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http://dx.doi.org/10.11627/jksie.2021.44.4.208

Design and Implementation of Integrated Production System for Large Aviation Parts  

Bae, Sungmoon (Department of Industrial and Systems Engineering, GyeongSang National Univeristy)
Bae, Hyojin (VMS Solutions)
Hong, Kum Suk (Korea Aerospace Industries, Ltd.)
Park, Chulsoon (Department of Industrial and Systems Engineering, Changwon National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.4, 2021 , pp. 208-219 More about this Journal
Abstract
In the era of the 4th industrial revolution driven by the convergence of ICT(information and communication technology) and manufacturing, research on smart factories is being actively conducted. In particular, the manufacturing industry prefers smart factories that autonomously connect and analyze data. For the efficient implementation of smart factories, it is essential to have an integrated production system that vertically integrates separately operated production equipment and heterogeneous S/W systems such as ERP, MES. In addition, it is necessary to double-verify production data by using automatic data collection technology so that the production process can be traced transparently. In this study, we want to show a case of data-centered integration of a large aircraft parts processing factory that requires high precision, takes a long time, and has the characteristics of processing large raw materials. For this, the components of the data-oriented integrated production system were identified and the connection structure between them was explained. And we would like to share the experience gained through the design and implementation case. The integrated production system proposed in this study integrates internal components based on data, which is expected to serve as a basis for SMEs to develop into an advanced stage, and traces materials with RFID technology.
Keywords
Integrated Production System; Smart Factory Implementation; Vertical Integration; Condition-Based Monitoring; Machine Learning;
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