Stochastic non-parametric identification in composite structures using experimental modal data

In the stochastic structural analysis of composite structure, the probabilistic knowledge of the uncertain parameters are essential. Variability of the manufacturing process of the composite structure introduces the uncertainty to the elastic parameters. It is easier to identify the uncertainty of the material parameters using stochastic inverse process. An efficient stochastic inverse identification of the elastic parameters of laminated composite plate using generalized Polynomial Chaos (gPC) theory is presented in this paper. A data set of measured eigen frequencies and mass density are used for stochastic inversion processes. Stochastic identification of the elastic parameters of composite plate transforms into estimation of deterministic coefficients of gPC expansion for the elastic parameters. A robust optimization technique by minimization of the quadratic difference between statistical moments is used to estimate the deterministic coefficients of the gPC expansion. These coefficients can effectively construct the distributions of the uncertain elastic parameters. Evaluation of the deterministic coefficients by higher order statistical moments minimization, can efficiently simulate the randomness of the experimental eigen frequencies.