Experimental evaluation and estimation of frictional behavior of polymer matrix composites

As the fiber-reinforced polymer matrix composites give good strength and can work in rigorous environmental conditions, nowadays, more focus is given to study the behavior of these materials under different operating conditions. Due to the environmental concern, the focus on the natural fiber reinforced polymer matrix composite is enhancing both in research and industrial sectors. Currently, the focus has been given to unifying solid fillers with the polymer matrix composite to improve their mechanical and tribo properties. Aligned to this, the present work discusses the effect of various weight fractions of fillers (Flyash, SiC, and graphite) on the frictional behavior of natural fiber (cotton) polyester matrix composites. The specimen prepared with a hand lay-up process followed by compression molding. A plan of experiments, response surface technique, was used to obtain a response in an organized way by varying load, speed, and sliding distance. The test results reveal that different weight concentration of fillers has a considerable result on the output. The frictional behavior of materials evaluated by general regression and artificial neural network. The validation experiment effects show the estimated friction by using the artificial neural network was closer to experimental values compare to the regression models.

Keywords:

Coefficient of Friction , Composites , Response surface method , Artificial neural network , Pin on disc