wear, composites, cotton fiber reinforced polyester composites, artificial neural network, pin-on-disc


The cotton fiber reinforced polyester composites were fabricated with varying amount of graphite fillers (0, 3, 5 wt.%) with a hand lay-up technique. Wear tests were planned by using a response surface (Box Behnken method) design of experiments and conducted on a pin-on-disc machine (POD) test setup. The effect of the weight percentage of graphite content on the dry sliding wear behavior of cotton fiber polyester composite (CFPC) was examined by considering the effect of operating parameters like load, speed, and sliding distance. The wear test results showed the inclusion of 5 wt.% of graphite as fillers in CFPC increase wear resistance compared to 3 wt.% of graphite fillers. The graphite fillers were recommended for CFPC to increase the wear resistance of the material. A scanning electron microscope (SEM) was used to study the wear mechanism. To predict the wear behavior of the composite material, comparisons were made between the general regression technique and an artificial neural network (ANN). The conformation test results revealed the predicted wear with the ANN was acceptable when compared with the actual experimental results and the regression mathematical models.


Tsinghua University Press