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[ 1 ] Gönnheimer, P.; Karle, A.; Mohr, L. & Fleischer, J. (2021), „Comprehensive Machine Data Acquisition through Intelligent Parameter Identification and Assignment“. Procedia CIRP, Elsevier, S. 720-725. 10.1016/j.procir.2021.11.121
In today’s highly competitive manufacturing environment, process data monitoring continues to be of high priority, but often relies on modern communication interfaces being provided by PLC manufacturers. This paper proposes an alternative approach in which data is acquired automatically from various PLC models through available interfaces. Multiple Machine Learning algorithms are incorporated to identify machine parameters, which are then assigned to appropriate machine information models. All functionalities can be provided by a dedicated hardware module or as software modules on IPCs. The proposed approach can be integrated into existing Industry 4.0 efforts to accelerate digitalization in challenging environments.

[ 2 ] Benfer, M.; Peukert, S. & Lanza, G. (2021), „Operations Research in International Manufacturing Networks“ in Global Manufacturing Management, Hrsg. Friedli, T.; Lanza, G. & Remling, D., Springer, Cham, S. 219-231. ISBN/ISSN: 978-3-030-72740-6
This chapter explores the use of operations research methods in production network managment. Challenges like increasing product variety, the fragmentation of value streams, and other have made the use of OR methods necessary. The chapter provides an overview of the commonly used methods in this context and discusses typical applications in production network managment. Furthermore, the benefits of applying OR methods in production networks like increased understanding and the ability to solve complex problem with conflicting goals are explored. The limitations of these methods are recognized and elaborated as well. Finally, a brief outlook on future developments in this field is given.

[ 3 ] Benfer, M.; Peukert, S. & Lanza, G. (2021), „A Framework for Digital Twins for Production Network Management“. Procedia CIRP, Elsevier, S. 1269-1274. 10.1016/j.procir.2021.11.213
The dynamic and highly complex task of production network management requires decision support through quantitative models. In the industrial praxis, these models are specifically designed and implemented for particular management decisions, requiring significant one-time effort for model creation. This contribution utilizes the digital twin concept to facilitate production network models that are continuously synchronized with the examined production network to support several different management decisions. The approach structures data from existing information systems as a synchronized generic base model, which is used to create problem-specific executable models, thereby saving costs through repeated model use and quicker decision making.

[ 4 ] Benfer, M.; Verhaelen, B.; Peukert, S. & Lanza, G. (2021), „Resilience Measures in Global Production Networks: A Literature Review and Conceptual Framework“, Die Unternehmung, Band 75, Nr. 4, 10.5771/0042-059X-2021-4-491
The resilience of globally interconnected production networks to changes in their environment and internal disruptions is an important research object in business and production science. While many different measures to improve resilience have been suggested in academic literature, effectively choosing measures to improve production networks remains challenging. This contribution analyzes measures to improve the resilience of production networks proposed in the existing body of literature. The most commonly suggested measures are discussed in detail. These measures are structured in a conceptual framework to enable increased clarity regarding the mechanics by which measures improve resilience and to choose specific measures for a production network.

[ 5 ] Mayer, D.; Wurba, A.; Bold, B.; Bernecker, J.; Smith, A. & Fleischer, J. (2021), „Investigation of the Mechanical Behavior of Electrodes after Calendering and Its Influence on Singulation and Cell Performance“, processes, Band 9, Nr. 11, 10.3390/pr9112009
Battery cell production is a complex process chain with interlinked manufacturing processes. Calendering in particular has an enormous influence on the subsequent manufacturing steps and final cell performance. However, the effects on the mechanical properties of the electrode, in particular, have been insufficiently investigated. For this reason, the impact of different densification rates during calendering on the electrochemical cell performance of NMC811 (LiNi0.8Mn0.1Co0.1O2) half-cells are investigated to identify the relevant calendering parameters. Based on this investigation, an experimental design has been derived. Electrode elongations after calendering in and orthogonal to the running direction of the NMC811 cathode are investigated in comparison with a hard carbon anode after calendering. Elongations orthogonal to the machine direction are observed to have no major dependencies on the compaction rate during calendering. In the machine direction, however, significant elongation occurs as a dependency of the compaction rate for both the hard carbon anode and the NMC811. In addition, the geometric shape of the NMC811 electrodes after separation into individual sheets is investigated with regard to different compaction rates during calendering. It is shown that the corrugations that occur during calendering are propagated into the single electrode, depending on the compaction rate.

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