Statistical modeling and optimization of density in a novel carbon/jute fiber reinforced hybrid composite

Abstract

Nowadays, researchers are inclined toward developing lightweight materials with high mechanical and tribological properties. The density of the material plays a significant role in influencing the weight and tribological behaviour of developed composite. The present work has developed a novel hybrid composite consisting of carbon and jute fiber as reinforcement and porcine bone powder (PBP) as filler. Statistical model using response surface methodology technique has been developed to predict the density (ρ) of the material. Further, the influence of various factors such as wt.% of PBP powder, the particle size of PBP, and the effect of alkali treatment of jute fiber on the density of the material have been studied. The Central composite design (CCD) approach was used to generate a set of 20 experiments, and the analysis of variance (ANOVA) technique was used to study the effect of factors having a significant effect (i.e., p > 0.05) on the density of the material. The experiments revealed that among the various factors, the influence of bone particle size was most significant (68.3%) on the material density, followed by the size of bone wt.% (17.4%). Whereas alkali treatment has the least influence (7.8%) on the density of a material. Further, optimization was performed using the genetic algorithm (GA) to determine the optimum parameters.