- Title
- Artificial Neural Network application for predicting in-flight particle characteristics of an atmospheric plasma spray process
- Creator
- Choudhury, Tanveer; Hosseinzadeh, Nasser; Berndt, Christopher
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161730
- Identifier
- vital:12553
- Identifier
-
https://doi.org/10.1016/j.surfcoat.2011.04.099
- Identifier
- ISBN:0257-8972
- Abstract
- Thermal spray consists of a group of coating processes that are used to apply metal or non-metallic coatings to protect a functional surface or to improve its performance. There are some 40 processing parameters that define the overall coating quality and these must be selected in an optimized fashion to manufacture a coating that exhibits desirable properties. The proper combination of processing variables is critical since these influence the cost as well as the coating characteristics.Because of this high number of processing parameters, a major challenge is to have full control over the system and to understand parameter interdependencies, correlations and their individual effects on the in-flight particle characteristics, which have significant influence on the in service coating properties. This paper proposes an approach, based on the Artificial Neural Network (ANN) method, to play this role and illustrates the model's design, network optimization procedures, the database handling and expansion steps, and analysis of the predicted values, with respect to the experimental ones, in order to evaluate the network's performance. © 2011 Elsevier B.V.
- Publisher
- Elsevier Ltd.
- Relation
- Surface and Coatings Technology Vol. 205, no. 21-22 (2011), p. 4886-4895
- Rights
- Copyright © 2011 Elsevier B.V.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0204 Condensed Matter Physics; 0306 Physical Chemistry (Incl. Structural); 0912 Materials Engineering; Artificial Neural Network; Atmospheric plasma spray; In-flight particle characteristics; Intelligent multivariable control; Kernel regression; Process control
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