주조공정 설비에 대한 실시간 모니터링을 통한 불량예측에 대한 연구

• 영문명: A Study on Defect Prediction through Real-time Monitoring of Die-Casting Process Equipment
• 발행기관: 한국산업경영시스템학회
• 저자명: 박철순 김흥섭
• 간행물 정보: 『산업경영시스템학회지』제45권 제4호, 157~166쪽, 전체 10쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2022.12.31

영문 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.

목차

1. 서 론

2. 데이터 전처리

3. 머신러닝을 이용한 예측모델 개발

4. 모니터링 시스템 구축

5. 결론 및 추후연구

키워드

Condition based Monitoring Machine Learning Predictive Maintenance Sensor Data Real-time Data Acquisition and Analysis