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http://dx.doi.org/10.3745/KTSDE.2017.6.2.67

Design of Data Fusion and Data Processing Model According to Industrial Types  

Jeong, Min-Seung (부경대학교 컴퓨터공학과)
Jin, Seon-A ((주)나라시스템)
Cho, Woo-Hyun (부경대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.6, no.2, 2017 , pp. 67-76 More about this Journal
Abstract
In industrial site in various fields it will be generated in combination with large amounts of data have a correlation. It is able to collect a variety of data in types of industry process, but they are unable to integrate each other's association between each process. For the data of the existing industry, the set values of the molding condition table are input by the operator as an arbitrary value When a problem occurs in the work process. In this paper, design the fusion and analysis processing model of data collected for each industrial type, Prediction Case(Automobile Connect), a through for corporate earnings improvement and process manufacturing industries such as master data through standard molding condition table and the production history file comparison collected during the manufacturing process and reduced failure rate with a new molding condition table digitized by arbitrary value for worker, a new pattern analysis and reinterpreted for various malfunction factors and exceptions, increased productivity, process improvement, the cost savings. It can be designed in a variety of data analysis and model validation. In addition, to secure manufacturing process of objectivity, consistency and optimization by standard set values analyzed and verified and may be optimized to support the industry type, fits optimization(standard setting) techniques through various pattern types.
Keywords
Data Fusion; Data Mining; Data Processing Technology; Manufacturing Process; Device;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
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