An Artificial Neural Network Approach to Fatigue Crack Growth

K. Zarrabi; W.W. Lu; A.K. Hellier

doi:10.4028/www.scientific.net/AMR.275.3

Paper Titles

An Artificial Neural Network Approach to Fatigue Crack Growth
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Experimental Investigation of PMMA Fracture under Mixed-Modes I, II and III Loading
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Modelling Fatigue Crack Growth near an Open Hole under Spectrum Loading Using a New Method for Elastic-Plastic Stress Calculation
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Effects of Crack Size and Crack Face Interference on Shear Fatigue Crack Growth in a Bearing Steel
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A Model for the Cyclic Behaviour of Steel under Earthquake Conditions
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Fatigue Strength of Ultrafine Grained Copper Treated by Post-ECAP Mild Annealing
p.23

Experimental Study on Theory of Critical Distance Applied to the Prediction of Fatigue from Notches
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Effects of Thermal Exposure on Microstructure and Mechanical Properties of Ni Based Superalloy GTD111
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 275An Artificial Neural Network Approach to Fatigue...

An Artificial Neural Network Approach to Fatigue Crack Growth

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.

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Periodical:

Advanced Materials Research (Volume 275)

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3-6

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.275.3

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Online since:

July 2011

Authors:

K. Zarrabi, W.W. Lu, A.K. Hellier

Keywords:

Artificial Neural Network (ANN), Constant Amplitude Mode I Cyclic Loading, Fatigue Crack Growth (FCG), Fatigue Life Prediction, Linear Elastic Fracture Mechanics (LEFM)

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