철강 연주공정에서 데이터마이닝을 이용한 품질제어 방법에 관한 연구

• 영문명: A Study on Quality Control Using Data Mining in Steel Continuous Casting Process
• 발행기관: 한국IT서비스학회
• 저자명: 김재경(Jae Kyeong Kim) 권택성(Taeck Sung Kwon) 최일영(Il Young Choi) 김혜경(Hyea Kyeong Kim) 김민용(Min-Yong Kim)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제10권 제3호, 113~126쪽, 전체 14쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2011.09.30

영문 초록

The smelting and the continuous casting of steel are important processes that determine the quality of steel products. Especially most of quality defects occur during solidification of the steel continuous casting process Although quality control techniques such as six sigma, SQC, and TQM can be applied to the continuous casting process for improving quality of steel products, these techniques don’t provide real-time analysis to identify the causes of defect occurrence. To solve problems, we have developed a detection model using decision tree which identified abnormal transactions to have a coarse grain structure. And we have compared the proposed model with models using neural network and logistic regression. Experiments on steel data showed that the performance of the proposed model was higher than those of neural network model and logistic regression model. Thus, we expect that the suggested model will be helpful to control the quality of steel products in real-time in the continuous casting process.

목차

Abstract

1. 서론

2. 이론적 배경

3. 연구방법 및 절차

4. 실험 및 평가

5. 결론 및 제언

참고문헌

저자소개

키워드

Data Mining Decision Tree Neural Network Logistic Regression Continuous Casting