산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제45권4호
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  • Pages.157-166
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  • 2022
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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주조공정 설비에 대한 실시간 모니터링을 통한 불량예측에 대한 연구

A Study on Defect Prediction through Real-time Monitoring of Die-Casting Process Equipment

  • 박철순 (창원대학교 산업시스템공학과) ;
  • 김흥섭 (창원대학교 산업시스템공학과)
  • Chulsoon, Park (Department of Industrial & Systems Engineering, Changwon National University) ;
  • Heungseob, Kim (Department of Industrial & Systems Engineering, Changwon National University)
  • 투고 : 2022.11.25
  • 심사 : 2022.12.13
  • 발행 : 2022.12.31
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초록

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

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과제정보

이 논문은 2021~2022년도 창원대학교 자율연구과제 연구비 지원으로 수행된 연구결과임

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