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공남웅,박유진,최용준 제어로봇시스템학회 2021 제어로봇시스템학회 국내학술대회 논문집 Vol.2021 No.6
In recent years, as the steel technology has been greatly developed, the quality demands of customers for hot-rolled products are increasing. Especially, the shape qualities for hot-rolled products are flatness, camber, telescope, and inner diameter variance. In the past, these qualities were measured manually by the inspectors. Accurate quality data is required to improve the quality of the winding coil. This paper proposes the technology to quantitatively measure the telescope, which is the representative shape quality of the winding coil, using 2D camera and line laser.
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열연 공정에서의 영상을 이용한 캠버 및 최적 절단선 검출 알고리즘
공남웅(Nam Woong Kong),문정혜(Jung Hye Moon),박부견(PooGyeon Park) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.10
This paper presents the vision-based camber and optimal cutting line detection algorithm for hot-rolling process. It is important to measure the camber of head and tail part of strips because many problems are caused by the camber in the hot-rolling process. The hot-rolling process has time constraints. The camber detection algorithm of head and tail parts requires fast and less complex for satisfying time constraints. The proposed algorithm consists of two parts: measurement of the camber in the head and tail part of strips and decision part of the optimal cutting line of hot-rolled strip. First, we obtain the camber value of the strip from the difference between the real center line and the center line of head, tail part. Second, the head and tail part of strips isn't suitable for strips connections. Therefore, the cutting process is needed in the hot-rolling process. The optimal cutting line is determined by the head and tail images obtained from cameras. The algorithm is applied into the vision system with two area cameras, Matrox image processing board and host PC for verification.
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공남웅(NamWoong Kong),박인석(In Seok Park),김민수(Min Su Kim),최용준(Yong-Joon Choi),박부견(Poogyeon Park),윤종필(Jong Pil Yun) 대한전자공학회 2018 대한전자공학회 학술대회 Vol.2018 No.6
In order to develop the precise camber simulator, it is necessary to predict the value of roll force. Thus, this paper considers the problem of prediction of the roll force in hot rolling mill process via deep nerual network. The neural network is constructed as 4 hidden layers. The training data is obtained from the real hot rolling mill process: 20832 samples. Since, in general, the simulator and controller are designed by using MATLAB, the artificial neural network model is designed by using MATLAB.
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