TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - An approach for aggregation and historicization of production entities in the graph T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9211907 KW - information modelling KW - aggregation KW - graph database Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9211907 SN - 978-1-7281-8956-7 SP - 1107 EP - 1110 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mueller, Ralph A1 - Großmann, Daniel A1 - Schmied, Sebastian A1 - Mathias, Selvine George T1 - Validation of dynamic interoperability and virtual commissioning of production equipment in early development stages T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9211988 KW - virtual commissioning KW - information model KW - cyber physical system KW - digital twin KW - machine communication KW - design engineering process Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9211988 SN - 978-1-7281-8956-7 SP - 1159 EP - 1162 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mueller, Ralph A1 - Mroß, Björn T1 - A Compliance Testing Structure for Implementation of Industry Standards through OPC UA T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9212119 KW - OPC UA KW - compliance testing KW - information models KW - servers KW - clients Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9212119 SN - 978-1-7281-8956-7 SP - 1091 EP - 1094 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Thakur, Akshay A1 - Beck, Robert A1 - Mostaghim, Sanaz A1 - Großmann, Daniel T1 - Machine Learning for evaluating Kaizens in Volkswagen Production System - An Industrial Case study T2 - Proceedings: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics: DSAA 2020 UR - https://doi.org/10.1109/DSAA49011.2020.00114 KW - supervised machine learning KW - Kaizen KW - best practices evaluation KW - clustering KW - production system Y1 - 2020 UR - https://doi.org/10.1109/DSAA49011.2020.00114 SN - 978-1-7281-8206-3 SP - 781 EP - 782 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Banerjee, Suprateek T1 - Vertical integration via dynamic aggregation of information in OPC UA T2 - Intelligent Information and Database Systems UR - https://doi.org/10.1007/978-981-15-3380-8_18 KW - OPC UA KW - interoperability KW - aggregation Y1 - 2020 UR - https://doi.org/10.1007/978-981-15-3380-8_18 SN - 978-981-15-3379-2 SN - 978-981-15-3380-8 SN - 1865-0929 SN - 1865-0937 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 204 EP - 215 PB - Springer CY - Singapur ER - TY - CHAP A1 - Banerjee, Suprateek A1 - Großmann, Daniel T1 - OPC UA and Dynamic Web Services - A generic flexible Industrial Communication Approach T2 - ICCAE 2019: Proceedings of the 2019 11th International Conference on Computer and Automation Engineering UR - https://doi.org/10.1145/3313991.3313996 KW - automation KW - OPC UA KW - web services KW - vertical integration KW - internet of services KW - industrial internet of things Y1 - 2019 UR - https://doi.org/10.1145/3313991.3313996 SN - 978-1-4503-6287-0 SP - 114 EP - 117 PB - ACM CY - New York ER - TY - CHAP A1 - Mathias, Selvine George A1 - Mancha, Mathew John A1 - Großmann, Daniel A1 - Kujat, Bernd A1 - Schiebold, Kay T1 - Investigations on numerical techniques for detecting variations in acoustic emissions T2 - IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society UR - https://doi.org/10.1109/IECON48115.2021.9589074 KW - acoustic emissions KW - Gaussian mixture model KW - self-organizing map KW - comparison Y1 - 2021 UR - https://doi.org/10.1109/IECON48115.2021.9589074 SN - 978-1-6654-3554-3 PB - IEEE CY - Piscataway (NJ) ER - TY - CHAP A1 - Schmied, Sebastian A1 - Großmann, Daniel A1 - Mathias, Selvine George A1 - Mueller, Ralph T1 - An approach for an industrial information model management T2 - Proceedings, 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) UR - https://doi.org/10.1109/ICPS48405.2020.9274751 KW - information modelling KW - cyber physical system KW - OPC UA Y1 - 2020 UR - https://doi.org/10.1109/ICPS48405.2020.9274751 SN - 978-1-7281-6389-5 SP - 402 EP - 405 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - de Caigny, Jan A1 - Tauchnitz, Thomas A1 - Becker, Ronny A1 - Diedrich, Christian A1 - Schröder, Tizian A1 - Großmann, Daniel A1 - Banerjee, Suprateek A1 - Graube, Markus A1 - Urbas, Leon T1 - NOA – von Demonstratoren zu Pilotanwendungen T1 - NOA – from demonstrators to pilot applications BT - vier Anwendungsfälle der Namur Open Architecture BT - four applications of Namur Open Architecture JF - atp magazin UR - https://doi.org/10.17560/atp.v61i1-2.2403 KW - NOA KW - Namur Open Architecture KW - NOA-Diode KW - NOA-Informationsmodell KW - Verification of Request KW - Anwendungsbeispiel KW - NOA KW - Namur Open Architecture KW - NOA diode KW - NOA information model KW - verification of request KW - use cases Y1 - 2019 UR - https://doi.org/10.17560/atp.v61i1-2.2403 SN - 2190‑4111 VL - 61 IS - 1-2 SP - 44 EP - 55 PB - Vulkan CY - Essen ER - TY - CHAP A1 - Kampa, Thomas A1 - El-Ankah, Amer A1 - Großmann, Daniel ED - Dörksen, Helene ED - Scanzio, Stefano ED - Jasperneite, Jürgen ED - Wisniewski, Lukasz ED - Man, Kim Fung ED - Sauter, Thilo ED - Seno, Lucia ED - Trsek, Henning ED - Vyatkin, Valeriy T1 - High Availability for virtualized Programmable Logic Controllers with Hard Real-Time Requirements on Cloud Infrastructures T2 - 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) UR - https://doi.org/10.1109/INDIN51400.2023.10218014 KW - RDMA KW - vPLC KW - state synchronization Y1 - 2023 UR - https://doi.org/10.1109/INDIN51400.2023.10218014 SN - 978-1-6654-9313-0 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Koval, Leonid A1 - Gaafar, Mahmoud A. A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - The Effect of Annotation Quality on Wear Semantic Segmentation by CNN JF - Sensors N2 - 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. UR - https://doi.org/10.3390/s24154777 Y1 - 2024 UR - https://doi.org/10.3390/s24154777 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49551 SN - 1424-8220 VL - 24 IS - 15 PB - MDPI CY - Basel ER - TY - CHAP A1 - Knollmeyer, Simon A1 - Mroß, Björn A1 - Mueller, Ralph A1 - Großmann, Daniel T1 - Ontology based knowledge graph for information and knowledge management in factory planning T2 - 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA54631.2023.10275409 Y1 - 2023 UR - https://doi.org/10.1109/ETFA54631.2023.10275409 SN - 979-8-3503-3991-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Kampa, Thomas A1 - Großmann, Daniel T1 - Half&Half: Intra-Flow Load Balancing and High Availability for Edge Cloud-enabled Manufacturing T2 - 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA54631.2023.10275483 Y1 - 2023 UR - https://doi.org/10.1109/ETFA54631.2023.10275483 SN - 979-8-3503-3991-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Großmann, Daniel T1 - Efficacy of Statistical Formulations on Acoustic Emission Signals for Tool Wear Predictions T2 - Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL N2 - 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. UR - https://doi.org/10.5220/0010676400003062 KW - acoustic emission KW - tool wear KW - data imputations KW - statistical approach Y1 - 2021 UR - https://doi.org/10.5220/0010676400003062 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18695 SN - 978-989-758-535-7 SP - 108 EP - 115 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Knollmeyer, Simon A1 - Caymazer, Oğuz A1 - Großmann, Daniel T1 - Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain JF - Electronics N2 - 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. UR - https://doi.org/10.3390/electronics14112102 Y1 - 2025 UR - https://doi.org/10.3390/electronics14112102 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59524 SN - 2079-9292 VL - 14 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Tangirala, Sri Girish A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks JF - Sensors N2 - 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. UR - https://doi.org/10.3390/s25051575 Y1 - 2025 UR - https://doi.org/10.3390/s25051575 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58058 SN - 1424-8220 VL - 25 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2025.3574785 Y1 - 2025 UR - https://doi.org/10.1109/ACCESS.2025.3574785 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59669 SN - 2169-3536 VL - 13 SP - 96400 EP - 96422 PB - IEEE CY - New York ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Koval, Leonid A1 - Gaafar, Mahmoud A. A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Automatized End Mill Wear Inspection Using a Novel Illumination Unit and Convolutional Neural Network JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2024.3454692 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3454692 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58160 SN - 2169-3536 VL - 12 SP - 124282 EP - 124297 PB - IEEE CY - New York ER - TY - CHAP A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Koval, Leonid A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel ED - Koprinkova-Hristova, Petia ED - Kasabov, Nikola T1 - Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation T2 - Artificial Intelligence: Methodology, Systems, and Applications: 19th International Conference, AIMSA 2024, Varna, Bulgaria, September 18–20, 2024, Proceedings UR - https://doi.org/10.1007/978-3-031-81542-3_11 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-81542-3_11 SN - 978-3-031-81542-3 SP - 128 EP - 142 PB - Springer CY - Cham ER - TY - CHAP A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Knollmeyer, Simon A1 - Koval, Leonid A1 - Großmann, Daniel ED - Rocha, Ana Paula ED - Steels, Luc ED - van den Herik, Jaap T1 - Industrial Image Grouping Through Pre-Trained CNN Encoder-Based Feature Extraction and Sub-Clustering T2 - Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 2 N2 - 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. UR - https://doi.org/10.5220/0013189000003890 Y1 - 2025 UR - https://doi.org/10.5220/0013189000003890 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58004 SN - 978-989-758-737-5 SP - 496 EP - 506 PB - SciTePress CY - Setúbal ER -