PermeabilityNets: comparing neural network architectures on a sequence-to-instance task in CFRP manufacturing

  • 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.

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Author:Simon StieberORCiDGND, Niklas SchroterORCiDGND, Ewald Fauster, Alexander SchiendorferORCiDGND, Wolfgang ReifORCiDGND
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Parent Title (English):2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA, 13-16 December 2021
Place of publication:Piscataway, NJ
Editor:M. Arif Wani, Ishwar Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
Type:Conference Proceeding
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2022/12/08
First Page:694
Last Page:697
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht