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볼 엔드밀 가공 시 Z축 경사각에 따른 표면 거칠기에 관한연구
박종복(Jong-Bog Park),박주석(Ju-Seog Park) 산업기술교육훈련학회 2009 산업기술연구논문지 (JITR) Vol.14 No.3
Today s manufacturing industry demand better quality and high productivity. so, The aim of this paper is to get a comprehensive understanding of it s machinability properties, to investigate the relationship between variation of angle Z-axis ball endmill and variation of ball endmill diameter as well as surface roughness. The mathematical equations developed to predict the each condition and surface roughness is compared with the experiment result. As a result, we arrived at the following conclusions. As to the surface roughness, 10 degree and 20 degree gap was better than 0 degree in the ball endmill process the Z-axis angle, however over the angle of 45 degree has the tendency that the surface roughness becomes inferior by the blade edge affect. In the same pitch, the ball endmill diameter variation could know rather than the thing in which a diameter is small that a diameter is big that the surface roughness was excellent. As to 2 feed value variation, the surface roughness of 500mm/min was better than 1000mm/min, however it seems to receive an affect in variation of angle Z-axis and ball endmill diameter variation.
신경회로망을 이용한 실시간 용접비드 자동제어에 관한 연구
박주석(Ju-Seog Park),박종복(Jong-Bog Park) 산업기술교육훈련학회 2011 산업기술연구논문지 (JITR) Vol.16 No.1
Recently, several models to control weld quality, productivity, microstructure and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead geometry as well as apply for the on-line quality automatic control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, The system will extend to other welding process and rule-based expert system which can be incorporated with to integrate an optimized system for the robotic welding system.