Uncertainty analysis of curing-induced dimensional variability of composite structures utilizing physics-guided Gaussian process meta-modeling

Abstract

Composite manufacturing process involves a suite of complex and inter-related procedures that span across multiple physics domains and scales. A variety of uncertainties that inevitably exist may degrade the quality of composites produced. Quantitatively characterizing the effect of uncertainties in such process hence becomes critically important. In this study, we establish a systematic framework for uncertainty analysis of composite manufacturing process. A finite element processing model has been developed to characterize the multi-physics and multi-scale nature of composite manufacturing process, based upon which a Gaussian process (GP) meta-model has been synthesized for efficient uncertainty quantification. By leveraging the well-trained GP meta-model, an importance ranking analysis of uncertainties was then carried out using a series of metrics, i.e., Pearson coefficient, Sobol index and Shapley Additive exPlanations (SHAP). Since the physics-based processing model involves several simplifying assumptions and empirical relations, modeling errors were also considered in the uncertainty analysis. Comprehensive case studies, which aim at elucidating the causes of composite spring-in angles, were conducted to examine the feasibility and validity of this new framework. Specifically, the mean error of GP predictions is smaller than 2%, and the uncertainty importance ranking can be obtained with high confidence in both cases with and without modeling errors.