@inproceedings{StieberSchroeterFausteretal.2021, author = {Stieber, Simon and Schr{\"o}ter, Niklas and Fauster, Ewald and Schiendorfer, Alexander and Reif, Wolfgang}, title = {PermeabilityNets: Comparing Neural Network Architectures on a Sequence-to-Instance Task in CFRP Manufacturing}, booktitle = {Proceedings: 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021)}, editor = {Wani, M. Arif and Sethi, Ishwar and Shi, Weisong and Qu, Guangzhi and Raicu, Daniela Stan and Jin, Ruoming}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-4337-1}, doi = {https://doi.org/10.1109/ICMLA52953.2021.00116}, pages = {694 -- 697}, year = {2021}, language = {en} } @inproceedings{WilfertPaprottaKosaketal.2021, author = {Wilfert, Jonas and Paprotta, Niklas and Kosak, Oliver and Stieber, Simon and Schiendorfer, Alexander and Reif, Wolfgang}, title = {A Real-Word Realization of the AntNet Routing Algorithm with ActivityBots}, booktitle = {Proceedings, 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion}, editor = {El-Araby, Esam and Kalogeraki, Vana and Pianini, Danilo and Lassabe, Fr{\´e}d{\´e}ric and Porter, Barry and Gharemani, Sona and Nunes, Ingrid and Bakhouya, Mohamed and Tomforde, Sven}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-4393-7}, doi = {https://doi.org/10.1109/ACSOS-C52956.2021.00072}, pages = {289 -- 290}, year = {2021}, language = {en} } @inproceedings{StieberHoffmannSchiendorferetal.2020, author = {Stieber, Simon and Hoffmann, Alwin and Schiendorfer, Alexander and Reif, Wolfgang and Beyrle, Matthias and Faber, Jan and Richter, Michaela and Sause, Markus}, title = {Towards real-time process monitoring and machine learning for manufacturing composite structures}, booktitle = {Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-8956-7}, doi = {https://doi.org/10.1109/ETFA46521.2020.9212097}, pages = {1455 -- 1458}, year = {2020}, language = {en} } @inproceedings{StieberSchroeterSchiendorferetal.2021, author = {Stieber, Simon and Schr{\"o}ter, Niklas and Schiendorfer, Alexander and Hoffmann, Alwin and Reif, Wolfgang}, title = {FlowFrontNet: Improving Carbon Composite Manufacturing with CNNs}, booktitle = {Machine Learning and Knowledge Discovery in Databases, Applied Data Science Track, Proceedings, Part IV}, editor = {Dong, Yuxiao and Mladenić, Dunja and Saunders, Craig}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-67667-4}, doi = {https://doi.org/10.1007/978-3-030-67667-4_25}, pages = {411 -- 426}, year = {2021}, language = {en} } @article{StieberSchroeterFausteretal.2022, author = {Stieber, Simon and Schr{\"o}ter, Niklas and Fauster, Ewald and Bender, Marcel and Schiendorfer, Alexander and Reif, Wolfgang}, title = {Inferring material properties from FRP processes via sim-to-real learning}, volume = {128}, journal = {The International Journal of Advanced Manufacturing Technology}, number = {3-4}, publisher = {Springer}, address = {London}, issn = {1433-3015}, doi = {https://doi.org/10.1007/s00170-023-11509-8}, pages = {1517 -- 1533}, year = {2022}, abstract = {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.}, language = {en} }