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원종률(Jong Youl Won),최영재(Young Jae Choi),이석우(Suk Woo Lee),최헌종(Hon Zong Choi) 대한기계학회 2003 대한기계학회 춘추학술대회 Vol.2003 No.11
Surface and edge finishing processes are important technological operations of in parts machining. Quality of the<br/> finished parts directly affect the performance of the whole product. Especially, edge quality, which depends on burr<br/> control, is extremely important. Burrs are undesirable projections of the material beyond the edge of the workpiece. A<br/> number of deburring processes have been developed such as barreling, brushing, chemical methods etc. But, there are<br/> only few publications in the area of applying ultrasonics to deburring. When ultrasonic vibration propagates in the<br/> liquid medium, a large number of bubbles are formed. These bubbles generate an extremely strong force, which can be<br/> used to remove burrs. Cavitation is used as a term to describe the erosion of parts caused by the action of cavities in<br/> liquid. The object of this study is to analyze the effects of ultrasonic cavitation in the deburring process. For this<br/> purpose, we introduce a new ultrasonic cavitation method, which efficiently removes the burrs. Experimental<br/> parameters to verify the deburring effects of ultrasonic cavitations are ultrasonic power, amplitude, distant of the<br/> transducer from the workpiece, deburring time and abrasive. It has been shown that deburring with ultrasonic cavitation<br/> in water is effective to burrs.
고속가공에서 2중 신경망을 이용한 표면거칠기 예측과 가공DB 구축 효율화 방안
원종률(Jong-Youl Won),남성호(Sung-Ho Nam),유송민(Song-Min Yoo),이석우(Seok-Woo Lee),최헌종(Hon-Zong Choi) 한국생산제조학회 2004 한국생산제조시스템학회 학술발표대회 논문집 Vol.2004 No.10
In this paper, a double artificial neural network (ANN) approach and the efficient machining database building scheme are presented for the prediction of surface roughness in high-speed machining. In this approach, 4 machining parameters and used for the prediction of cutting force components, and the combinations of 4 parameters and the predicted cutting force components are finally used for the prediction of surface roughness. The experimental results comparing the these results with the predicted values using simple 4 input nodes have been also investigated to verify the effectiveness of the proposed approach.
고속가공에서 2중 신경망을 이용한 표면거칠기 예측과 가공DB 구축 효율화 방안
원종률,남성호,유송민,이석우,최헌종 한국공작기계학회 2004 한국공작기계학회 추계학술대회논문집 Vol.2004 No.-
In this paper, a double artificial neural network (ANN) approach and the efficient machining database building scheme are presented for the prediction of surface roughness in high-speed machining. In this approach, 4 machining parameters and used for the prediction of cutting force components, and the combinations of 4 parameters and the predicted cutting force components are finally used for the prediction of surface roughness. The experimental results comparing the these results with the predicted values using simple 4 input nodes have been also investigated to verify the effectiveness of the proposed approach.