Adaptive Neural Network Modelling in Fatigue life Prediction under Load History effects

M. Abdul Razzaq; Kamal A. Ariffin; Ahmed El Shafie; Shahrum Abdullah; Z. Sajuri; N.A. Akeel

doi:10.4028/www.scientific.net/AMR.284-286.1266

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 284-286Adaptive Neural Network Modelling in Fatigue life...

Adaptive Neural Network Modelling in Fatigue life Prediction under Load History effects

Abstract:

Artificial intelligence (AI) techniques and in particular, adaptive neural networks (ANN) have been commonly used in order to Fatigue life prediction. The aim of this paper is to consider a new crack propagation principle based on simulating experimental tests on three point-bend (TPB) specimens, which allow predicting the fatigue life and fatigue crack growth rate (FCGR). An important part of this paper is estimation of FCG rate related to different load histories. The effects of different load histories on the crack growth life are obtained in different representative simulation and experiments.

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Periodical:

Advanced Materials Research (Volumes 284-286)

Pages:

1266-1270

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.284-286.1266

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Online since:

July 2011

Authors:

M. Abdul Razzaq, Kamal A. Ariffin, Ahmed El Shafie, Shahrum Abdullah, Z. Sajuri, N.A. Akeel

Keywords:

Fatigue, Feed Forward (ANN), Load Sequence, Variable Amplitude Loading

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