대한용접접합학회:학술대회논문집 (Proceedings of the KWS Conference)
- 대한용접접합학회 2000년도 특별강연 및 춘계학술발표대회 개요집
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- Pages.303-306
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- 2000
동저항 패턴 인식 및 실시간 품질 평가
Pattern Recognition of Dynamic Resistance and Real Time Quality Estimation
초록
Quality estimation of the weld has been one of the important issues in RSW which is a main process of the sheep metal fabrication in auto-body industry, It was well known that among the various welding process variables, dynamic resistance has a close relation with nugget formation. With this variable, it is possible to estimate the weld quality in real time. In this study, a new quality estimation algorithm is developed with the primary dynamic resistance measured at welding machine timer. For this, feature recognition method of Hopfield neural network is used. Primary resistance patterns are vectorized and classified with five patterns. The network trained by these patterns recognizes the dynamic resistance pattern and estimates the weld quality Because the process variable monitored at the primary circuit is used, it is possible to apply this system to real time application without any consideration of electrode wear or shunt effect.
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