An artificial neural network approach to fatigue crack growth

Citation

• Zarrabi, K., Lu, W. W., & Hellier, A. K. (2011). An artificial neural network approach to fatigue crack growth. Advanced Materials Research, 275, 3-6. doi:10.4028/www.scientific.net/AMR.275.3

Digital Object Identifier (doi)

• 10.4028/www.scientific.net/AMR.275.3

Scopus Eid

• 2-s2.0-79960435507

Web Of Science Accession Number

• WOS:000303351300001

Start Page

• 3

End Page

• 6

Volume

• 275

Issue

Place Of Publication

Abstract

• This paper proposes a new three-layer artificial neural network (ANN) to predict the fatigue crack length under constant amplitude mode I cyclic loading. It is shown that the proposed model predicts the crack length with an error of less than 0.05%, and more accurately than the current commonly-used models. �� (2011) Trans Tech Publications, Switzerland.

UOW Authors

•   Hellier, Alan

Publication Date

• 2011

Publisher

• TRANS TECH PUBLICATIONS LTD 

Published In

• Advanced Materials Research  Journal

Citation

• Zarrabi, K., Lu, W. W., & Hellier, A. K. (2011). An artificial neural network approach to fatigue crack growth. Advanced Materials Research, 275, 3-6. doi:10.4028/www.scientific.net/AMR.275.3

Digital Object Identifier (doi)

• 10.4028/www.scientific.net/AMR.275.3

Scopus Eid

• 2-s2.0-79960435507

Web Of Science Accession Number

• WOS:000303351300001

Start Page

• 3

End Page

• 6

Volume

• 275

Issue

Place Of Publication