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사승윤,유은이,유봉환,Sa, Seung-Yun,Yu, Eun-Lee,Ryu, Bong-Hwan 대한기계학회 1997 大韓機械學會論文集A Vol.21 No.6
The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, digital image of time series sequence was acquired by taking advantage of optical technique. Mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR(auto regressive) model was selected for system model and fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, it was found that there was a system stability.
유은이,사승윤,유봉환,Yu, Eun-Lee,Sa, Seung-Yun,Ryu, Bong-Hwan 대한기계학회 1997 大韓機械學會論文集A Vol.21 No.9
Mordern industrial society pursues unmanned system and automation of manufacturing process. Abreast with this tendensy, production of goods which requires advanced accuracy is increasing as well. According to this, the work sensing time of dressing by monitoring and diagnosing the condition of grinding, which is th representative way in accurate manufacturing, is an important work to prevent serious damages which affect grinding process or products by wearing grinding wheel. Computer vision system was composed, so that grinding wheel surface was acquired by CCD camera and the change of cutting edge ratio was measured. Then we used automatic thresholding technique from histogram as a way of dividing grinding cutting edge from grinding surface. As a result, we are trying to approach unmanned system and automation by deciding more accurate time of dressing and by visualizing behavior of grinding wheel by making use of computer vision.