ANN Controlled Plasma Spray Process by using Industrial Waste

Abstract

Plasma spray coating process has become a subject of intense research because it becomes an affordable solution for many industrial applications. The present work aims at developing and studying the industrial wastes (Flay-ash powder mixture) as the coating material, which is to be deposited on Mild Steel and Cupper substrates. To increase the coating deposition efficiency and to decrease the cost of coating process, one of the artificial intelligence (AI) method i.e. Artificial Neural Network (ANN) technique used. By this parameter optimization technique, it is sufficient to describe approximation complex inter-relations in atmospheric plasma spray process. ANN technique helps in saving time and resources for experimental trials. The aim of this work is to outline a procedure for selecting an appropriate input vector in ANN coating efficiency models, based on statistical pre-processing of the data set. This methodology can provide deep understanding of various co-relationships across multiple parameter set, which could be essential for improvement of product and process performance. The aim of this article is to find optimum parameter for deposition efficiency. ANN experimental results indicate that the projection network has good generalization capability to optimize the deposition efficiency.