Abstract
In this article, plasma-sprayed coatings of borosilicate glass microspheres (BGM) premixed with TiO2 particles in different proportions are deposited 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 artificial neural network (ANN) approach is then implemented taking into account training and testing procedures to predict the triboperformance 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. This article proposes an integrated application of ANN and Taguchi's experimental design for analyzing and predicting the solid particle erosion wear response of a new class of coatings.
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