@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} } @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} } @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} } @inproceedings{AkmalAsifKovaletal.2025, author = {Akmal, Muhammad Uzair and Asif, Saara and Koval, Leonid and Mathias, Selvine George and Knollmeyer, Simon and Großmann, Daniel}, title = {Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation}, booktitle = {Artificial Intelligence: Methodology, Systems, and Applications: 19th International Conference, AIMSA 2024, Varna, Bulgaria, September 18-20, 2024, Proceedings}, editor = {Koprinkova-Hristova, Petia and Kasabov, Nikola}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-81542-3}, doi = {https://doi.org/10.1007/978-3-031-81542-3_11}, pages = {128 -- 142}, year = {2025}, language = {en} } @inproceedings{MathiasAsifAkmaletal.2025, author = {Mathias, Selvine George and Asif, Saara and Akmal, Muhammad Uzair and Knollmeyer, Simon and Koval, Leonid and Großmann, Daniel}, title = {Industrial Image Grouping Through Pre-Trained CNN Encoder-Based Feature Extraction and Sub-Clustering}, booktitle = {Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 2}, editor = {Rocha, Ana Paula and Steels, Luc and van den Herik, Jaap}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-737-5}, doi = {https://doi.org/10.5220/0013189000003890}, pages = {496 -- 506}, year = {2025}, abstract = {A common challenge faced by many industries today is the classification of unlabeled image data from production processes into meaningful groups or patterns for better documentation and analysis. This paper presents a sequential approach for leveraging industrial image data to identify patterns in products or processes for plant floor operators. The dataset used is sourced from steel production, and the model architecture integrates feature reduction through convolutional neural networks (CNNs) like VGG, EfficientNet, and ResNet, followed by clustering algorithms to assign appropriate labels to the observed data. The model's selection criteria combine clustering metrics, including entropy minimization and silhouette score maximization. Once primary clusters are identified, sub-clustering is performed using near-labels, which are pre-assigned to images with initial distinctions. A novel metric, C-Score, is introduced to assess cluster convergence and grouping accuracy. Experimental re sults demonstrate that this method can address challenges in detecting variations across images, improving pattern recognition and classification.}, language = {en} } @inproceedings{KnollmeyerAkmalKovaletal.2024, author = {Knollmeyer, Simon and Akmal, Muhammad Uzair and Koval, Leonid and Asif, Saara and Mathias, Selvine George and Großmann, Daniel}, title = {Document Knowledge Graph to Enhance Question Answering with Retrieval Augmented Generation}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Facchinetti, Tullio and Cenedese, Angelo and Lo Bello, Lucia and Vitturi, Stefano and Sauter, Thilo and Tramarin, Federico}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6123-0}, doi = {https://doi.org/10.1109/ETFA61755.2024.10711054}, year = {2024}, language = {en} }