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.

为确保在树脂传递模塑 (RTM) 工艺中正确填充，必须开发足够的数值模型以正确捕获其物理特性，以便可以考虑将该模型用于工艺优化。然而，现象的复杂性往往使数值模型无法准确预测其行为，限制了其使用。为了克服这一局限性，数值模型用测量数据进行了丰富，以确保其正确的可预测性。然而，由于实际限制，例如传感器数量有限或实验活动的高成本，所使用的数据通常是有限的。在这种情况下，本文演示了一个富含数据的数值模型的实施，称为混合双胞胎应用于 RTM 过程，当在要注入的模具中考虑很少的传感器时。