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신경망을 이용한 수치제어공작기계 진원도 예측에 대한 연구
이홍국(H. G. Lee),유송민(S. M. Yoo),박환서(H. S. Park),노대호(D. H. Roh) 한국생산제조학회 2006 한국공작기계학회 춘계학술대회논문집 Vol.2006 No.-
This study is to predict the roundness of Numerical Control Machining Tool and set up a displacement error database so that helps the operator to choose the right machining conditions to produce a product within the given error limits. Principles of learning of neural network is mostly based in Backpropagation Theory. From this study. the base was set to setup the database to produce precisely machined product by predicting the rate of error in the fabrication facility which does not have the environment to analyze it.
박환서,이홍국,노대호,박용배,유송민 한국공작기계학회 2008 한국공작기계학회 춘계학술대회논문집 Vol.2008 No.-
The purpose of this study is to enhance the gear tooth machining process outcome by identifying the correlation between the product qualityand machining condition. A surface profilometer exclusively used for tracing and extracting key characteristic parameters from the gear tooth surface profile is facilitated. In order to replicate the professional gear tooth machining process, a cylindrical grinding machine was used. Separate approaches using two types of surface roughness measurement machines are introduced to characterize the product surface. Tentative results revealed that chatter could be observed in higher cutting speed or in poor gripper condition. A neural network tool was used to define optimal process condition area for best process output quality.