Erosion modelling using Bayesian regulated artificial neural networks

Danaher, Sean, Datta, Psantu, Hackney, Philip and Waddle, I. (2004) Erosion modelling using Bayesian regulated artificial neural networks. Wear, 256 (9-10). pp. 879-888. ISSN 0043-1648

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Official URL: http://dx.doi.org/10.1016/j.wear.2003.08.006

Abstract

Modelling of the high temperature erosion behaviour of Ni-base alloys using artificial neural networks (ANNs) is presented. Two scenarios have been used: (i) a simple equation-based model and (ii) a comprehensive dataset looking at erosion as a function of particle size, velocity, impact angle and temperature. Common problems associated with ANNs are discussed within the context of erosion modelling. It has been found that the use of multilayer perceptron artificial neural networks for modelling erosion gave unreliable results when trained with traditional algorithms. The more recent Bayesian regularisation algorithm however has proved very successful, yielding both high Pearsonian correlation coefficients (r>0.95) and accuracies averaging better than 90%.

Item Type: Article
Additional Information: A collaborative work between Energy Systems, Materials & Manufacturing research group and Northumbria Communication Research Laboratory, using the neural network for the first time to predict the behaviour of erosion. A good example of research collaboration between the two research groups illustrating true interdisciplinary research in the General Engineering context.
Uncontrolled Keywords: Neural networks (Computer science)
Subjects: H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mathematics and Information Sciences
Depositing User: EPrints Services
Date Deposited: 20 Nov 2008 11:16
Last Modified: 14 Oct 2015 10:39
URI: http://nrl.northumbria.ac.uk/id/eprint/1441

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