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

TL;DR

This paper introduces a probabilistic virtual process chain that models manufacturing-induced uncertainties in Sheet Molding Compounds, enabling accurate prediction of mechanical performance variations through a digital twin approach.

Contribution

It develops a comprehensive digital twin framework that integrates process simulation, microstructure modeling, and probabilistic analysis for SMC manufacturing.

Findings

Predicted mechanical performance aligns well with experimental results.

Quantified the impact of microstructure variability on performance.

Provided a probabilistic assessment of process-induced uncertainties.

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.