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공압식 소재물림 가이드부쉬 시스템의 설계 및 가공정도 평가
이재훈(Jae-Hoon Lee),이수민(Su-Min Yi),박성훈(Seonghun Park),이시복(Shibok Lee) 한국생산제조학회 2010 한국생산제조학회지 Vol.19 No.6
Generally, a fixed type guide-bush system is installed during machining miniature work-pieces with high precision in the multi-task CNC lathe. But a conventional guide-bush system does not provide a constant clamping force under the condition of varying work-piece diameters. It is important to maintain a constant clamping force for guaranteeing machining precision. This paper proposes a new guide-bush system with a pneumatic clamping device for the CNC Swiss-turn lathe to keep constant clamping force with changes in work-piece diameters. Through performance tests, new clamping system developed in the study showed better machining precision at the cost of a small increase in the temperature of the system than conventional systems due to an increase in the frictional heat and a change in the heat transfer route.
유전 알고리듬을 이용한 소형 고속스핀들 시스템의 바-피더 지지부의 위치 최적선정
이재훈(Jae-Hoon Lee),김무수(Musu Kim),박성훈(Seonghun Park),강재근(Jae-Keun Kang),이시복(Shibok Lee) Korean Society for Precision Engineering 2009 한국정밀공학회지 Vol.26 No.11
Since a long work piece influences the natural frequency of the entire system with a miniature high speed spindle, a bar-feeder is used for a long work piece to improve the vibration characteristics of a spindle system. Therefore, it is very important to design optimally support positions between a bar-feeder and a long work piece for a miniature high speed spindle system. The goal of the current paper is to present an optimization method for the design of support positions between a bar-feeder and a long work piece. This optimization method is effectively composed of the method of design of experiment (DOE), the artificial neural network (ANN) and the genetic algorithm (GA). First, finite element models which include a high speed spindle, a long work piece and the support conditions of a bar-feeder were generated from the orthogonal array of the DOE method, and then the results of natural vibration analysis using FEM were provided for the learning inputs of the neural network. Finally, the design of bar-feeder support positions was optimized by the genetic algorithm method using the neural network approximations.