Journal Article


Experimental validation of an ANN model for random loading fatigue analysis

Abstract

The use of artificial intelligence especially based on artificial neural networks (ANN) is now prevalent in many fields of data analysis and interpretation. There have been a number of papers published in the literature on the use of ANN for fatigue characterisation. Most of these have however been developed for rather focussed application with limited capability for fatigue life prediction for a broad scope of material and loading conditions. The authors recently presented a uniquely generalised ANN model that is capable of making fatigue life prediction for a broad range of material fatigue properties and loading spectral forms. The model was developed using simulated data albeit subject to conceivable constraints between possible materials properties and load forms. This paper presents a validation of the ANN model using a Society of Automotive Engineers (SAE) random fatigue loading experimental test data. The capabilities and potentials of the model are demonstrated by comparison with the SAE random load fatigue test results and with results obtained from other predictive methods. The performance of the ANN is highly encouraging as a general tool for random loading fatigue analysis.

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Authors

Ramachandra, Shashidhar
Durodola, John F.
Fellows, Neil A.
Gerguri, Shpend
Thite, Anand

Oxford Brookes departments

Faculty of Technology, Design and Environment\School of Engineering, Computing and Mathematics

Dates

Year of publication: 2019
Date of RADAR deposit: 2019-05-30


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


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This RADAR resource is the Accepted Manuscript of Experimental validation of an ANN model for random loading fatigue analysis

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