Mechanical characterization of particle reinforced jute fiber composite and development of hybrid Grey-ANFIS predictive model

Abstract

Natural fiber composites are a potential material in a range of engineering applications because of their excellent properties, which include reduced weight, better strength, and economic affordability. Aside from these features, these materials are biodegradable and renewable. The intent of this study is to look through into mechanical properties of aluminum oxide (Al2O3), boron carbide (B4C), and silicon carbide (SiC) particle-filled jute fiber polymer composite. The response surface methodology (RSM) with three levels/three factors is used to achieve the different combinations of process variables required to fabricate the desired polymer composites. In this regard, the effect of process variables on tensile characteristics, increase in weight %, and flexural characteristics is examined in detail. Further, the best combination of process parameters is chosen to produce composites with the desired mechanical qualities. The significance of such variables on each output variable is assessed using analysis of variance. A hybrid grey-based adaptive neuro-fuzzy inference system model is constructed for establishing multiple performance indexes. From the validation outcomes obtained, it is proved that the evolved model is proficient for precise prediction.