TY - JOUR A1 - Vogt, Anna A1 - Schmidt, Philipp Heiner A1 - Mayer, Sebastian A1 - Stark, Rainer T1 - Production in the loop: the interoperability of digital twins of the product and the production system JF - Procedia CIRP N2 - The Internet of Things (IoT) era facilitates new possibilities at the interface between the lifecycle of the product and the manufacturing disciplines allowing to add value and meet growing legal requirements. This work presents use cases from the automobile industry which are analyzed in order to derive requirements for data exchange. The analysis of the use cases shows clearly that we need to focus on the instance level for this purpose. This means that both, a specific unit of the product and the production system, must be considered. As a solution approach, a communication loop is proposed bringing the Digital Twin concept in a new field of usage. The key aspect of the proposed solution is the direct intercommunication between the Digital Twins of the product being produced as a Cyber-Physical System (CPS) and the production resources as Cyber-Physical Production Systems (CPPS). This solution enables the aggregation of production-related data within the products Digital Twin and therefore promotes maximal transparency throughout engineering disciplines, phases of the product lifecycle and the whole supply chain. UR - https://doi.org/10.1016/j.procir.2021.03.077 KW - digital twin KW - digital shadow KW - digital twin lifecycle KW - cyber-physical system KW - intelligent production systems KW - smart production Y1 - 2021 UR - https://doi.org/10.1016/j.procir.2021.03.077 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13120 SN - 2212-8271 VL - 2021 IS - 99 SP - 561 EP - 566 PB - Elsevier CY - Amsterdam ER - 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 - 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 -