Artificial neural networks in spectrum fatigue life prediction of composite materials

Vassilopoulos, Anastasios P.; Georgopoulos, Efstratios F.; Dionysopoulos, Vasileios

doi:10.1016/j.ijfatigue.2006.03.004

Cited by 139 publications

(70 citation statements)

References 15 publications

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“…The training process adjusts the weight of each neuron to an appropriate value. There are many available training algorithms, but the most popular one is the error back-propagation algorithm (Hagan et al, 1996;Sterjovski et al, 2005;Yuan et al, 2002;Fuh et al, 2004;Vassilopoulos et al, 2007;Fonseca et al, 2003) and it was used in this study. There is no strict rule for design of the ANN structure.…”

Section: Network Training and Testingmentioning

confidence: 99%

“…They found that the estimation could be more accurate by using different ANN's structures for different cost range. Vassilopoulos et al used ANN to generalize the experimental data and approximate the relationship between design parameters (orientation angle of the fibres, stress ratio, the maximum applied stress and the amplitude of applied stress) and the fatigue life of multidirectional composite laminates (Vassilopoulos et al, 2007). In their study, only 50% of experimental data was enough to model the fatigue life characteristics of the material.…”

Section: Introductionmentioning

confidence: 99%

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An integrated FEM and ANN methodology for metal-formed product design

Chan

2008

Engineering Applications of Artificial Intelligence

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Section: Network Training and Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrated FEM and ANN methodology for metal-formed product design

Chan

2008

Engineering Applications of Artificial Intelligence

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“…Vassilopoulos et al [12] demonstrated that ANN is a good tool for modeling the fatigue life of multidirectional glass fiber reinforced plastics (GFRP) composite laminates. Tensiontension, compression-compression and tension-compression loading patterns were investigated and modeling accuracy of the proposed ANN model was validated.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect

2010

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Artificial Neural Networks (ANN) have been successfully used in predicting the fatigue behavior of fiber-reinforced composite materials. In most cases, the predictions were obtained for the same material used in training subjected to different loading conditions. The method would be of greater value if one could predict the failure of materials other than those used for training the network. In a recent paper, ANN trained using the experimental fatigue data obtained for composites subjected to a constant stress ratio R ¼ s min =s max ð Þ was successfully used to predict the cyclic behavior of a composite made of a different material. In this work, this method is extended to include the stress ratio effect. The results show that ANN can provide accurate fatigue life prediction for different materials under different values of the stress ratio. These results can allow for the development of a materials smart database that can be used for various engineering applications.

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“…The major advantages of AMCs compared to unreinforced materials are as follows: greater strength, improved stiffness, reduced density, good corrosion resistance, improved high temperature properties, controlled thermal expansion coefficient, thermal/heat management, enhanced and tailored electrical performance, improved wear resistance and improved damping capabilities [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%