TY - JOUR A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology JF - IEEE Access N2 - Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of “Production IT.” Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis. UR - https://doi.org/10.1109/ACCESS.2024.3406020 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3406020 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49240 SN - 2169-3536 VL - 12 SP - 80266 EP - 80282 PB - IEEE CY - New York ER - TY - CHAP A1 - Koval, Leonid A1 - Pfaller, Daniel A1 - Bilal, Mühenad A1 - Bregulla, Markus A1 - Cupek, Rafal T1 - An analysis of convolutional neural network models for classifying machine tools T2 - Advances in Computational Collective Intelligence UR - https://doi.org/10.1007/978-3-030-88113-9_37 KW - image classification KW - domain relevance KW - F1-score KW - confusion-matrix KW - machine tools Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-88113-9_37 SN - 978-3-030-88112-2 SN - 978-3-030-88113-9 SN - 1865-0929 SN - 1865-0937 SP - 461 EP - 473 PB - Springer CY - Cham ER - TY - CHAP A1 - Bilal, Mühenad A1 - Mayer, Christian A1 - Kancharana, Sunil A1 - Bregulla, Markus A1 - Cupek, Rafal A1 - Ziębiński, Adam ED - Bădică, Costin ED - Treur, Jan ED - Benslimane, Djamal ED - Hnatkowska, Bogumiła ED - Krótkiewicz, Marek T1 - Damage Detection of Coated Milling Tools Using Images Captured by Cylindrical Shaped Enclosure Measurement Setup T2 - Advances in Computational Collective Intelligence. 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28–30, 2022, Proceedings UR - https://doi.org/10.1007/978-3-031-16210-7_21 KW - quality inspection KW - damage detection KW - image processing KW - tool regrinding KW - measurement setup Y1 - 2022 UR - https://doi.org/10.1007/978-3-031-16210-7_21 SN - 978-3-031-16209-1 SN - 978-3-031-16210-7 SP - 264 EP - 272 PB - Springer CY - Cham ER - TY - CHAP A1 - Bilal, Mühenad A1 - Kancharana, Sunil A1 - Mayer, Christian A1 - Bregulla, Markus A1 - Ziębiński, Adam A1 - Cupek, Rafal ED - Osten, Wolfgang ED - Nikolaev, Dmitry ED - Zhou, Jianhong T1 - Image-based damage detection on TiN-coated milling tools by using a multi-light scattering illumination technique T2 - Fourteenth International Conference on Machine Vision (ICMV 2021) UR - https://doi.org/10.1117/12.2623140 Y1 - 2022 UR - https://doi.org/10.1117/12.2623140 SN - 978-1-5106-5045-9 SN - 978-1-5106-5044-2 PB - SPIE CY - Bellingham ER - TY - JOUR A1 - Bornschlegl, Martin A1 - Bregulla, Markus A1 - Franke, Jörg T1 - Methods-Energy Measurement BT - An approach for sustainable energy planning of manufacturing technologies JF - Journal of Cleaner Production UR - https://doi.org/10.1016/j.jclepro.2016.06.059 KW - Energy management KW - Energy efficiency KW - Energy measurement KW - Life-cycle costing KW - Methods-Energy Measurement KW - Production planning Y1 - 2016 UR - https://doi.org/10.1016/j.jclepro.2016.06.059 SN - 1879-1786 VL - 2016 IS - 135 SP - 644 EP - 656 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Großmann, Daniel A1 - Bregulla, Markus A1 - Banerjee, Suprateek A1 - Schulz, Dirk A1 - Braun, Roland T1 - OPC UA server aggregation — The foundation for an internet of portals T2 - ETFA'2014: 19th IEEE International Conference on Emerging Technologies and Factory Automation UR - https://doi.org/10.1109/ETFA.2014.7005354 KW - Servers KW - Automation KW - Aggregates KW - Security KW - Prototypes KW - Computer architecture KW - Market research Y1 - 2014 UR - https://doi.org/10.1109/ETFA.2014.7005354 SN - 978-1-4799-4846-8 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 - Bilal, Mühenad A1 - Kancharana, Sunil A1 - Mayer, Christian A1 - Pfaller, Daniel A1 - Koval, Leonid A1 - Bregulla, Markus A1 - Cupek, Rafal A1 - Ziębiński, Adam ED - Farinella, Giovanni Maria ED - Radeva, Petia ED - Bouatouch, Kadi T1 - High Resolution Mask R-CNN-based Damage Detection on Titanium Nitride Coated Milling Tools for Condition Monitoring by using a New Illumination Technique T2 - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications N2 - The implementation of intelligent software in the manufacturing industry is a technology of growing importance and has highlighted the need for improvement in automatization, production, inspection, and quality assurance. An automated inspection system based on deep learning methods can help to enhance inspection and provide a consistent overview of the production line. Camera-based imaging systems are among the most widely used tools, replacing manual industrial quality control tasks. Moreover, an automatized damage detection system on milling tools can be employed in quality control during the coating process and to simplify measuring tool life. Deep Convolutional Neural Networks (DCNNs) are state-of-the-art methods used to extract visual features and classify objects. Hence, there is great interest in applying DCNN in damage detection and classification. However, training a DCNN model on Titanium-Nitride coated (TiN) milling tools is extremely challenging. Due to the coating, the optical properties such as reflection and light scattering on the milling tool surface make image capturing for computer vision tasks quite challenging. In addition to the reflection and scattering, the helical-shaped surface of the cutting tools creates shadows, preventing the neural network from efficient training and damage detection. Here, in the context of applying an automatized deep learning-based method to detect damages on coated milling tools for quality control, the light has been shed on a novel illumination technique that allows capturing high-quality images which makes efficient damage detection for condition monitoring and quality control reliable. The method is outlined along with results obtained in training a ResNet 50 and ResNet 101 model reaching an overall accuracy of 83% from a dataset containing bounding box annotated damages. For instance and semantic segmentation, the state-of-the-art framework Mask R-CNN is employed. UR - https://doi.org/10.5220/0010781800003124 KW - Predictive Maintenance KW - Machine Learning KW - Damage Detection KW - Illumination Source KW - Mask R-CNN Y1 - 2022 UR - https://doi.org/10.5220/0010781800003124 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27498 SN - 978-989-758-555-5 SN - 2184-4321 VL - vol. 5: VISAPP SP - 305 EP - 314 PB - SciTePress CY - Setúbal 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 - Drechsel, Michael A1 - Bornschlegl, Martin A1 - Spreng, Simon A1 - Bregulla, Markus A1 - Franke, Jorg T1 - A New Approach to Integrate Value Stream Analysis into a Continuous Energy Efficiency Improvement Process T2 - Proceedings: IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society UR - https://doi.org/10.1109/IECON.2013.6700382 Y1 - 2014 UR - https://doi.org/10.1109/IECON.2013.6700382 SN - 978-1-4799-0224-8 SP - 7502 EP - 7507 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Bornschlegl, Martin A1 - Kreitlein, Sven A1 - Bregulla, Markus A1 - Franke, Jörg T1 - A method for forecasting the running costs of manufacturing technologies in automotive production during the early planning phase JF - Procedia CIRP N2 - The running costs of production sites are a decisive factor in the overheads of automotive production. Because of this, it is important for many operators to decrease those costs in a sustainable way. Therefore, they try to reduce both the energy consumption costs of production systems, as well as their maintenance costs. However, most parts of the running costs are already determined during the very early phases of the product creation process. The approach in this paper shows how the decision for a specific manufacturing technology influences the factory costs. It is necessary to determine the life-cycle costs with regard to the manufacturing technology. Therefore, deep knowledge about the process itself and the support processes is required. This paper shows how cost relevant parameters can be identified and introduces a method to determine the prospective costs for maintenance and energy consumption in advance. UR - https://doi.org/10.1016/j.procir.2014.07.103 KW - factory planning KW - holistic planning process KW - life-cycle costing KW - sustainable manufacturing KW - running costs Y1 - 2015 UR - https://doi.org/10.1016/j.procir.2014.07.103 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23996 SN - 2212-8271 N1 - Part of special issue: "12th Global Conference on Sustainable Manufacturing – Emerging Potentials" VL - 2015 IS - 26 SP - 412 EP - 417 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Bornschlegl, Martin A1 - Drechsel, Michael A1 - Kreitlein, Sven A1 - Bregulla, Markus A1 - Franke, Jörg ED - Kucera, Markus ED - Waas, Thomas T1 - A new Approach to increasing Energy Efficiency by utilizing Cyber-Physical Energy Systems T2 - Proceedings of the Eleventh International Workshop on Intelligent Solutions in Embedded Systems Y1 - 2013 UR - https://ieeexplore.ieee.org/document/6664947 SN - 978-3-00-042899-9 PB - IEEE CY - Piscataway ER -