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Prediction of fatigue life of fiber glass reinforced composite (FGRC) using artificial neural Network
Joint Authors
Muhammad, Abd Allah Jarrah
Abd al-Razzaq, Nazhat Said
Abd al-Latif, Mustafa Sami
Source
Engineering and Technology Journal
Issue
Vol. 35, Issue 4A (30 Apr. 2017), pp.327-339, 13 p.
Publisher
Publication Date
2017-04-30
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
The present work studies the mechanical properties of composite materials, experimentally and analytically, that are fabricated by stacking 4-layers of fiberglass reinforced with polyester resin.
This plies are tested under dynamic load (fatigue test) in fully reversible tension-compression (R=-1) to estimate the fatigue life of the composite where fatigue performance of fiberglass reinforced composed is an increasingly important consideration especially when designing wind turbine blades.
In order to predict fatigue life (Number of cycles to failure), conventional analytical techniques are used in the present work.
In addition, Artificial Neural Network (ANN) is a reliable and accurate technique that is used for predicting fatigue life.
The used networks are; Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial Bases Function Neural Network (RBFNN).
Based on the comparison of the results, it is found that the ANN techniques are better than conventional methods for prediction.
The results shows that (RBNN2), where stress load and angle of orientation are input to the network and number of cycles to failure as output, is an efficient tool for prediction and optimization the fatigue life of fiberglass reinforced composite.
American Psychological Association (APA)
Abd al-Latif, Mustafa Sami& Abd al-Razzaq, Nazhat Said& Muhammad, Abd Allah Jarrah. 2017. Prediction of fatigue life of fiber glass reinforced composite (FGRC) using artificial neural Network. Engineering and Technology Journal،Vol. 35, no. 4A, pp.327-339.
https://search.emarefa.net/detail/BIM-795453
Modern Language Association (MLA)
Abd al-Latif, Mustafa Sami…[et al.]. Prediction of fatigue life of fiber glass reinforced composite (FGRC) using artificial neural Network. Engineering and Technology Journal Vol. 35, no. 4A (2017), pp.327-339.
https://search.emarefa.net/detail/BIM-795453
American Medical Association (AMA)
Abd al-Latif, Mustafa Sami& Abd al-Razzaq, Nazhat Said& Muhammad, Abd Allah Jarrah. Prediction of fatigue life of fiber glass reinforced composite (FGRC) using artificial neural Network. Engineering and Technology Journal. 2017. Vol. 35, no. 4A, pp.327-339.
https://search.emarefa.net/detail/BIM-795453
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 337-339
Record ID
BIM-795453