A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds

Abstract

The manufacturing process of Sheet Molding Compound (SMC) influences the properties of a component in a non-deterministic fashion. To predict this influence on the mechanical performance, we develop a virtual process chain acting as a digital twin for SMC specimens from compounding to failure. More specifically, we inform a structural simulation with individual fields for orientation and volume fraction computed from a direct bundle simulation of the manufacturing process. The structural simulation employs an interpolated direct deep material network to upscale a tailored SMC damage model. We evaluate hundreds of virtual specimens and conduct a probabilistic analysis of the mechanical performance. We estimate the contribution to uncertainty originating from the process-induced inherent random microstructure and from varying initial SMC stack configurations. Our predicted results are in good agreement with experimental tensile tests and thermogravimetric analysis.

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• A numerical method for computing the overall response of nonlinear composites with complex microstructure
• Stochastic Constitutive Model of Isotropic Thin Fiber Networks Based on Stochastic Volume Elements
• Non-isothermal direct bundle simulation of SMC compression molding with a non-Newtonian compressible matrix