@inproceedings{ThakurBeckMostaghimetal.2020, author = {Thakur, Akshay and Beck, Robert and Mostaghim, Sanaz and Großmann, Daniel}, title = {Machine Learning for evaluating Kaizens in Volkswagen Production System - An Industrial Case study}, booktitle = {Proceedings: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics: DSAA 2020}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-8206-3}, doi = {https://doi.org/10.1109/DSAA49011.2020.00114}, pages = {781 -- 782}, year = {2020}, language = {en} } @inproceedings{SchmiedGrossmannMathiasetal.2020, author = {Schmied, Sebastian and Großmann, Daniel and Mathias, Selvine George and Banerjee, Suprateek}, title = {Vertical integration via dynamic aggregation of information in OPC UA}, booktitle = {Intelligent Information and Database Systems}, publisher = {Springer}, address = {Singapur}, isbn = {978-981-15-3379-2}, issn = {1865-0929}, doi = {https://doi.org/10.1007/978-981-15-3380-8_18}, pages = {204 -- 215}, year = {2020}, language = {en} } @inproceedings{BanerjeeGrossmann2019, author = {Banerjee, Suprateek and Großmann, Daniel}, title = {OPC UA and Dynamic Web Services - A generic flexible Industrial Communication Approach}, booktitle = {ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-6287-0}, doi = {https://doi.org/10.1145/3313991.3313996}, pages = {114 -- 117}, year = {2019}, language = {en} } @inproceedings{MathiasManchaGrossmannetal.2021, author = {Mathias, Selvine George and Mancha, Mathew John and Großmann, Daniel and Kujat, Bernd and Schiebold, Kay}, title = {Investigations on numerical techniques for detecting variations in acoustic emissions}, booktitle = {IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-6654-3554-3}, doi = {https://doi.org/10.1109/IECON48115.2021.9589074}, year = {2021}, language = {en} } @inproceedings{SchmiedGrossmannMathiasetal.2020, author = {Schmied, Sebastian and Großmann, Daniel and Mathias, Selvine George and Mueller, Ralph}, title = {An approach for an industrial information model management}, booktitle = {Proceedings, 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6389-5}, doi = {https://doi.org/10.1109/ICPS48405.2020.9274751}, pages = {402 -- 405}, year = {2020}, language = {en} } @article{deCaignyTauchnitzBeckeretal.2019, author = {de Caigny, Jan and Tauchnitz, Thomas and Becker, Ronny and Diedrich, Christian and Schr{\"o}der, Tizian and Großmann, Daniel and Banerjee, Suprateek and Graube, Markus and Urbas, Leon}, title = {NOA - von Demonstratoren zu Pilotanwendungen}, volume = {61}, journal = {atp magazin}, subtitle = {vier Anwendungsf{\"a}lle der Namur Open Architecture}, number = {1-2}, publisher = {Vulkan}, address = {Essen}, issn = {2190‑4111}, doi = {https://doi.org/10.17560/atp.v61i1-2.2403}, pages = {44 -- 55}, year = {2019}, language = {de} } @inproceedings{KampaElAnkahGrossmann2023, author = {Kampa, Thomas and El-Ankah, Amer and Großmann, Daniel}, title = {High Availability for virtualized Programmable Logic Controllers with Hard Real-Time Requirements on Cloud Infrastructures}, booktitle = {2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}, editor = {D{\"o}rksen, Helene and Scanzio, Stefano and Jasperneite, J{\"u}rgen and Wisniewski, Lukasz and Man, Kim Fung and Sauter, Thilo and Seno, Lucia and Trsek, Henning and Vyatkin, Valeriy}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-9313-0}, doi = {https://doi.org/10.1109/INDIN51400.2023.10218014}, year = {2023}, language = {en} } @article{BilalPodishettiKovaletal.2024, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Koval, Leonid and Gaafar, Mahmoud A. and Großmann, Daniel and Bregulla, Markus}, title = {The Effect of Annotation Quality on Wear Semantic Segmentation by CNN}, volume = {24}, pages = {4777}, journal = {Sensors}, number = {15}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s24154777}, year = {2024}, abstract = {In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32\% compared to 8.6\% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation.}, language = {en} } @inproceedings{KnollmeyerMrossMuelleretal.2023, author = {Knollmeyer, Simon and Mroß, Bj{\"o}rn and Mueller, Ralph and Großmann, Daniel}, title = {Ontology based knowledge graph for information and knowledge management in factory planning}, booktitle = {2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-3991-8}, doi = {https://doi.org/10.1109/ETFA54631.2023.10275409}, year = {2023}, language = {en} } @inproceedings{KampaGrossmann2023, author = {Kampa, Thomas and Großmann, Daniel}, title = {Half\&Half: Intra-Flow Load Balancing and High Availability for Edge Cloud-enabled Manufacturing}, booktitle = {2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-3991-8}, doi = {https://doi.org/10.1109/ETFA54631.2023.10275483}, year = {2023}, language = {en} } @inproceedings{MathiasGrossmann2021, author = {Mathias, Selvine George and Großmann, Daniel}, title = {Efficacy of Statistical Formulations on Acoustic Emission Signals for Tool Wear Predictions}, booktitle = {Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-535-7}, doi = {https://doi.org/10.5220/0010676400003062}, pages = {108 -- 115}, year = {2021}, abstract = {Acoustic emission (AE) signals obtained during machining processes can be used to detect, locate and assess flaws in structures made of metal, concrete or composites. This paper aims to characterize AE signals using derived parameters from raw signatures along with statistical feature extractions to correlate with tool wear readings. Missing tool wear values are imputed using domain knowledge rules and compared to AE signals using machine learning models. The amount of effect on tool wear is formulated using Bayesian Inferences on derived parameters such as areas under the raw signal curve in addition to comparisons with the supervised models for predictions. Using the constructed models and formulation, the presented study also includes a trace-back pseudo-algorithm for determining the stage in process where tool wear values begin to approach the wear limits.}, language = {en} }