Publication

Abstract : In structural applications, laminated composites are typically the best choice for providing a high strength-to-weight ratio. The composite laminates, on the other hand, are susceptible to the first ply failure (FPF), which can result in delamination, matrix cracking, and fiber breaking. As a result, it is critical to map the FPF of laminated composites against the uncertainty in material properties. In this paper, we presented a framework based on coupled statistical modeling and failure criteria to perform sensitivity analysis corresponding to the FPF of laminated composites. The practically relevant randomness in material properties (elastic modulus, shear modulus, Poisson’s ratio, and mass density) is enforced by utilizing the Monte Carlo random sampling method. The FPF of a laminated composite subjected to random material properties is evaluated using five failure criteria: maximum strain, maximum stress, Tsai-Hill, Tsai-Wu, and Hoffman. Such a random sampling-based dataset is used to train and validate the random sampling high dimensional model representation (RS-HDMR) metamodel and Gaussian process regression (GPR) model. To ensure sound generalization capabilities, the models are rigorously cross-validated. With sufficient confidence in the constructed models, the models are further utilized to perform the variance-based sensitivity analysis. It is worth mentioning that observations from both models in terms of parameters with the highest sensitivity (for the first-order polynomial function) are comparable. The RS-HDMR metamodel is further used to perform the second-order polynomial function-based sensitivity analysis, wherein the sensitivity index for the most sensitive parameter is observed to be very low when compared with the observations of first-order polynomial function-based sensitivity analysis. The numerically quantifiable outcomes of the present study will serve its purpose in the bottom-up design of the laminated composites.