A study on analysis and prediction of erosion response of plasma sprayed titania coatings

Abstract

This work presents successful implementation of Taguchi experimental design integrated with artificial neural networks (ANN) to develop a robust and efficient method of analyzing and predicting the erosion wear response of a new class of metal-glass coatings prepared by plasma spraying. Plasma spray technology utilizes the exotic properties of the plasma medium to effect physical, chemical or metallurgical reactions to produce new materials or impart new functional properties to conventional materials. Titania(TiO2 )are preferred as the coating material over irregular ones due to low surface area to volume ratio, high density, free flowing ability and close sizing etc. Coatings of this titania are deposited on mild steel 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 model based on experimental data that performs self-learning by updating weightings is proposed in this work. It takes into account training and test procedure to predict the erosion 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.