Earticle

초록

영어

The use of heat exchangers in various applications such as chemical, air conditioning systems, fuel processing, and power industries is increasing. In order to improve the performance of the heat exchanger, the problem of bonding quality of the copper tube, which is a major member, is emerging. However, since the copper tube is in the form of a pipe, it is difficult to identify internal defects with external factors. In this study, a thermal imaging camera was used to develop and verify an algorithm for detecting defects in the brazing part, and in the process, the brazing performance characteristics were analyzed according to the electrode position, and finally, a learning model was developed and performance evaluation was performed. It was confirmed that the method of supplying heat to the base material and melting the filler metal through the heat transfer effect is more effective than supplying heat input to the filler metal in the bonding process of copper tubes through high-frequency induction heating brazing. Thermal image data was used to develop a defect discrimination model, and 80% of training data and 20% of test data were selected, and a neural network-based single-layer copper tube brazing defect discrimination model was developed through k-Flod cross-validation., the prediction accuracy of 95.2% was confirmed as a result of the error matrix analysis.

목차

ABSTRACT
1. 서론
2. 동 튜브 브레이징 접합 실험
2.1 실험방법 및 계획
2.2 열화상 데이터 측정방법
2.3 실험 결과
3. 동 튜브 브레이징 접합 불량 판별 모델
3.1 열화상 데이터 정규화
3.2 신경망을 활용한 불량 판별모델 개발
3.3 동 튜브 접합 불량 예측 및 신뢰성 검토
4. 결론
References

키워드

저자

• 이충우 [ Chung-Woo Lee | Korea Institute of Industrial Technology, Jeonbuk National University ]
• 김철우 [ Cheol-Woo Kim | Korea Institute of Industrial Technology ]
• 백경윤 [ Gyeng-Yun Baek | Gwangju University ]
• 김지선 [ Ji-Sun Kim | Korea Institute of Industrial Technology Senior Researcher ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620