Inferring material properties from CFRP processes via Sim-to-Real learning

Abstract

Carbon fiber reinforced polymers provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold. Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments. The three properties are fiber volume content and permeability in X and Y direction. Finally, we show how simulation-to-real transfer learning can improve a digital twin in CFRP manufacturing, compared to simulation-only models and models based on sparse real data. The best model, trained on the most realistic simulation data outperforms the same model trained on less sophisticated simulation data by 4 percent points and 0.34 points in intersection over union, more than tripling this metric.

RTMsim - A Julia module for filling simulations in Resin Transfer Moulding with the Finite Area Method

“…Furthermore, parts of the RTMsim package can be integrated in separate codes. For example, a customized lightweight version of the solver was already used as training environment for Reinforcement learning (RL) algorithms to keep the flow front straight in a rectangular flow with reinforcement patches and three inlet ports (Stieber et al, 2023).…”