Application of Probablistic Neural Network for the Development of Wear Mechanism Map for Glass Fiber Reinforced Plastics

Abstract

Glass fiber reinforced plastic composite materials are finding increased applications due to their excellent properties. Fiber reinforced plastic (FRP) composite materials comprise soft matrix and fiber elements. This article presents the tribological aspects of glass fiber reinforced plastic composites. The wear test is carried out for FRP in a pin-on-roller wear tester. The wear rate obtained from different sliding speeds and normal pressure are plotted as a contour map. The scanning electron microscopy images are taken to study the deformations that occurred in the wear zone. The regions of different wear mechanisms are identified using scanning electron microscopy. The regions of wear mechanisms are classified using probabilistic neural networks and superimposed over wear rate contours. The wear mechanisms observed using scanning electron microscopy, along with wear rate data, are used for the construction of wear mechanism maps.

Keywords

fiber reinforced plastics tribology wear mechanisms probabilistic neural network.

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