The orientation of fibers in the layers is an important factor that must be obtained in order to predict how well the finished composite product will perform under real-world working conditions. In this research, a five-layer glass-epoxy composite truncated cone structure under buckling load was considered. The simulation of the structure was done utilizing finite element method and was confirmed comparing with the published experimental results. Then the effect of different orientation of fibers on the buckling load was considered. For this, a computer programing was developed to compute the buckling load for different orientations of fibers in each layer. These orientations were produced randomly with the delicacy of 15 degrees. Finally, neural network and genetic algorithm methods were utilized to obtain the optimum orientations of fibers in each layer using the training data obtained from finite element simulation. There are many parameters such as the number of hidden layers, the number of neurons in each hidden layer, the training algorithm, the activation function and so on which must be specified properly in development of a neural network model. The number of hidden layers and number of neurons in each layer was obtained by try and error method. In this study, multilayer back-propagation (BP) neural network with the Levenberg-Marquardt training algorithm (trainlm) was used. Finally, the results showed that the truncated cone with optimum layers withstand considerably more buckling load.
A polarization-independent and ultra-broadband terahertz metamaterial absorber studied based on circular-truncated cone structure
