Dr.-Ing.

Kuhn, Thomas

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  • Publication
    Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning
    ( 2023)
    Müller-Zhang, Zai
    ;
    Kuhn, Thomas
    ;
    Oliveira Antonino de Assis, Pablo
    Production flow is becoming increasingly complex since manufacturers must react quickly to changing markets demands and diverse customer requirements. In order to ensure production efficiency, it is essential to have an adequate scheduling system capable of managing diverse process flows and handling unforseen changes. In this paper, we present an approach leveraging Digital Twins (DTs) and Deep-Q-Learning to perform integrated process planning and scheduling for service-based production. DTs of production assets provide live information about their physical entities for our approach to perform live decision-making based on the current operation conditions. We use Deep-Q-Learning which is a deep Reinforcement Learning (RL) algorithm to perform integrated process planning and scheduling. We present two RL-designs that deal with different situations of live decision-making. We have evaluated the learning efficiency and scalability of the RL-designs on a virtual aluminum cold rolling mill developed by the SMS Group,1 in the context of the BaSys 4.2 project.2 The results show that the first RL-design is suitable for deriving schedules for individualized production with small lots where process plans must be re-calculated frequently, while the second RL-design is optimal for production with large job quantities where jobs arrive continuously.
  • Publication
    A Digital Twin-based Approach Performing Integrated Process Planning and Scheduling for Service-based Production
    ( 2022)
    Müller-Zhang, Zai
    ;
    Kuhn, Thomas
    Nowadays, the automation industry is undergoing many changes, manufactures must react to fast changing market demands and more individual customer requirements. Recept-based, Service-Oriented Architectures enable the efficient adaption of production processes to new operating conditions. However, to ensure production performance, service-oriented production should also be complemented by adequate scheduling approaches to guarantee critical performance factors. We present a Digital Twin-based approach that performs integrated process planning and scheduling for service-based production. For our approach, we have identified a common set of input data required for integrated process planning and scheduling. We use Deep-Q-Network, which is a deep Reinforcement Learning method, to derive near optimal schedules for production conditions described in Digital Twins. If an unforseen event happens during the production, our approach is able to adapt current schedules to the changed operating conditions. The case study shows that our approach is able to derive near optimal schedules for customized products and adapt its currents schedules for new orders with different production goals.
  • Publication
    Architecture Blueprint Enabling Distributed Digital Twins
    ( 2021)
    Schnicke, Frank
    ;
    Espen, Daniel
    ;
    Antonino, Pablo Oliveira
    ;
    Kuhn, Thomas
    Mass production today is optimized for large lot sizes, and changes to industrial production lines are effort-intense, time-consuming, and costly. The fourth industrial revolution, Industry 4.0 (I4.0), aims at reducing the effort needed for changes in industrial production lines. The key benefits of next-generation manufacturing systems are less downtimes and the production of small lot sizes down to lot size 1. I4.0 does not introduce a silver bullet technology, but requires a transformation of the system architecture of production systems. In the literature, however, there systematic guidance for designing manufacturing systems that address central I4.0 use cases like plug'n'produce and end-to-end communication is still missing, as are details on the infrastructure needed to enable I4.0 technologies such as Digital Twins. To contribute to filling this gap, this paper presents (i) a Digital Twin architecture blueprint driven by central I4.0 use cases and (ii) a prototypical open-source implementation of the architecture using the concept of the Asset Administration Shell.