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A Study on Sensor Data Analysis and Product Defect Improvement for Smart Factory  

Hwang, Sewong (연세대학교 정보대학원)
Kim, Jonghyuk (연세대학교 정보대학원)
Hwangbo, Hyunwoo (연세대학교 정보대학원)
Publication Information
The Journal of Bigdata / v.3, no.1, 2018 , pp. 95-103 More about this Journal
Abstract
In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.
Keywords
Smart Factory; Industry 4.0; Sensor; Predictive Maintenance; Decision Tree;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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