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Composites: Mechanics, Computations, Applications: An International Journal
ESCI SJR: 0.193 SNIP: 0.125 CiteScore™: 0.23

ISSN Print: 2152-2057
ISSN Online: 2152-2073

Composites: Mechanics, Computations, Applications: An International Journal

DOI: 10.1615/CompMechComputApplIntJ.v6.i4.50
pages 321-338

PARTICLE SWARM OPTIMIZATION-BASED NEURAL NETWORK FOR PREDICTING FATIGUE STRENGTH IN COMPOSITE LAMINATES OF WIND TURBINE BLADES

Khaled Ziane
Laboratoire d'Ingénierie de la Sécurité Industrielle et du Développement Durable LISIDD, IMSI, Université d'Oran, B.P N°5, Route de l'aéroport 31000 Es-Sénia, Oran, Algérie
Soraya Zebirate
Laboratoire SCAMRE, ENPO; Laboratoire d'Ingénierie de la Sécurité Industrielle et du Développement Durable LISIDD, IMSI, Université d'Oran, B.P N°5, Route de l'aéroport 31000 Es-Sénia, Oran, Algérie
Adel Zaitri
Materials Science and Informatics Laboratory MSIL, University of Djelfa, P. BOX N°3117, Road of Moudjbara 17000, Djelfa, Algeria

ABSTRACT

In this paper, the fatigue strength in multidirectional (MD)/unidirectional (UD) composite laminates of wind turbine blades is predicted by using particle swarm optimization-based artificial neural networks (PSO-ANN). In the PSO-ANN approach used in this work, the objective function was assessed using the mean square error (MSE) computed as the squared difference between the predicted values and the target values for a number of training set samples. Different materials based on different reinforcing fabrics and resins are compared in terms of the maximum tensile fatigue stress. Tension–tension constant amplitude fatigue loads were applied to thermoset materials including glass-fiber/epoxy, polyester and vinyl esters. All materials were treated in closed molds with resin infusion process, which were molded into their final dogbone shape without machining. The results show that the PSO-ANN can provide accurate fatigue strength prediction for different MD/UD composite laminates under different values of fiber orientation.


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