Modeling of fiber pull-out in continuous fiber reinforced ceramic composites using finite element method and artificial neural networks

A Machine Learning Technique to Predict Biaxial Failure Envelope of Unidirectional Composite Lamina

“…Artificial Neural Networks (ANNs) have been employed to determine mechanical properties and failure analysis of composite materials [14][15][16][17][18][19][20][21]. NNs offer several advantages over the traditional finite element approach in structural design, such as computational efficiency for large number of inputs, ease of performing parametric study on the effect of inputs on the outcome, obtaining nonlinear mechanical behavior with relative effortlessness etc.…”

Incorporating feedforward neural network within finite element analysis for L-bending springback prediction

“…Therefore, there were many attempts by researchers to combine the FE and ANN in order to take advantage of the capabilities of both approaches, which would reduce laboratory experimental cost and increase prediction accuracy (Apalak, Ekici, Yildirim, & Apalak, 2014;Bachi, Abdulrazzaq, & He, 2014;Bheemreddy, Chandrashekhara, Dharani, & Hilmas, 2013;Hasançebi & Dumlupınar, 2013;Khalaj Khalajestani & Bahaari;Selvakumar, Arulshri, Padmanaban, & Sasikumar, 2013;Tian, Luo, Wang, & Wu, 2014). From the beginning of its usage until recently, most studies related to the combination of ANN and FE methods in solving engineering problems have used inverse analysis aimed at optimising the FE simulation parameters.…”

Predicting the limit pressure capacity of pipe elbows containing single defects

“…For this purpose, parametric FEA in combination with ANN, as a powerful method [28][29][30][31] was implemented. An analytical approach by modifying DNV 2015 formula was also developed and discussed.…”