Artificial Neural Network and Response Surface Methodology Based Analysis on Solid Particle Erosion Behavior of Polymer Matrix Composites

Abstract

Polymer-based fibrous composites are gaining popularity in marine and sports industries because of their prominent features like easy to process, better strength to weight ratio, durability and cost-effectiveness. Still, erosive behavior of composites under cyclic abrasive impact is a significant concern for the research fraternity. In this paper, the S type woven glass fibers reinforced polymer matrix composites (PMC s ) are used to analyze the bonding behavior of reinforcement and matrix against the natural abrasive slurry. The response surface methodology is adopted to analyze the effect of various erosion parameters on the erosion resistance. The slurry pressure, impingement angle and nozzle diameter, were used as erosion parameters whereas erosion loss, i.e., weight loss during an erosion phenomenon was considered as a response parameter. The artificial neural network model was used to validate the attained outcomes for an optimum solution. The comparative analysis of response surface methodology (RSM) and artificial neural network (ANN) models shows good agreement with the erosion behavior of glass fiber reinforced polymer matrix composites. outer structure faces surface erosion because of the existence of highly abrasive particles in the air [8,9]. The erosion of fibrous composites depends upon various process parameters such as impacting particles, angle of impingement, velocity, etc. Tewari et al. [10] reported that erosion of fibrous composites is significantly influenced by fiber orientation because unidirectional fiber orientation exhibits semi ductile erosion behavior. Pool et al. [11] reported that polymer composite shows brittle erosion behavior whereas permanent aramid fiber epoxy shows quasi ductile behavior. The increase in weight fraction of glass fiber as reinforcement can convert ductile erosion behavior into brittle behavior [12]. The effect of particle size during erosion is also analyzed and reported that erosion rate of the composite surface is directly proportional to the size of impacting particle and sometimes becomes zero by using particle size of the range of 2 µm [13]. Miyazaki et al. [14] revealed that fiber-reinforced polymers (FRPs) having treated fibers exhibits better resistance against erosion than non-treated fibers because of higher interfacial strength between matrix and reinforcement. The angle of impingement significantly affects the erosion rate of the composite surface. Rajesh et al. [15] revealed that the impact of an abrasive particle at an oblique angle (30 • ) is much more influencing than the normal impact angle (90 • ). The optimization of process parameters for tribological behavior has been performed by various scientists using several techniques like the Taguchi's methodology [16], response surface methodology [17], artificial neural network [18], genetic algorithm [19], etc. Response surface methodology (RSM) is employed as an optimization tool for the modeling and optimization of single as well as multi-objective optimization [20]. In recent times, several r...

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