Open Journal of Composite Materials

Volume 2, Issue 2 (April 2012)

ISSN Print: 2164-5612   ISSN Online: 2164-5655

Google-based Impact Factor: 2.33  Citations  

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

Full-Text HTML  Download Download as PDF (Size: 548KB)  PP. 54-60  
DOI: 10.4236/ojcm.2012.22008    5,189 Downloads   9,794 Views   Citations

ABSTRACT

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.

Cite this paper

A. Behera and S. Mishra, "Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method," Open Journal of Composite Materials, Vol. 2 No. 2, 2012, pp. 54-60. doi: 10.4236/ojcm.2012.22008.

Cited by

[1] Effect of Addition of Multimodal YSZ and SiC Powders on the Mechanical Properties of Nanostructured Cr2O3 Plasma-Sprayed Coatings
Journal of Thermal Spray Technology, 2019
[2] Plasma Sprayed Red Mud-Fly Ash Composite Coatings on Mild Steel: A Comprehensive Outline
2019
[3] Process Optimization of Slurry Spray Technique Through Multi-attribute Utility Function
Arabian Journal for Science and Engineering, 2018
[4] Design, Fabrication, and Characterization of an Indigenously Fabricated Prototype Transferred Arc Plasma Furnace
2018
[5] Behavior of an indigenously fabricated transferred arc plasma furnace for smelting studies
Plasma Science and Technology, 2018
[6] Comparison of Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) Modeling Approaches in Predicting the Deposition Efficiency of Plasma …
Journal of Advanced Microscopy Research, 2017
[7] Finite Element Simulation of Residual Stress Development in Thermally Sprayed Coatings
Journal of Thermal Spray Technology, 2017
[8] Comparison of Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) Modeling Approaches in Predicting the Deposition Efficiency of Plasma …
Journal of Advanced Microscopy Research, 2017
[9] 樹脂溶媒に分散した微粒子材料を用いたガスフレーム溶射法による皮膜形成の開発と応用に関する研究
2016
[10] Functional NiAl-graphene oxide composite as a model coating for aerospace component repair
Carbon, 2016
[11] Effect of Plasma Spray Process on TiO2 Coating over Mild Steel Substrate
Applied Mechanics and Materials, 2015
[12] Effect of Plasma Spray Process on TiO2 coating over Mild Steel Substrate.
Applied Mechanics & Materials, 2015
[13] Yipai Jiang
2014
[14] Analysis on the Application of Artificial Intelligence Technology in Modern Physical Education
Information Technology Journal, 2014
[15] Technology in Modern Physical Education
2014
[16] STUDY ON THE EFFECTS OF ATMOSPHERIC PLASMA SPRAY (APS) PROCESS PARAMETERS ON POROSITIES OF FLY ASH DEPOSITED COATINGS
2014
[17] Analysis on the Application of Artificial Intelligence Technology hi Modern Physical Education.
Y Jiang - Information Technology Journal, 2014
[18] Metallurgical & Materials Engineering
Doctoral dissertation, NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA, INDIA, 2013
[19] Least square support vector machine alternative to artificial neural network for prediction of surface roughness and porosity of plasma sprayed copper substrates
International Journal of Current Research (IJCR), 2012
[20] Property prediction of ductile iron: Artificial neural network approach
Journal of Metallurgy and Materials Science, 2012

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.