In today’s world, there is a growing emphasis on using fiber-reinforced composites carefully across diverse sectors like automotive, construction, machinery and appliances. These materials are valued for their capacity to be efficiently repurposed, recycled or disposed of with minimal ecological consequences. Thus, integrating sustainable, eco-friendly and environmental mindful approaches into developing new materials and processes has become increasingly imperative. This study aims to analyze the impact strength, inter laminar shear strength and water absorption capacity of the jute fiber composite filled with the particles like alumina (Al2O3), boron carbide (B4C) and silicon carbide (SiC). The response surface methodology (RSM), employing three levels and three parameters, enables the identification of distinct combinations of input parameters essential for the fabrication of suitable polymer composites. The effects of process factors on interlaminar shear strength (ILSS), impact strength and % weight increase are examined. For each output measures, the connotation of the input parameters is determined by analysis of variance (ANOVA). To establish multiple performance indexes, a hybrid model called adaptive neuro-fuzzy inference system (ANFIS) based on Grey theory is developed. The model’s capability is validated and the results confirm the effective prediction of the preferred performance indicator.

中文翻译：

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颗粒增强黄麻纤维/环氧杂化复合材料的层间剪切和冲击性能

当今世界，人们越来越重视在汽车、建筑、机械和电器等不同领域仔细使用纤维增强复合材料。这些材料因其能够有效地重新利用、回收或处置而具有最小的生态后果而受到重视。因此，将可持续、生态友好和环保的方法融入到新材料和新工艺的开发中变得越来越必要。本研究旨在分析填充氧化铝（Al）等颗粒的黄麻纤维复合材料的冲击强度、层间剪切强度和吸水能力。2氧3）、碳化硼（B4C) 和碳化硅 (SiC)。响应面方法 (RSM) 采用三个水平和三个参数，能够识别制造合适的聚合物复合材料所必需的输入参数的不同组合。研究了工艺因素对层间剪切强度 (ILSS)、冲击强度和重量增加百分比的影响。对于每个输出度量，输入参数的内涵由方差分析 (ANOVA) 确定。为了建立多个性能指标，开发了一种基于灰色理论的称为自适应神经模糊推理系统（ANFIS）的混合模型。该模型的能力得到了验证，结果证实了对首选性能指标的有效预测。