TY - GEN
T1 - Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm
AU - Rohman, Muhamad Nur
AU - Hidayat, Mas Irfan P.
AU - Purniawan, Agung
N1 - Publisher Copyright:
© 2018 Author(s).
PY - 2018/4/3
Y1 - 2018/4/3
N2 - 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.
AB - 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.
KW - Fatigue life cycle
KW - composite
KW - genetic algorithm
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85045694794&partnerID=8YFLogxK
U2 - 10.1063/1.5030241
DO - 10.1063/1.5030241
M3 - Conference contribution
AN - SCOPUS:85045694794
T3 - AIP Conference Proceedings
BT - Proceedings of the 3rd International Conference on Materials and Metallurgical Engineering and Technology, ICOMMET 2017
A2 - Hidayat, Mas Irfan P.
PB - American Institute of Physics Inc.
T2 - 3rd International Conference on Materials and Metallurgical Engineering and Technology: Advancing Innovation in Materials Science, Technology and Applications for Sustainable Future, ICOMMET 2017
Y2 - 30 October 2017 through 31 October 2017
ER -