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Open AccessArticle

Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

Institute of Machine Tools and Production Technology, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany
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J. Manuf. Mater. Process. 2020, 4(3), 92; https://doi.org/10.3390/jmmp4030092
Received: 29 July 2020 / Revised: 4 September 2020 / Accepted: 8 September 2020 / Published: 11 September 2020
(This article belongs to the Special Issue Cyber Physical Production Systems)
The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support. View Full-Text
Keywords: digital twin; surrogate modeling; machine learning; cyber physical production systems; thermoplastic composites; overmolding; interface bond strength digital twin; surrogate modeling; machine learning; cyber physical production systems; thermoplastic composites; overmolding; interface bond strength
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Hürkamp, A.; Gellrich, S.; Ossowski, T.; Beuscher, J.; Thiede, S.; Herrmann, C.; Dröder, K. Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites. J. Manuf. Mater. Process. 2020, 4, 92.

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