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Carbon fiber reinforced polymers (CFRP) offer highly desirable properties such as weight-specific strength and stiffness. Liquid composite moulding (LCM) processes are prominent, economically efficient, out-of-autoclave manufacturing techniques and, in particular, resin transfer moulding (RTM), allows for a high level of automation. There, fibrous preforms are impregnated by a viscous polymer matrix in a closed mould. Impregnation quality is of crucial importance for the final part quality and is dominated by preform permeability. We propose to learn a map of permeability deviations based on a sequence of camera images acquired in flow experiments. Several ML models are investigated for this task, among which ConvLSTM networks achieve an accuracy of up to 96.56%, showing better performance than the Transformer or pure CNNs. Finally, we demonstrate that models, trained purely on simulated data, achieve qualitatively good results on real data.

Components made from carbon fiber reinforced plastics (CFRP) offer attractive stability properties for the automotive or aerospace industry despite their light weight. To automate the CFRP production, resin transfer molding (RTM) based on thermoset plastics is commonly applied. However, this manufacturing process has its shortcomings in quality and costs. The project CosiMo aims for a highly automated and costattractive manufacturing process using cheaper thermoplastic materials. In a thermoplastic RTM (T-RTM) process, the polymerization of-caprolactam to polyamide 6 is investigated using an "intelligent tooling". Multiple sensor types integrated into the mold allow for tracking of several process-relevant variables, such as material flow and state of polymerization. In addition to the evaluation of the T-RTM process, a digital twin helps to visualize progress and to make predictions about possible problems and countermeasures based on machine learning. In this paper, the combination of software and hardware developments is described which will help to validate an optimal process setup for an industrial CFRP demonstrator.

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.

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