Abstract

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Materials and Metallurgical Engineering and Technology, ICOMMET 2017
Subtitle of host publicationAdvancing Innovation in Materials Science, Technology and Applications for Sustainable Future
EditorsMas Irfan P. Hidayat
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735416406
DOIs
Publication statusPublished - 3 Apr 2018
Event3rd International Conference on Materials and Metallurgical Engineering and Technology: Advancing Innovation in Materials Science, Technology and Applications for Sustainable Future, ICOMMET 2017 - Surabaya, Indonesia
Duration: 30 Oct 201731 Oct 2017

Publication series

NameAIP Conference Proceedings
Volume1945
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Conference on Materials and Metallurgical Engineering and Technology: Advancing Innovation in Materials Science, Technology and Applications for Sustainable Future, ICOMMET 2017
Country/TerritoryIndonesia
CitySurabaya
Period30/10/1731/10/17

Keywords

  • Fatigue life cycle
  • composite
  • genetic algorithm
  • neural networks

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