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.

ABSTRACT
1. 서 론
2. 실험 방법
    2.1 스폿용접의 품질영향 인자
    2.2 동저항 계측 및 영상처리시스템의 구성
3. 실험 결과 및 고찰
    3.1 전극 수명예측 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. 결 론
References

• 김영곤(한국생산기술연구원) | Younggon Kim 교신
• 김재웅(한국생산기술연구원) | Jaewoong Kim