Erosion Wear Response of Glass Microsphere Coatings: Parametric Appraisal and Prediction Using Taguchi Method and Neural Computation

Abstract

This article presents an analysis of the erosion wear response of borosilicate glass microsphere (BGM)-coated metal specimens subjected to reproducible erosive situations. The coatings are deposited on metal substrates by a plasma spraying route using an atmospheric plasma spray setup working on a nontransferred arc mode. The response of these coatings to solid particle erosion for different test parameters is studied. The erosion test schedule is planned as per Taguchi's experimental design and is carried out under controlled laboratory conditions using an air jet–type erosion tester. The analysis of test results reveals that the impact velocity is the most significant among various factors influencing the erosion wear rate of these coatings. A prediction tool based on artificial neural networks (ANNs) is then implemented to predict the triboperformance of such coatings in regard to their erosion rates under different test conditions. ANN is a technique that takes into account the training, testing, and validation protocols using the database generated from experimentation. This technique helps in saving time and resources for a large number of experimental trials and it is seen in this work that it can successfully predict the wear rate of the coatings for test conditions both within and beyond the experimental domain.