Neural network based buckling strength prediction of laminated composite plate with central cutout

Laminated composite plates are greatly used in many applications where high specific strength and stiffness are mandatory. These structures may have holes in order to accommodate windows and doors if it is used for air craft structures or to provide cables and inspection system if it is used in the applications like power transmission systems and automobiles. The laminated composite plates with holes shall be analyzed using finite element analysis. It is necessary to optimize the parameters like thickness, fiber orientation, material and the stacking sequence to obtain the desired characteristics for these structures. But using finite element analysis makes the process more tedious job. With this in mind it is proposed here to construct the artificial neural network to predict the buckling behavior of the composite plate.