Abstract: In this paper, an alternative methodology to obtain the strength ratio for the laminated composite material is pre-sented. Traditionally, classical lamination theory and related fail-ure criteria are used to calculate the numerical value of strength ratio of laminated composite material under in-plane and out-of-plane loading from a knowledge of the material properties and its layup. In this study, to calculate the strength ratio, an alternative approach is proposed by using an artificial neural network, in which the genetic algorithm is proposed to optimize the search process at four different levels: the architecture, parameters, connections of the neural network, and active functions. The results of the present method are compared to those obtained via classical lamination theory and failure criteria. The results show that an artificial neural network is a feasible method to calculate the strength ratio concerning in-plane loading instead of classical lamination and associated failure theory.

Huiyao Zhang and Atsushi Yokoyama, “Predicting Strength Ratio of Laminated Composite Material with Evolutionary Artificial Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 12(6), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120602

@article{Zhang2021,
title = {Predicting Strength Ratio of Laminated Composite Material with Evolutionary Artificial Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120602},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120602},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {6},
author = {Huiyao Zhang and Atsushi Yokoyama}
}