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http://dx.doi.org/10.15207/JKCS.2018.9.3.053

Sequence Mining based Manufacturing Process using Decision Model in Cognitive Factory  

Kim, Joo-Chang (Division of Computer Science and Engineering, Kyonggi University)
Jung, Hoill (Department of Computer.Software Engineering, Wonkwang University)
Yoo, Hyun (Department of Computer Information Engineering, Sangji University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.3, 2018 , pp. 53-59 More about this Journal
Abstract
In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.
Keywords
Data Mining; Production; Decision Model; Decision Tree; Entropy;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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