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Seo, Won Chan;Lee, Dong Uk
- Journal of Korean Society of Steel Construction
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- v.11 no.1 s.38
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- pp.1-11
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- 1999
The aim of this study is to construct a machine vision monitoring system for an automatic visual inspection of weld joint in steel structure. An image processing algorithm for a visual weld defects detection on weld bead is developed using the intensity image. An optic system for getting four intensity images was set as a fixed camera position and four different illumination directions. The input images were thresholded and segmented after a suitable preprocessing and the features of each region were defined and calculated. The features were used in the detection and the classification of the visual weld defects. It is confirmed that the developed algorithm can detect weld defects that could not be detected by previously developed techniques. The recognized results were evaluated and compared to expert inspectors' results.
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Yi, Myung-Su;Heo, Hee-Young;Park, Jung-Goo;Cho, Si-Hoon;Jang, Tae-Won
- Proceedings of the KWS Conference
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- 2009.11a
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- pp.5-5
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- 2009
용접에 의한 이면부 도장의 Burn damage는 관리하기 힘든 고질적인 품질문제이다. 도장면의 Burn damage 품질문제 발생시 재작업 등으로 인하여 많은 비용이 발생한다. 이런 경우 기존에 보유한 실험자료 및 적절한 이론자료 부족으로 인하여 일회적인 실험 혹은 해석적 방법을 사용하여 용접 이면부의 최고온도 등을 예측하고 회피할 수 있는 방법론을 제공하였다. 그러나 각 경우에 대해 해석 및 실험을 진행하게 되면 시간 및 비용에서 많은 문제점을 일으키게 된다. 따라서 체계적이고 효율적인 Burn damage 예측방법의 필요성이 대두되었다. 본 연구의 목적은 실험적/해석적 방법을 통해 용접이면부 최고 온도를 예측하고 이를 통해 일반화된 용접이면부 최고도달온도 예측식을 유도하는 것에 있다. 이를 위해 다양한 조건에서의 실험과 해석을 실시하였으며 이를 통해 일반화된 용접 이면부 최고도달온도 예측식을 유도하였다.
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Kim Gwan-Hyung;Kang Sung-In
- Journal of the Korea Institute of Information and Communication Engineering
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- v.9 no.6
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- pp.1379-1386
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- 2005
For efficient welding it is necessary to maintain stability of the welding process and control the shape of the welding bead. The welding quality can be controlled by monitoring important parameters, such as, the Arc Voltage, Welding Current and Welding Speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using fuzzy logic and neural networks for improving welding qualify and maintaining the desired weld bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and remove the welding detects.
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