Optimization of Filler Content and Size on Mechanical Performance of Graphene/Hemp/Epoxy-Based Hybrid Composites using Taguchi with ANN Technique

The usage of nanofillers in composite materials has grown over time due to various benefits, including superior properties, better adhesion, and high stiffness. To accomplish this, 150, 200, 250, 300 gsm hemp fiber mat with thicknesses weight proportions graphene powder, 0%, 3%, 6%, 9%, as well 3, 6, 18, 25 µm-sized particles, were used. High-speed mechanical stirring was used evenly mix the nanofiller (nanographene) epoxy-based nanocomposites at loadings. We looked bending interlaminar shear strength (ILSS) properties hybrid nanomaterials. According study, adding epoxy composites filled 6 wt% nanographene significantly improved properties. development a forecasting model determine using artificial neural networks (ANN). constructed significant connection test findings. A correlation 0.9724 for Levenberg–Marquardt training procedure indicates between predicted experimental models. observational projected results ILSS have <3% 4% errors, corresponding ANN prediction Taguchi L16 matrix. potential is expanded by close relationship following parameters current study flexural strength: content (40.79%), size (34.19%), number layers (12.57%), thickness (11.65%). Similar ILSS, accounts 47.82% total, (27.87%), (11.80%), (also 11.80%) all contributing (11.78%).