Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method

• Published in: Open Journal of Composite Materials (Irvine, CA) Vol. 2; no. 2; pp. 54 - 60
• Main Authors: Behera, Ajit, Mishra, S. C.
• Format: Journal Article
• Language: English
• Published: 01.04.2012
• Subjects: Coating; Deposition; Economics; Learning theory; Mathematical models; Networks; Neural networks; Spray coating
• ISSN: 2164-5612; 2164-5655; 2164-5655
• DOI: 10.4236/ojcm.2012.22008

Modern industrial technologies call for the development of novel materials with improved surface properties, lower costs and environmentally suitable processes. Plasma spray coating process has become a subject of intense research which attempts to create functional layers on the surface is obviously the most economical way to provide high per- formance to machinery and industrial equipments. The present work aims at developing and studying the industrial wastes (Flay-ash, Quartz and illmenite composite mixture) as the coating material, which is to be deposited on Mild Steel and Copper substrates. To study and evaluate Coating deposition efficiency, artificial neural network analysis (ANN) technique is used. By this quality control technique, it is sufficient to describe approximation complex of in- ter-relationships of operating parameters 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 vec- tors in ANN coating efficiency models, based on statistical pre-processing of the experimental data set. This methodology can provide deep understanding of various co-relationships across multiple scales of length and time, which could be essential for improvement of product and process performance. The deposition efficiency of coatings has a strong dependence on input power level, particle size of the feed material, powder feed rate and torch to substrate distance. ANN experimental results indicate that the projection network has good generalization capability to optimize the deposition efficiency, when an appropriate size of training set and network is utilized.