The use of fiber reinforced polymer matrix composites (FRPCs) is in boom in many structural, industrial, automotive, and engineering applications. Polymer matrix composites have been turned out the most hopeful material which can replace the conventional materials, metals, and woods. Owing to this the demand for analyzing the tribological behavior of FRPCs is amplified. In the current article an inclusive literature survey on the tribological behavior of FRPCs in terms of friction and wear properties of composite materials is explored. The paper reviews the effects of different operating parameters and material parameters on wear rate and frictional behavior of FRPCs. The analysis reveals that operating parameters like sliding velocity, sliding distance, load, temperature and material parameters like a fiber volume fraction, orientation of fibers, fiber length, filler content, and effect of surface treatment have a significant effect on the tribological behavior of composite material. The wear rate of FRPCs is controlled by adding the proper amount of filler content and fiber orientation.

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Abstract: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.

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