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스마트 공장에서 의사결정 모델을 이용한 순차 마이닝 기반 제조공정

Sequence Mining based Manufacturing Process using Decision Model in Cognitive Factory

  • 김주창 (경기대학교 컴퓨터공학부) ;
  • 정호일 (원광대학교 컴퓨터.소프트웨어공학과) ;
  • 유현 (상지대학교 컴퓨터정보공학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • 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)
  • 투고 : 2018.01.12
  • 심사 : 2018.03.20
  • 발행 : 2018.03.28

초록

본 논문에서는 스마트 공장에서 의사결정 모델을 이용한 순차 마이닝 기반 제조공정을 제안한다. 제안하는 모델은 소규모의 제조공정에서 순차 마이닝 의사결정 모델을 적용하여 제조 효율을 높이는 방법이다. 제조 단계 중 제품 제조 과정에서 나타나는 데이터를 입력 변수들로 구성하고, 시간당 제조량과 불량률을 출력 변수로 구성한다. t-검정을 통해 유의수준이 높은 변수만을 사용하여 GSP 알고리즘과 REPTree 알고리즘을 이용한 규칙과 모델을 생성한다. 의미있는 순차 규칙과 의사결정 모델은 정확도, 민감도, 특이성, 예측도를 통해 유의미함을 확인한다. 결과적으로, 실제 제조에 적용한 결과 불량률은 0.38%가 개선되었고, 시간당 제조량은 평균 1.89/h 증가되었다. 이는 소규모 제조 공정에서 데이터 마이닝 분석을 통한 제조 효율을 높이기 위한 의미있는 결과를 나타낸다.

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.

키워드

참고문헌

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