http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
오차행렬을 이용한 5축 공작기계의 오차보정모델 생성 및 실험적 검증
권성환(Sung Hwan Kweon),이동목(Dong Mok Lee),양승한(Seung Han Yang) Korean Society for Precision Engineering 2013 한국정밀공학회지 Vol.30 No.5
This paper proposes a new model to compensate for errors of a five-axis machine tool. A matrix with error components, that is, an error matrix, is separated from the error synthesis model of a five-axis machine tool. Based on the kinematics and inversion of the error matrix which can be obtained not by using a numerical method, an error compensation model is established and used to calculate compensation values of joint variables. The proposed compensation model does not need numerical methods to find the compensation values from the error compensation model, which includes nonlinear equations. An experiment using a double ball-bar is implemented to verify the proposed model.
Servo Mismatch Estimation of Miniaturized Machine Tools Using Laser Tracker
이훈희(Hoon Hee Lee),권성환(Sung Hwan Kweon),손진관(Jin Gwan Son),양승한(Seung Han Yang) Korean Society for Precision Engineering 2016 한국정밀공학회지 Vol.33 No.8
Servo mismatch, which affects positioning accuracy of multi-axis machine tools, is usually estimated via the circular test. However, due to mechanical restrictions in measuring instruments, the circular test using a double ball-bar is difficult to apply in miniaturized or super-large sized machine tools. Laser trackers are widely used to measure the form accuracy of parts and the positioning accuracy of driving systems. In this paper, a technique for the servo mismatch estimation of multi-axis machine tools is proposed via the circular test using a laser tracker. To verify the proposed technique, experiments using a double ball-bar and laser tracker are conducted in a 3-axis machine tool. The difference in the evaluation results is 0.05 msec. The servo mismatch for the miniaturized machine tool is also evaluated using the proposed technique.
PSD 를 이용한 초정밀소형공작기계의 기하학적 오차 측정
권설령(Seol Ryung Kwon),권성환(Sung Hwan Kweon),양승한(Seung Han Yang) 대한기계학회 2011 大韓機械學會論文集A Vol.35 No.1
초정밀 소형공작기계는 초정밀가공분야에서 마이크로/메조 스케일 가공품의 정밀제조기술의 핵심으로 개발되어 왔다. 소형초정밀기계의 기하학적 오차는 가공품의 품질에 큰 영향을 미치기 때문에 반드시 분석 및 보정되어야 한다. 기존 소형공작기계의 기하학적 오차는 주로 레이저 간섭계로 측정되었으나 한번의 설치로 모든 기하학적 오차를 측정할 수 없고 까다로운 절차를 따라야 한다. 그 대안으로써 PSD 로 구성된 측정시스템이 개발되었으나 측정가능거리가 PSD 의 유효영역에 한정되었다. 본 논문에서는 측정가능거리를 확장시키고 설치오차를 최소화하여 6-자유도 기하학적 오차를 측정하는 시스템을 제안하고 민감도 해석과 실험을 통하여 이 측정 시스템의 정확도를 증명하였다. Ultra-precision miniaturized machine tools essential for manufacturing accurate machine components in micro/meso-scale have been developed. To realize high accuracy using mMTs, geometric errors, which are considered as the main sources of inaccuracy should be identified and compensated. The conventional systems for measuring geometric errors, such as a laser interferometer, can measure only one geometric error in a single setup and they involve complicated measurement procedures. A measurement system using PSDs is a promising alternative but the measurable range of such systems is limited to the active range of the PSDs. The proposed measurement system using PSDs can overcome the limit of small measurable range. Further, the mounting errors that could occur during set-up process can be avoided. In this paper, an algorithm corresponding to the system was analyzed and experiments were carried out.
CNN-based Human Recognition and Extended Kalman Filter-based Position Tracking Using 360° LiDAR
정기범(Kibum Jung),권성환(Sung Hwan Kweon),전병국(Martin Byung-Guk Jun),정영훈(Young Hun Jeong),양승한(Seung-Han Yang) Korean Society for Precision Engineering 2022 한국정밀공학회지 Vol.39 No.8
The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.