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 - 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 - TY - CHAP A1 - Koval, Leonid A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Optimizing AI-Driven Production in Industry 4.0: A Morphological Box and Taxonomy Approach T2 - 2025 International Conference on Computer Technology Applications (ICCTA) UR - https://doi.org/10.1109/ICCTA65425.2025.11166106 Y1 - 2025 UR - https://doi.org/10.1109/ICCTA65425.2025.11166106 SN - 979-8-3315-1265-1 SP - 272 EP - 278 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Koval, Leonid A1 - Wächter, Sonja A1 - Erdogan, Hüseyin A1 - Großmann, Daniel T1 - Ontology-Driven Modeling and Integration of Production Processes in Advanced Driver-Assistance Systems within the Gaia-X Ecosystem T2 - 2025 11th International Conference on Computer Technology Applications (ICCTA 2025) UR - https://doi.org/10.1109/ICCTA65425.2025.11166225 Y1 - 2025 UR - https://doi.org/10.1109/ICCTA65425.2025.11166225 SN - 979-8-3315-1265-1 SP - 211 EP - 216 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Mathias, Selvine George A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Knollmeyer, Simon A1 - Koval, Leonid A1 - Grossmann, Daniel ED - Maglogiannis, Ilias ED - Iliadis, Lazaros ED - Andreou, Andreas ED - Papaleonidas, Antonios T1 - Handling Anonymized Non-numerical Features in Data Using Transformations for Regression Models T2 - Artificial Intelligence Applications and Innovations: 21st IFIP WG 12.5 International Conference, AIAI 2025, Limassol, Cyprus, June 26–29, 2025, Proceedings, Part III UR - https://doi.org/10.1007/978-3-031-96231-8_8 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-96231-8_8 SN - 978-3-031-96231-8 SP - 99 EP - 111 PB - Springer CY - Cham ER - TY - CHAP A1 - Akmal, Muhammad Uzair A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Humanizing AI: A Human-Centered Architecture to Developing Trustworthy Intelligent Systems T2 - CS & IT Conference Proceedings Y1 - 2025 UR - https://www.csitcp.com/abstract/15/158csit03 SN - 978-1-923107-57-1 SP - 27 EP - 44 PB - AIRCC CY - [s. l.] ER -