TY - CHAP A1 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Nicosia, Giuseppe ED - Pardalos, Panos ED - Umeton, Renato ED - Giuffrida, Giovanni ED - Sciacca, Vincenzo T1 - A chained neural network model for photovoltaic power forecast T2 - Machine Learning, Optimization, and Data Science: 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings UR - https://doi.org/10.1007/978-3-030-37599-7_47 Y1 - 2019 UR - https://doi.org/10.1007/978-3-030-37599-7_47 SN - 978-3-030-37598-0 SN - 0302-9743 SP - 566 EP - 578 PB - Springer CY - Cham ER - TY - CHAP A1 - Stieber, Simon A1 - Schröter, Niklas A1 - Schiendorfer, Alexander A1 - Hoffmann, Alwin A1 - Reif, Wolfgang ED - Dong, Yuxiao ED - Mladenić, Dunja ED - Saunders, Craig T1 - FlowFrontNet: Improving Carbon Composite Manufacturing with CNNs T2 - Machine Learning and Knowledge Discovery in Databases, Applied Data Science Track, Proceedings, Part IV UR - https://doi.org/10.1007/978-3-030-67667-4_25 KW - process monitoring KW - convolutional neural networks KW - digital twin KW - manufacturing KW - industrial automation KW - resin transfer molding KW - carbon composites Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-67667-4_25 SN - 978-3-030-67667-4 SN - 978-3-030-67666-7 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 411 EP - 426 PB - Springer CY - Cham ER - TY - JOUR A1 - Stieber, Simon A1 - Schröter, Niklas A1 - Fauster, Ewald A1 - Bender, Marcel A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang T1 - Inferring material properties from FRP processes via sim-to-real learning JF - The International Journal of Advanced Manufacturing Technology N2 - Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models. UR - https://doi.org/10.1007/s00170-023-11509-8 KW - Sequence-to-Image Learning KW - Architecture comparison KW - FRP KW - LCM KW - Transfer Learning KW - Industry 4.0 KW - Digital Twin Y1 - 2022 UR - https://doi.org/10.1007/s00170-023-11509-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32402 SN - 1433-3015 VL - 128 IS - 3-4 SP - 1517 EP - 1533 PB - Springer CY - London ER - TY - CHAP A1 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Amini, Massih-Reza ED - Canu, Stéphane ED - Fischer, Asja ED - Guns, Tias ED - Kralj Novak, Petra ED - Tsoumakas, Grigorios T1 - A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks T2 - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I UR - https://doi.org/10.1007/978-3-031-26387-3_28 KW - Graph Machine Learning KW - Recommendation KW - Computer-aided Design KW - AI-aided Design Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-26387-3_28 SN - 978-3-031-26387-3 SN - 978-3-031-26386-6 SP - 457 EP - 473 PB - Springer CY - Cham ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Lassner, Christoph A1 - Anders, Gerrit A1 - Reif, Wolfgang A1 - Lienhart, Rainer ED - Cardoso, João M. P. T1 - Active Learning for Abstract Models of Collectives T2 - ARCS 2015 - 28th International Conference on Architecture of Computing Systems, Workshop Proceedings Y1 - 2015 UR - https://www.vde-verlag.de/proceedings-de/563657010.html SN - 978-3-8007-3657-7 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/abstract/document/7107102 PB - VDE Verlag CY - Berlin ER -