Experimental investigation and prediction of wear behavior of cotton fiber polyester composites

Abstract

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.

Influence of fiber surface treatment on the tribological properties of Calotropis gigantea plant fiber reinforced polymer composites

“…[31] The NaOH treated and untreated fiberreinforced composites were analyzed for their tribological behavior and found that the more influential factors are in the order of load, speed, and sliding distance. [32] From the experimental investigation of abrasive wear for Ipomoea epoxy composites filled with particulates, it was found that the rate of wear decreased gradually with the increase in sliding distance and reached saturation when F I G U R E 1 (A) Calotropis plant; (B) stems; and (C) raw fiber extracts multiple passes were given. Ramie fiber reinforced epoxy composites were subjected to tribological behavior analysis, specifically CoF, and it was found that they had undergone an increase in CoF when the applied load on the fiber increased.…”

“…[31] The NaOH treated and untreated fiberreinforced composites were analyzed for their tribological behavior and found that the more influential factors are in the order of load, speed, and sliding distance. [32] From the experimental investigation of abrasive wear for Ipomoea epoxy composites filled with particulates, it was found that the rate of wear decreased gradually with the increase in sliding distance and reached saturation when…”

Estimation of adhesive wear behavior of the glass fiber reinforced polyester composite materials using ANFIS model

“…Wear is acceptably predicted by regression models and ANNs model. 10 Dry friction wear testers determine abrasive wear levels. ANNs and particle swarm optimization (PSO) based prediction tools estimates tribological and mechanicalproperties; such as abrasion resistance, impact energy, tensile modulus and hardness; of the composites.…”

Artificial neural network modeling of sliding wear

“…14 One of the main obstacles associated with applications of ANNs to physical processes like wear is the difficulty of explaining functional interrelations (between the wear rate and the physical variables governing the process) as they are predicted by ANN approximations. 15,16 To address this so-called ''black box'' issue of ANN modeling approach, a number of problem-specific ANN-based models of hybrid type have been developed. [17][18][19] In the present paper, we consider the main problem associated with the application of multilayer perceptron (MLP) for analyzing experimental data obtained from pin-on-disk sliding wear tests.…”

“…14 One of the main obstacles associated with applications of ANNs to physical processes like wear is the difficulty of explaining functional interrelations (between the wear rate and the physical variables governing the process) as they are predicted by ANN approximations. 15,16…”