Fabrication to Performance: A Comprehensive Multiscale Stochastic Predictive Model for Composites

We propose a comprehensive framework for uncertainty management within the manufacturing process of non-crimp fiber composites (NCF), including the forming, resin injection, curing, and distortion. The challenge of meeting performance requirements while having incomplete knowledge about the fundamental physical processes is addressed with the objective of proposing manufacturing guidelines that are agnostic to these uncertainties. We accomplish this by making the functional dependence of uncertainties in the performance metrics and uncertainties in the various parameters and models explicit. We tackle the issues associated with dimensionality, which has hampered similar efforts in the past, through a basis adaptation procedure that permits the development of functional dependencies, for realistic systems, without any loss of accuracy. These representations are uniquely suited for design optimization as they provide explicit, yet highly accurate, stochastic reduced order models (SROM) that can be analytically differentiated and integrated. We compute several Quantities of Interest (QoI) as functions of random variables and processes of material properties and process conditions. During the forming stage, we consider the mechanical properties of the fibers and the local fiber directions to be random. The deformation of the fabric was computed via a reduced model consisting of an effective shell with embedded upscaling algorithms at the integration points. Forming induces stochastic fluctuations in the relative shearing angles of the fabric, which are mapped, through a stochastic model, into spatial fluctuations of the permeability field. The simulations were carried out using the PAM-COMPOSITESâ„¢