In this present work the effect of stacking sequence on erosive wear behavior of untreated woven jute and glass fabric reinforced epoxy hybrid composites has been investigated experimentally. Composite Laminates were fabricated by hand lay-up technique in a mold and cured under light pressure for one hour, followed by curing at room temperature for forty eight hours. All the laminates were made with a total of 4 plies, by varying the number and position of glass layers so as to obtain six different stacking sequences. One group of only jute laminate was also fabricated for comparison purpose. The erosion rates of these composites have been evaluated at different impingement angles (30-90 0 ) and at three different particle speeds (v=48, 70, 82m/s).The erodent used is silica sand with the size range 150-250 µm of irregular shapes.The impingement angle was found to have a significant influence on the erosion rate. The composite material showed semi ductile behaviour with maximum erosion at 45 0 impingement angle. The morphology of the eroded surface was examined by SEM.It is conclude from the study that the erosive wear behavior of natural fiber jute can be improved significantly by hybridizing with synthetic fiber glass.
This article proposes the application of an artificial neural network (ANN) to a Taguchi orthogonal experiment to develop a robust and efficient method of analyzing and predicting the solid particle erosion wear response of a new class of metal-ceramic coatings. An ANN model based on data obtained from experiments performs self-learning by updating weightings and repeated learning epochs. In this work, plasma-sprayed coatings of fly ash premixed with aluminum powder in different weight proportions are deposited on aluminum substrates at various input power levels of the plasma torch. Erosion wear characteristics of these coatings are investigated following a plan of experiments based on the Taguchi technique, which is used to acquire the erosion test data in a controlled way. The study reveals that the impact velocity is the most significant among various factors influencing the wear rate of these coatings. An ANN approach is then implemented taking into account training and test procedure to predict the tribo-performance under different erosive wear conditions. This technique helps in saving time and resources for a large number of experimental trials and successfully predicts the wear rate of the coatings both within and beyond the experimental domain.
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