Hybrid twin of RTM process at the scarce data limit

Abstract

To ensure correct filling in the resin transfer molding (RTM) process, adequate numerical models have to be developed in order to correctly capture its physics, so that this model can be considered for process optimization. However, the complexity of the phenomenon often makes it impossible for numerical models to accurately predict its behavior, limiting its usage. To overcome this limitation, numerical models are enriched with measured data to ensure their correct predictability. Nevertheless, the data used is often limited due to practical constraints, such as a limited number of sensors or the high costs of experimental campaigns. In this context, the present paper demonstrates the implementation of a numerical model enriched with data, called Hybrid Twin applied to the RTM process when few sensors are considered in the mold to be injected. The performances of the developed hybrid twin are tested in a virtual test for the injection of a 2D mold, where the hybrid twin constructed using a simplified numerical model allows to accurately predict a complex model's resin flow-front over its entire time history.

Integrated computational modeling of large format additive manufacturing: Developing a digital twin for material extrusion with carbon fiber-reinforced acrylonitrile butadiene styrene

“…This HT was first applied in the field of composite simulation in liquid composites molding processes by the research group led by F. Chinesta at ENSAM, in Paris. 15 In this case, a physics-based model (VT) is combined with data to overcome the limitations of its virtual and digital counterparts. The main idea involves fitting a VT to real-time sensor-measured data.…”