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 - TY - JOUR A1 - Mathias, Selvine George A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Pattern Identifications in Transformed Acoustic Signals Using Classification Models JF - Procedia CIRP N2 - Pattern identifications in signals is necessary to discern variations from approved normal values in different scenarios. With machine learning algorithms, it is possible to use hybrid methods of pattern identifications such as feature extractions followed by classifications and/or clustering. This paper presents a pattern identification approach of acoustic signals using their transformations as inputs to classification algorithms. The analysis is carried out on two transformed versions of acoustic emission (AE) hits such as log transformations of peak hits and binary sequencing based on threshold crossing. A comparative analysis using custom data loss metrics is made to determine which inputs provide the best information in predictive methods for identifying commonly occurring patterns while acknowledging significant data loss. The methodology is conducted on transformed versions of a public dataset and the results show that patterns can be discerned to above 90% accuracy with the transformed datasets. The experimental results yield that actual source signals need not be utilized depending on which transformations suit the practical application. UR - https://doi.org/10.1016/j.procir.2024.10.061 Y1 - 2024 UR - https://doi.org/10.1016/j.procir.2024.10.061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56233 SN - 2212-8271 VL - 2024 IS - 130 SP - 93 EP - 99 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Knollmeyer, Simon A1 - Akmal, Muhammad Uzair A1 - Koval, Leonid A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Großmann, Daniel ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Document Knowledge Graph to Enhance Question Answering with Retrieval Augmented Generation T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10711054 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10711054 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Mathias, Selvine George A1 - Großmann, Daniel ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Supervised Anomaly Detection for Production Line Images using Data Augmentation and Convolutional Neural Network T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10710718 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10710718 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Fleischmann, Albert A1 - Friedl, Anton A1 - Großmann, Daniel A1 - Schmidt, Werner T1 - Modeling and implementing of industrie 4.0 scenarios T2 - Modelling to Program UR - https://doi.org/10.1007/978-3-030-72696-6_4 KW - Industrie 4.0 KW - Industry 4.0 KW - subject oriented modeling KW - business process management Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-72696-6_4 SN - 978-3-030-72695-9 SN - 978-3-030-72696-6 SN - 1865-0929 SN - 1865-0937 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 90 EP - 112 PB - Springer CY - Cham 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 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - Addressing the complexity of AI Integration in Manufacturing: A Morphological Analysis T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10711011 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10711011 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Koval, Leonid A1 - Mathias, Selvine George A1 - Knollmeyer, Simon A1 - Großmann, Daniel ED - Sgurev, Vassil ED - Jotsov, Vladimir ED - Piuri, Vincenzo ED - Doukovska, Luybka ED - Yoshinov, Radoslav T1 - A Conceptual Framework for Addressing Class Imbalance in Image Data: Challenges and Strategies T2 - 2024 IEEE 12th International Conference on Intelligent Systems (IS): Proceedings UR - https://doi.org/10.1109/IS61756.2024.10705251 Y1 - 2024 UR - https://doi.org/10.1109/IS61756.2024.10705251 SN - 979-8-3503-5098-2 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 - Nowacki, Natalie Samanta A1 - Mueller, Ralph A1 - Großmann, Daniel A1 - Lueder, Arndt T1 - Approach for identifying data usage information objects for the later implementation in production environments BT - Enabling a logic for the information modelling based on use cases using a conversation concept T2 - 2023 The 10th International Conference on Industrial Engineering and Applications (Europe), ICIEA-EU 2023 UR - https://doi.org/10.1145/3587889.3587915 Y1 - 2023 UR - https://doi.org/10.1145/3587889.3587915 SN - 978-1-4503-9852-7 SP - 169 EP - 175 PB - ACM CY - New York ER - TY - JOUR A1 - Mathias, Selvine George A1 - Großmann, Daniel T1 - Use Cases of Data Reduction to Time Series Data in Sensor Monitoring JF - Journal of Ubiquitous Systems and Pervasive Networks UR - https://doi.org/10.5383/JUSPN.16.02.005 Y1 - 2022 UR - https://doi.org/10.5383/JUSPN.16.02.005 SN - 1923-7332 VL - 16 IS - 2 SP - 87 EP - 92 PB - IASKS CY - [s. l.] ER - TY - JOUR A1 - Schmied, Sebastian A1 - Mathias, Selvine George A1 - Großmann, Daniel A1 - Mueller, Ralph A1 - Jumar, Ulrich T1 - Information modelling with focus on existing manufacturing systems JF - Annual Reviews in Control UR - https://doi.org/10.1016/j.arcontrol.2021.04.010 KW - information model KW - aggregation KW - OPC UA KW - cyber–physical manufacturing systems Y1 - 2021 UR - https://doi.org/10.1016/j.arcontrol.2021.04.010 SN - 1367-5788 VL - 2021 IS - 51 SP - 392 EP - 400 PB - Elsevier CY - Amsterdam ER -