http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
신경망을 이용한 유연디스크 디버링가공 아크형상구간 인자예측에 관한 연구
유송민,Yoo, Song-Min 한국생산제조학회 2009 한국생산제조학회지 Vol.25 No.2
Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.
신경망을 이용한 유연디스크 가공 종단부 품질예측에 관한 연구
유송민,Yoo, Song-Min 한국생산제조학회 2010 한국생산제조학회지 Vol.26 No.1
Even though a flexible disk grinding process was often applied to enhance the product quality, it produced non-flat zone in the beginning and the exit (end) area. Since latter area is susceptible to poor product quality with burn mark, careful analysis is required to cope with such degradation. The flexible disk grinding exit stage was analyzed for workpiece length, wheel speed, depth of cut and feed. The exit stage qualities defined as exit stage ratio and exit stage angle or slope was characterized. A neural network application results reveled that exit stage characteristics was predicted more accurately without workpiece dimension with minimum error of 1.3%.
신경망을 이용한 유연디스크 디버링가공 아크형상구간 인자예측에 관한 연구
유송민(Song Min Yoo) 한국생산제조학회 2009 한국생산제조학회지 Vol.18 No.6
Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.
유송민(Song Min Yoo) 한국생산제조학회 2007 한국생산제조학회지 Vol.16 No.6
Inherent dynamic interaction between flexible disk and workpiece creates partially non-flat surface profile. A flat zone was defined using minimum depth of engagement. Several key parameters were defined to explain the characteristics of the zone. Process conditions including disk rotation speed, initial depth of cut and feed speed were varied to produce product profile database. Correlation between key factors was examined to find the characteristic dependencies. Trends of key parameters were displayed and explained. Higher flat zone ratio was observed for lower depth of cut and higher disk rotation speed. Ratio of minimum depth of cut against target depth of cut increased for higher feed speed and disk rotation speed but was insensitive to the depth of cut variation. The process transition was visualized by continuously displaying instantaneous orientation of the deflected disk and the location of key parameters were clearly marked for comparison.
신경망을 이용한 유연성 디스크 연삭가공공정 인자 예측에 관한 연구
유송민(Song Min Yoo) 한국생산제조학회 2008 한국생산제조학회지 Vol.17 No.5
In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.