@inproceedings{SchmiedGrossmannMathiasetal.2020, author = {Schmied, Sebastian and Großmann, Daniel and Mathias, Selvine George and Mueller, Ralph}, title = {An approach for aggregation and historicization of production entities in the graph}, 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/ETFA46521.2020.9211907}, pages = {1107 -- 1110}, year = {2020}, language = {en} } @inproceedings{MuellerGrossmannSchmiedetal.2020, author = {Mueller, Ralph and Großmann, Daniel and Schmied, Sebastian and Mathias, Selvine George}, title = {Validation of dynamic interoperability and virtual commissioning of production equipment in early development stages}, 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/ETFA46521.2020.9211988}, pages = {1159 -- 1162}, year = {2020}, language = {en} } @inproceedings{MathiasSchmiedGrossmannetal.2020, author = {Mathias, Selvine George and Schmied, Sebastian and Großmann, Daniel and Mueller, Ralph and Mroß, Bj{\"o}rn}, title = {A Compliance Testing Structure for Implementation of Industry Standards through OPC UA}, 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/ETFA46521.2020.9212119}, pages = {1091 -- 1094}, year = {2020}, language = {en} } @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} } @article{KnollmeyerCaymazerGrossmann2025, author = {Knollmeyer, Simon and Caymazer, Oğuz and Großmann, Daniel}, title = {Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain}, volume = {14}, pages = {2102}, journal = {Electronics}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {2079-9292}, doi = {https://doi.org/10.3390/electronics14112102}, year = {2025}, abstract = {Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document's intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG's structured retrieval strategy, highlighting its advantages in complex reasoning tasks.}, language = {en} } @article{BilalPodishettiTangiralaetal.2025, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Tangirala, Sri Girish and Großmann, Daniel and Bregulla, Markus}, title = {CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks}, volume = {25}, pages = {1575}, journal = {Sensors}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s25051575}, year = {2025}, abstract = {Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features.}, language = {en} } @article{BilalPodishettiGrossmannetal.2025, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Großmann, Daniel and Bregulla, Markus}, title = {Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2025.3574785}, pages = {96400 -- 96422}, year = {2025}, abstract = {In this study, we introduce the ToolSurface-144 dataset, which is presented here for the first time. It comprises four subsets - Full R, Full S, Top R, and Top S - each containing 144 tool classes captured under varying illumination conditions and fields of view. A newly developed, patented imaging approach was employed to acquire the data. It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. In this study, we evaluate six state-of-the-art convolutional neural networks—AlexNet, DenseNet161, EfficientNet-B0, ResNet152, ResNet50, and VGG16—using three training strategies: fine-tuning, freezing of pre-trained layers, and training from scratch. The results show that EfficientNet-B0 consistently achieved the highest classification accuracy in nearly all experiments and data sets. Especially using the fine-tuning training strategy, the model achieved 99\% accuracy in tool classification. ResNet50 benefited greatly from fine-tuning and freezing, achieving a significant increase in performance compared to training from scratch. In contrast, ResNet152, AlexNet, and VGG16 consistently showed poor classification performance, indicating difficulties regarding learning and generalisation. The results show that diffuse illumination and complete tool views provide the best classification conditions, while restricted image sections with homogeneous illumination negatively affect model performance. Among the evaluated training strategies, fine-tuning proved the most efficient training method for developing CNN models for tool classification.}, 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 = {Automatized End Mill Wear Inspection Using a Novel Illumination Unit and Convolutional Neural Network}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2024.3454692}, pages = {124282 -- 124297}, year = {2024}, abstract = {Ensuring cutting tools are in optimal condition is essential for achieving peak machining performance, given their direct impact on both workpiece quality and process efficiency. However, accurately assessing wear on end mills, especially those with complex geometries, pose a significant challenge due to their reflective surfaces and varied wear patterns. Presented here is a novel method that addresses this challenge by employing a customized illumination unit in conjunction with a convolutional neural network (CNN) for end mill wear analysis. This innovative approach involves utilizing the specially designed illumination unit to capture high-quality images, enabling precise examination of material wear on helically shaped end mills. Notably, this method is tailored to illuminate reflective surfaces and represents a pioneering application in the realm of wear testing.We validate the viability of this approach by employing CNN-based models to segment wear on complex-shaped end mills coated with titanium carbonitride (TiCN) and titanium nitride (TiN). We achieved remarkable mean Intersection over Union (mIoU) results in wear detection on a test dataset: 0.99 for tool segmentation, 0.78 for abnormal wear, and 0.71 for normal wear segmentation.}, 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} }