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Comparison of Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) Modeling Approaches in Predicting the Deposition Efficiency of Plasma Sprayed Alumina Coatings on AZ31B Magnesium Alloy

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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 performance to machinery and industrial equipments. Plasma spray parameters such as power, stand-off distance and powder feed rate have significant influence on coating characteristics like deposition efficiency. Two methods, response surface methodology and artificial neural network were used to predict the deposition efficiency of plasma sprayed alumina coatings on AZ31B magnesium alloy. The experiments were conducted based on three factors, five-level, and central composite rotatable design with full replications technique, and mathematical model was developed. A linear regression relationship was established between porosity and deposition efficiency of the alumina coatings. The results obtained through response surface methodology were compared with those through artificial neural networks.
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Document Type: Research Article

Publication date: March 1, 2017

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  • Journal of Advanced Microscopy Research (JAMR) provides a forum for rapid dissemination of important developments in high-resolution microscopy techniques to image, characterize and analyze man-made and natural samples; to study physicochemical phenomena such as abrasion, adhesion, corrosion and friction; to perform micro and nanofabrication, lithography, patterning, micro and nanomanipulation; theory and modeling, as well as their applications in all areas of science, engineering, and medicine.
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