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.

中文翻译：

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使用 Taguchi 和 ANN 方法优化纳米 SiO2/香蕉纤维增强杂化复合材料的众多影响参数

纳米填料具有比强度高、强度重量比、成本低廉等优点，是目前天然纤维研究最多的课题。当前研究的主要目标是结合田口和人工神经网络 (ANN) 方法来最大化纳米复合材料的机械特性。参数：(i) 纳米二氧化硅₂ wt%，(ii) 香蕉纤维 wt%，(iii) 以 MPa 为单位的压缩压力，和 (iv) 以 °C 为单位的压缩成型温度被选择以实现上述目标。安大号₁₆基于田口技术，使用正交阵列优化工艺参数。根据预期的实验，评估了机械特性，例如拉伸、弯曲和冲击强度。ANN 用于预测优化的结果。香蕉纤维的纤维毡厚度和纳米SiO ₂的重量配比对杂化复合材料的力学性能有相当大的改善。根据田口技术，在 5% SiO 2 的情况下，最显着的机械特性为 47.36 MPa 拉伸、64.48 MPa 弯曲和 35.33 kJ_冲击、19 MPa 压力和 110 °C。ANN 预测的机械强度具有 95% 的准确度。人工神经网络预测比回归模型和实验数据更准确。上述纳米混合复合材料主要用于满足现代汽车领域的需求。