TY - CHAP A1 - Mayer, Sebastian A1 - Arnet, Christian A1 - Gankin, Dennis A1 - Endisch, Christian T1 - Standardized framework for evaluating centralized and decentralized control systems in modular assembly systems T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) UR - https://doi.org/10.1109/SMC.2019.8914314 KW - virtual commissioning KW - cyber-physical production system KW - digital manufacturing KW - scheduling KW - production control KW - multi-agent system KW - modular assembly system Y1 - 2019 UR - https://doi.org/10.1109/SMC.2019.8914314 SN - 978-1-7281-4569-3 SP - 113 EP - 119 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Mayer, Sebastian A1 - Gankin, Dennis A1 - Arnet, Christian A1 - Endisch, Christian T1 - Adaptive production control with negotiating agents in modular assembly systems T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) UR - https://doi.org/10.1109/SMC.2019.8914498 KW - multi-agent system KW - negotiation-based coordination KW - intelligent manufacturing KW - production scheduling KW - production control KW - modular assembly system Y1 - 2019 UR - https://doi.org/10.1109/SMC.2019.8914498 SN - 978-1-7281-4569-3 SP - 120 EP - 127 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Mayer, Sebastian A1 - Höhme, Nikolas A1 - Gankin, Dennis A1 - Endisch, Christian T1 - Adaptive production control in a modular assembly system BT - towards an agent-based approach T2 - 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) UR - https://doi.org/10.1109/INDIN41052.2019.8972152 KW - intelligent manufacturing KW - scheduling KW - production control KW - multi-agent system KW - modular assembly system KW - automated guided vehicles Y1 - 2020 UR - https://doi.org/10.1109/INDIN41052.2019.8972152 SN - 978-1-7281-2927-3 SN - 2378-363X SP - 45 EP - 52 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Mayer, Sebastian A1 - Endisch, Christian T1 - Adaptive production control in a modular assembly system based on partial look-ahead scheduling T2 - 2019 IEEE International Conference on Mechatronics (ICM) UR - https://doi.org/10.1109/ICMECH.2019.8722904 KW - scheduling KW - production control KW - production planning KW - modular assembly system KW - automated guided vehicles KW - cyber-physical systems KW - intelligent manufacturing KW - genetic algorithm Y1 - 2019 UR - https://doi.org/10.1109/ICMECH.2019.8722904 SN - 978-1-5386-6959-4 SP - 293 EP - 300 PB - IEEE CY - Piscataway (NJ) 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 - 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 - Mayer, Sebastian A1 - Classen, Tobias A1 - Endisch, Christian T1 - Modular production control using deep reinforcement learning: proximal policy optimization JF - Journal of Intelligent Manufacturing N2 - EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design. UR - https://doi.org/10.1007/s10845-021-01778-z KW - modular production KW - production control KW - production scheduling KW - deep reinforcement learning KW - proximal policy optimization KW - automotive industry Y1 - 2021 UR - https://doi.org/10.1007/s10845-021-01778-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13092 SN - 1572-8145 VL - 32 IS - 8 SP - 2335 EP - 2351 PB - Springer Nature CY - Cham ER -