@inproceedings{BhavnaniSchiendorfer2022, author = {Bhavnani, Sidhant and Schiendorfer, Alexander}, title = {Towards copeland optimization in combinatorial problems}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022; Proceedings}, editor = {Schaus, Pierre}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-08010-4}, doi = {https://doi.org/10.1007/978-3-031-08011-1_4}, pages = {34 -- 43}, year = {2022}, language = {en} } @inproceedings{HankeKosakSchiendorferetal.2018, author = {Hanke, Julian and Kosak, Oliver and Schiendorfer, Alexander and Reif, Wolfgang}, title = {Self-organized Resource Allocation for Reconfigurable Robot Ensembles}, booktitle = {2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-5386-5172-8}, doi = {https://doi.org/10.1109/SASO.2018.00022}, pages = {110 -- 119}, year = {2018}, language = {en} } @inproceedings{SchiendorferGajekReif2021, author = {Schiendorfer, Alexander and Gajek, Carola and Reif, Wolfgang}, title = {Turning Software Engineers into Machine Learning Engineers}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, number = {141}, editor = {Bischl, Bernd and Guhr, Oliver and Seibold, Heidi and Steinbach, Peter}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v141/schiendorfer21a.html}, pages = {36 -- 41}, year = {2021}, language = {en} } @inproceedings{EllaGajekSchiendorferetal.2021, author = {Ella, Miriam and Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {An Interactive Web Application for Decision Tree Learning}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, number = {141}, editor = {Bischl, Bernd and Guhr, Oliver and Seibold, Heidi and Steinbach, Peter}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v141/elia21a.html}, pages = {11 -- 16}, 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{SchiendorferReif2019, author = {Schiendorfer, Alexander and Reif, Wolfgang}, title = {Reducing bias in preference aggregation for multiagent soft constraint problems}, booktitle = {Principles and Practice of Constraint Programming, 25th International Conference, CP 2019, Proceedings}, editor = {Schiex, Thomas and de Givry, Simon}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-30047-0}, doi = {https://doi.org/10.1007/978-3-030-30048-7_30}, pages = {510 -- 526}, year = {2019}, language = {en} } @inproceedings{GajekSchiendorferReif2019, author = {Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {A chained neural network model for photovoltaic power forecast}, booktitle = {Machine Learning, Optimization, and Data Science: 5th International Conference, LOD 2019, Siena, Italy, September 10-13, 2019, Proceedings}, editor = {Nicosia, Giuseppe and Pardalos, Panos and Umeton, Renato and Giuffrida, Giovanni and Sciacca, Vincenzo}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-37598-0}, issn = {0302-9743}, doi = {https://doi.org/10.1007/978-3-030-37599-7_47}, pages = {566 -- 578}, year = {2019}, 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} } @inproceedings{GajekSchiendorferReif2023, author = {Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks}, booktitle = {Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I}, editor = {Amini, Massih-Reza and Canu, St{\´e}phane and Fischer, Asja and Guns, Tias and Kralj Novak, Petra and Tsoumakas, Grigorios}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-26387-3}, doi = {https://doi.org/10.1007/978-3-031-26387-3_28}, pages = {457 -- 473}, year = {2023}, language = {en} }