Gradient-based intuitive search intelligence for the optimization of mechanical behaviors in hybrid bioparticle-impregnated coir-polyester composites

The present investigation is focused on the optimization of mechanical behaviors of hybrid bioparticle-impregnated natural coir-polyester composites using gradient-based Intuitive Search Intelligence. The bioparticles, such as rice husk and red mud, were used as particle reinforcements along with natural green husk coir fibers-reinforced polyester matrix. The tensile, flexural, and impact tests were carried out on the prepared composite sheets as per ASTM standards. The statistical inference analysis was performed to study the effect of fiber length, fiber content, and particulate content on the mechanical behaviors of the composites. The correlation between fabrication parameters and mechanical behaviors was formed by using data-based regression modeling in terms of polynomial expressions. The optimum values of mechanical behaviors were obtained by using a conventional optimization algorithm called the Reduced Gradient Method. After obtaining optimum results, the better value of mechanical behaviors was searched individually and simultaneously by means of Intuitive Search Intelligence. The effectiveness of the search was validated by using confirmation experiments on an obtained set of fabrication conditions. J. VINYL ADDIT. TECHNOL., 23:275-283, 2017. (c) 2015 Society of Plastics Engineers