Optimizing Numerous Influencing Parameters of Nano-SiO2/Banana Fiber-Reinforced Hybrid Composites using Taguchi and ANN Approach

Abstract

High specific strength, strength-to-weight ratio, cheap cost, and other advantages, nanofillers are now the subject of most research on natural fibers. The current research’s main goal is to combine the Taguchi and artificial neural networks (ANN) approaches to maximize the mechanical characteristics of nanocomposites. The parameters: (i) nano-SiO2 wt%, (ii) banana fiber wt%, (iii) compression pressure in MPa, and (iv) compression molding temperature in °C were selected to achieve the objectives above. An L16 orthogonal array was used to optimize the process parameters based on the Taguchi technique. According to the intended experiment, mechanical characteristics, such as tension, bending, and impact strength, were assessed. The ANN was used to forecast outcomes that were optimized. The fiber mat thickness of banana fiber and the weight ratio of nano-SiO2 showed a considerable improvement in the mechanical characteristics of hybrid composites. According to the Taguchi technique, the most significant mechanical characteristics were 47.36 MPa tensile, 64.48 MPa flexural, and 35.33 kJ of impact under circumstances of 5% SiO2, 19 MPa pressure, and 110 °C. With 95% accuracy, ANN-predicted mechanical strength. The ANN forecast was more accurate than the regression model and experimental data. The above nanobased hybrid composites are mainly employed to satisfy the needs of the contemporary vehicle sector.