A modeling approach for prediction of erosion behavior of glass fiber-polyester composites

In recent years, a fairly good number of articles dealing in characterization of solid particle erosion of glass fiber reinforced composites are available but exhaustive study on this vital aspect leading to understand erosion phenomenon is hardly found in the literature. Therefore, in the present work, a theoretical model based on principle of conservation of particle kinetic energy is developed to determine wear rate of glass-polyester composites due to multiple impact erosion. Room temperature erosion tests are then carried out to study the effect of various control factors in an interacting environment on the erosion behavior of these composites. For this purpose, design of experiments approach utilizing Taguchi's orthogonal arrays is adopted to test the specimens on air jet type erosion test configuration. The results indicate that erodent size, fiber loading, impingement angle and impact velocity are the significant factors in the order of their influence on wear rate. Taguchi approach enables to determine optimal parameter settings that lead to minimization of erosion rate. Artificial neural network (ANN) approach is applied to the erosive wear data to reach at acceptable predictive models. Scanning electron microscopy of the eroded surface of the composites is performed for observation of the features such as crack formation, fiber fragmentation and matrix body deformation. Finally, popular evolutionary approach known genetic algorithm (GA) is used to generalize the method of finding out optimal factor settings for minimum wear rate.