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Cognitive Production Systems: A Mapping Study

: Ghofrani, Javad; Deutschmann, Bastian; Divband Soorati, Mohammad; Reichelt, Dirk; Ihlenfeldt, Steffen

Volltext ()

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-; University of Warwick:
18th IEEE International Conference on Industrial Informatics, INDIN 2020. Proceedings : Online Event. July 20th - July 23rd, 2020. WMG, Universiy of Warwick, UK
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-4965-3
ISBN: 978-1-7281-4964-6
ISBN: 978-1-7281-4963-9
International Conference on Industrial Informatics (INDIN) <18, 2020, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IWU ()
production system; robotics; cognitive; IIoT; Multi-Agent-System; Human-Machine Interaction

In order to guarantee the quality and the productivityof a production system in a competitive marketplace, it isimportant to be able anticipate the changes in specifications ofproducts and systems. The time limits in running productions, thecomplexity of manufacturing systems, and the diversification ofcomponents, are the challenges that human experts cannot handlewithout cognitive systems. Capabilities of cognitive Systems inobserving, learning, and predicting the behavior and the evolutionof the manufacturing systems make them special candidatesfor solving these problems. This mapping study provides aninsight into the application of cognitive systems in the domainof production. We categorize different approaches and estimatetheir progress. We also discuss the optimizations and persistingproblems and barriers. These representations can help in recognizingthe concrete problems of the field. According to theresults of our mapping study, Human-Machine Interaction andKnowledge Gaining/Sharing represents the largest categories ofthe domain. A gain in efficiency and maximized effectiveness canbe achieved as optimization. The most common problem is themissing or only difficult generalization of the presented concept.