The compaction behaviour of technical textiles such as non-crimp fabrics (NCF) is of much interest to build high quality parts in liquid composite moulding processes (LCM). In this paper, the compaction response of a glass fibre NCF was investigated in two different ways: (1) characterisation tests via a universal testing machine and (2) height measurements via a line laser measurement unit during the infusion process. Results from both measurement systems are compared and it is found that the results of characterisation tests via testing machine can be used only to a certain extent. The pressure-thickness correlation during the infusion process cannot be described by the results of the testing machine, while the compaction results before and after the infusion process are in good agreement for both the methods. With the data of the line laser measurement, a model for the pressure-thickness correlation is derived, which can be used in future simulations. The infusion process was carried out using different scenarios at the vent, on the one hand a semi-permeable membrane and on the other hand an omega profile at the venting port. The results obtained using these two different venting scenarios were compared and it was found that using a semi-permeable membrane as an outlet can lead to thicker parts (up to 10 %).
The resin transfer molding (RTM) process shows considerable advantages in composite manufacturing. Nevertheless, the part quality manufactured by RTM is sensitive to material and process variations during the preform impregnation. To improve the process robustness and achieve better process control, a methodology for resin flow monitoring based on a combination of a sensing system and a neural network model is proposed, which can be easily implemented into a generic RTM process. Using pressure data provided by a limited number of sensors distributed over the mold surface, the proposed method allows the prediction of flow-front patterns at any impregnation time. The dataset for training is generated by physical-based simulations. Considering the permeability changes caused by uncertainty conditions, the permeability tensor is modeled with random variations. The network parameters are obtained by trial-and-error. Furthermore, the sensor distribution scheme and the dataset size are identified as the sensitive factors of the model. Finally, the predicted results are verified by numerical solutions. This method can be used to avoid the formation of voids and improve the final part quality.
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