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Rule-based integration of smart services using the manufacturing service bus

: Wieland, Matthias; Steimle, Frank; Mitschang, Bernhard; Lucke, Dominik; Einberger, Peter; Schel, Daniel; Luckert, Michael; Bauernhansl, Thomas


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computational Intelligence Society:
IEEE SmartWorld 2017. Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation : San Francisco Bay Area, California, USA, August 4-8, 2017; Conference proceedings
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-1591-1
ISBN: 978-1-5386-0435-9
ISBN: 978-1-5386-0434-2
Smart World Congress (SWC) <3, 2017, San Francisco/Calif.>
International Conference on Scalable Computing and Communications (ScalCom) <17, 2017, San Francisco/Calif.>
International Conference on Ubiquitous Intelligence Computing (UIC) <14, 2017, San Francisco/Calif.>
International Conference on Advanced and Trusted Computing (ATC) <14, 2017, San Francisco/Calif.>
Conference on Cloud and Big Data Computing (CBDCom) <2017, San Francisco/Calif.>
International Conference on Internet of People (IoP) <2017, San Francisco/Calif.>
Conference on Smart Cities Innovation (SCI) <2017, San Francisco/Calif.>
Conference Paper
Fraunhofer IPA ()
Industrie 4.0; Smart Factory; integration; manufacturing service bus

Factories have to adapt permanently to changing situations in order to stay competitive. Premise to achieve this objective is up-to-date information on all levels of a factory and during the product life cycle, so that men and machine can optimize their activities according to their tasks. One approach to implement this economically is the massive application of sensors and information and communication technologies (ICT) leading to a Smart Factory. This process and related applications are summarized under the term of the forth industrial revolution (Industrie 4.0). It demands a flexible and easy-to-use integration of assets on the shop floor with ICT systems. The contribution of this paper is an Manufacturing Integration Assistant (MIALinx) that enables all these steps. The steps range from the integration to the sensing and analyzing of the sensor data to the execution of required actions. Furthermore, MIALinx provides an abstract rule based approach for users to model the behavior of the system. The presented system is based on concepts and technologies of the Internet of Things and service-oriented middleware. The main users targeted with our system are small and mediumsized enterprises that do not have the expertise or the investment possibilities to invest in completely new Industrie 4.0 systems but rather use their existing production assets and enrich them to achieve Industrie 4.0 capability.