Erosion modelling using Bayesian regulated artificial neural networks

Sean Danaher, Psantu Datta, Philip Hackney, I. Waddle

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

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%.
Original languageEnglish
Pages (from-to)879-888
JournalWear
Volume256
Issue number9-10
DOIs
Publication statusPublished - Apr 2004

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