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  4. Predicting the fiber orientation of injection molded components and the geometry influence with neural networks
 
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2024
Journal Article
Title

Predicting the fiber orientation of injection molded components and the geometry influence with neural networks

Abstract
The injection molding simulation of short fiber reinforced plastics (SFRP) is time consuming. However, until now it is necessary for predicting the local fiber orientation, to optimize the molding process and to predict the mechanical behavior of the material. This research presents the capabilities of artificial neural networks (NN) in predicting fiber orientation tensor (FOT) during injection molding processes, with a focus on enhancing computational efficiency compared to traditional simulation methods. Three NN architectures are compared based on simulated injection molded plates, with the goal of predicting the effect of the plate geometry on the local fiber orientation. Results indicate that NN outperform the baseline assumption of aligned fibers and demonstrate significant potential for accurate FOT prediction. The computational efficiency of NN, especially during the prediction phase, showcases a reduction in processing time by a factor of 104 compared to traditional simulation methods. This research lays a foundation for further exploration into the feasibility of NN in partly replacing time-consuming simulations for practical applications in injection molding processes.
Author(s)
Herrmann, Till
Fraunhofer-Institut für Werkstoffmechanik IWM  
Niedziela, Dariusz  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Salimova, Diyora
Univ. Freiburg/Brsg., Mathematische Fakultät, Institut für Angewandte Mathematik  
Schweiger, Timo  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Journal
Journal of composite materials : JCM  
Open Access
DOI
10.1177/00219983241248216
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Keyword(s)
  • fiber orientation distribution

  • short fiber reinforced plastics

  • neural networks

  • molding simulations

  • fiber orientation prediction

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