@inproceedings{GerngrossKohlerEndischetal.2020, author = {Gerngroß, Martin and Kohler, Markus and Endisch, Christian and Kennel, Ralph}, title = {Model-based control of nonlinear wire tension in dynamic needle winding processes}, booktitle = {Proceedings, 2020 IEEE International Conference on Industrial Technology}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5754-2}, doi = {https://doi.org/10.1109/ICIT45562.2020.9067168}, pages = {381 -- 388}, year = {2020}, language = {en} } @inproceedings{KohlerFendtEndisch2022, author = {Kohler, Markus and Fendt, David and Endisch, Christian}, title = {Modeling Geometric Wire Bending Behavior in Needle Winding Processes Using Circular Arcs with Tangential Linear Functions}, booktitle = {ITEC+2022: 2022 IEEE/AIAA Transportation Electrification Conference and Electric Aircraft Technologies Symposium (ITEC+EATS)}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-6654-0560-7}, doi = {https://doi.org/10.1109/ITEC53557.2022.9814041}, pages = {894 -- 901}, year = {2022}, language = {en} } @inproceedings{KohlerGerngrossEndisch2022, author = {Kohler, Markus and Gerngroß, Martin and Endisch, Christian}, title = {A Test Bench Concept and Method for Image-Based Modeling of Geometric Wire Bending Behavior in Needle Winding Processes}, booktitle = {2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8240-0}, issn = {2163-5145}, doi = {https://doi.org/10.1109/ISIE51582.2022.9831494}, pages = {1113 -- 1120}, year = {2022}, language = {en} } @inproceedings{KohlerHerreraGerngrossetal.2024, author = {Kohler, Markus and Herrera, Christian and Gerngroß, Martin and Kennel, Ralph and Endisch, Christian}, title = {Empirical Investigation and Feed-Forward Control of Wire Tension in Needle Winding Processes}, booktitle = {2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE), Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9408-5}, doi = {https://doi.org/10.1109/ISIE54533.2024.10595747}, year = {2024}, language = {en} } @article{KohlerMitsiosEndisch2024, author = {Kohler, Markus and Mitsios, Dionysios and Endisch, Christian}, title = {Reconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity}, volume = {2025}, journal = {Journal of Manufacturing Systems}, number = {78}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1878-6642}, doi = {https://doi.org/10.1016/j.jmsy.2024.12.005}, pages = {410 -- 432}, year = {2024}, abstract = {Manufacturing wound rotor synchronous machines (WRSMs) for electric vehicle traction systems necessitates rigorous quality inspection to ensure optimal product performance and efficiency. This paper presents a novel visual anomaly detection method for monitoring the needle winding process of WRSMs, utilizing unsupervised learning with convolutional autoencoders (CAEs) and the structural similarity index measure (SSIM). The method identifies deviations from the desired orthocyclic winding pattern during each stage of the winding process, enabling early detection of winding errors and preventing resource wastage and potential damage to the product or winding machinery. Trajectory-synchronized frame extraction aligns the visual inspection system with the winding trajectory, ensuring precise monitoring traceable to a specific point in the winding process. We present the comprehensive Winding Anomaly Dataset (WAD), which comprises images of WRSM rotor prototypes with and without winding faults recorded in different lighting conditions. The proposed reconstruction-based anomaly detection technique is trained on fault-free data only and utilizes the introduced masked mean structural dissimilarity index measure (MMSDIM) to focus on the relevant sections of the winding during inference. Comprehensive comparative analysis reveals that the CAE with unregularized latent space and the maximum mean discrepancy Wasserstein autoencoder (MMD-WAE) outperform the beta variational autoencoder (beta-VAE) in terms of anomaly detection performance, with the CAE and WAE delivering comparable results. Extensive testing confirms the approach's effectiveness, achieving 95.6 \% recall at 100 \% precision, an AUROC of 99.9 \%, and an average precision of 99.1 \% on the challenging WAD, considerably outperforming state-of-the-art visual anomaly detection models. This work thus offers a robust solution for WRSM production quality monitoring and promotes the incorporation of visual inspection in electric drive manufacturing systems.}, language = {en} }