Experimental validation of an ANN model for random loading fatigue analysis. (September 2019)

• Record Type: Journal Article
• Authors: Ramachandra, S.; Durodola, J.F.; Fellows, N.A.; Gerguri, S.; Thite, A.
• Is Part Of: International journal of fatigue. Volume 126(2019)
• Page Start: 112
• Page End: 121
• Publication Date: 2019-09
• Subjects: Random fatigue -- Frequency domain -- Time domain -- Artificial intelligence -- Artificial neural networks -- SAE; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2019.04.028
• Languages: English
• ISSNs: 0142-1123

Abstract

Highlights: The paper presents the validation of a generalised ANN model for a broad of component conditions and material properties. Validation of theoretical ANN models with experimental data is rare in the literature. The results of the validation show ANN to be a better approach for fatigue analysis than existing frequency domain methods. The results of the study also show that rainflow time domain fatigue analysis is not universally agreeable with experimental results. 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.