Tribo-Performance Analysis of Fly Ash–Aluminum Coatings Using Experimental Design and ANN

Autor:	Debadutta Mishra, Amar Patnaik, Suvendu Pr. Sahu, K P Sreekumar, Alok Satapathy
Rok vydání:	2010
Předmět:	Engineering drawing Materials science Artificial neural network business.industry Mechanical Engineering Surfaces and Interfaces Tribology Surfaces Coatings and Films Power (physics) Taguchi methods Mechanics of Materials Plasma torch Fly ash Metal powder Process engineering business Test data
Zdroj:	Tribology Transactions. 53:533-542
ISSN:	1547-397X 1040-2004
DOI:	10.1080/10402000903491317
Popis:	This article proposes the application of an artificial neural network (ANN) to a Taguchi orthogonal experiment to develop a robust and efficient method of analyzing and predicting the solid particle erosion wear response of a new class of metal–ceramic coatings. An ANN model based on data obtained from experiments performs self-learning by updating weightings and repeated learning epochs. In this work, plasma-sprayed coatings of fly ash premixed with aluminum powder in different weight proportions are deposited on aluminum 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 approach is then implemented taking into account training and test procedure to predict the ...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ec3cd3c47b345782f6c28f09c2f96f1f https://doi.org/10.1080/10402000903491317 Zobrazit plný text záznamu