Abstract
In order to optimize the processing of composite materials, the importance of digital methods in materials science is steadily increasing. In this study, two approaches were used for composite materials processing. First, for the winding of H2 storage vessels using towpregs, Bayesian optimization (BO) was used as a tool to find optimal process conditions within specified parameter limits. The applied algorithm can efficiently maximize selected target values within existing parameter limits. In this case, the goal was to achieve a maximum towpreg width of a winding standard of ¼” (6.35 mm) with a fibre volume content between 55–60 vol.%. Although this maximum couldn’t be achieved within the specified parameter limits, the BO resulted in a steady reduction of the standard deviation and thus a significant increase in process quality. In the second part of this study, an in-situ monitoring tool for a fibre spreading process was developed. Fibre spreading of low-cost but mechanically weak Heavy Tows has the potential to be used in the future production of H2 storage vessels, especially for the highly cost-effective automotive market. To gain deeper insights into the spreading process itself, a sufficient in-line monitoring tool is needed to observe and analyse the spreading behaviour of the given fibres. Using a camera setup and a developed Python tool, a method has been developed to observe the spreading process of the fibres in depth. This opens the possibility for further in-depth parameter studies on the spreading behaviour and the use of Heavy Tows in automotive applications.
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Funding
The Bayesian optimization approach was funded in the project “CryoFuselage” in the BayLu25 program under guidance of German Aerospace Center (DLR). Grants were given by the Regierung of Oberbayern with the Bavarian Ministry of Economic affairs, Regional Development and Energy (StmWi) with grant number LABAY108A.
The in-situ monitoring tool development for fibre spreading was funded in the project “InLineCon” in the LuFo VI-1 program under the guidance of German Aerospace Center (DLR). Grants were given by the Federal Ministry for Economic Affairs and Climate Action (BMWK) with grant number 20E1903B.
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Schönl, F., Hübner, F., Luik, M., Thomas, J., de Albuquerque, R., Ruckdäschel, H. (2023). Digital Approaches for Optimization of Composite Processing: Bayesian Optimization for Impregnation and Fibre Spreading In-Situ Monitoring. In: Rieser, J., Endress, F., Horoschenkoff, A., Höfer, P., Dickhut, T., Zimmermann, M. (eds) Proceedings of the Munich Symposium on Lightweight Design 2022. MSLD MSLD MSLD 2022 2022 2022. Springer Vieweg, Cham. https://doi.org/10.1007/978-3-031-33758-1_5
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