TY - JOUR A1 - Bornschlegl, Martin A1 - Kreitlein, S. 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 - 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 - 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 - 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 - 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 - 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 SN - 978-989-758-555-5 SN - 2184-4321 VL - vol. 5: VISAPP SP - 305 EP - 314 PB - SciTePress CY - Setúbal 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 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Uzair Akmal, Muhammad 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 -