Earticle

초록

영어

Spot welding is a representative process in automotive welding and the application of intelligent systems is accelerating. In particular, in the case of welding electrode management, the timing of electrode wear and dressing was determined by continuous spot welding evaluation, however there is concerned that errors in welding equipment or processes may work in a complex manner. In this study, a dynamic resistance waveform sensing and image measurement system that greatly affects the nugget formation, which is important to the quality of spot welding, was fabricated and used. Based on the experimental data of the galvanized steel sheet, an electrode life prediction algorithm for electrode wear was derived through CNN(Convolutional Neural Network) model of machine learning training.

목차

ABSTRACT
1. 서론
2. 실험 방법
2.1 스폿용접의 품질영향 인자
2.2 동저항 계측 및 영상처리시스템의 구성
3. 실험 결과 및 고찰
3.1 전극 수명 예측 기초 실험 수행
3.2 동저항 파형 및 전극이미지 분석 결과
3.3 기계학습모델 학습 훈련 결과
3.4 용접전극 상태 진단 알고리즘 도출
4. 결론
References

키워드

저자

• 김영곤 [ Young-Gon Kim | Member, Korea Institute of Industrial Technology ] Corresponding Author
• 김재웅 [ Jae-Woong Kim | Korea Institute of Industrial Technology ]

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