Uncertainty quantification in buckling strength of variable stiffness laminated composite plate under thermal loading

In this paper, thermal buckling analysis of variable stiffness composite laminate (VSCL) is carried out. The firstorder shear deformation theory is adopted with eight noded isoparametric element to construct a finite element model for thermal buckling analysis. The effect of the uncertain composite properties that could arise due to complex manufacturing and fabrication process to the buckling temperature is investigated using polynomial neural network (PNN). The contribution of individual properties, such as the material properties, fiber orientations, and the thermal expansion coefficients of VSCL plate are performed for various boundary conditions and lamination sequences. A sensitivity analysis is also carried out and parameters to which buckling temperature has high sensitivity are identified. The accuracy and efficiency of PNN model are compared with Monte Carlo simulation (MCS). Further, PNN is employed to evaluate the reliability parameters of VSCL plate. From the various studies, it is observed that even though the influence of different stochastic input parameters are depend on the boundary conditions, the transverse coefficient of thermal expansion (alpha 22) is found to be most influential parameter, followed by transverse (E22) and longitudinal (E11) Young's modulus, respectively.