 and mean absolute percentage errors of 3.81% and 5.37% on the training and test sets, respectively. These findings demonstrate that hybrid SiO2/Al2O3 reinforcement can meaningfully improve GFRP mechanical performance, with the optimum filler content depending on the dominant loading mode.