• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2003년도 추계학술대회 논문요약집
• /
• pp.287-287
• /
• 2003

• 한국정밀공학회지
• /
• 제16권3호통권96호
• /
• pp.215-221
• /
• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• 한국정밀공학회지
• /
• 제17권4호
• /
• pp.135-141
• /
• 2000

The major role of temperature sensors in thermal error compensation system of machine tools is improving machining accuracy by supplying reliable temperature data on the machine structure. This paper presents a new method for fault diagnosis of temperature sensors and recovery of faulted data to establish the reliability of thermal error compensation system. The detection of fault and its location is based on the correlation coefficients among temperature data from the sensors. The multiple linear regression model which is prepared using complete normal data is also used fur the recovery of faulted data. The effectiveness of this method was tested by comparing the computer simulation results and measured data in a CNC machining center.

• 대한기계학회논문집A
• /
• 제24권10호
• /
• pp.2589-2596
• /
• 2000

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model, etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcomes limitation of accuracy in the linear regression model or the engineering judgment model. It shows that the fuzzy model has more better performance than linear regression model, though it has less number of thermal variables than the other. The fuzzy model does not need to have complex procedure such like multi-regression and to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Also, the fuzzy model can be applied to any machine, but it delivers greater accuracy and robustness.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1999년도 춘계학술대회 논문집
• /
• pp.75-80
• /
• 1999

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcome limitation of accuracy in the linear regression model or the engineering judgment model. And this model is compared with the engineering judgment model. It is not necessary complex process such like multi-regression analysis of the engineering judgment model. A fuzzy model does not need to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Like a regression model, this model can be applied to any machine, but it delivers greater accuracy and robustness.

• 항공우주시스템공학회지
• /
• 제17권4호
• /
• pp.18-27
• /
• 2023

본 논문에서는 열해석의 하중조건으로 레이저 열원을 설정하여 반사경의 구조-열-광학 성능 분석을 수행하였다. 레이저 열원 모델은 가우시안 빔을 바탕으로 반투명한 소재를 고려한 Beer-Lambert 법칙을 적용하여 하중조건으로 선정하였으며, 반사경만의 성능 분석을 위하여 기구부는 고려하지 않았다. 열변형해석을 수행하여 반사경 표면의 온도 변화로 인한 열응력과 열변형 데이터를 얻었다. 열변형에 의한 반사경 표면의 변위 데이터를 Zernike 다항식에 피팅하여 파면오차를 계산하였으며, 이를 통해 고에너지 레이저가 반사경으로 입사될 때 반사경의 광학 성능을 예측할 수 있었다.

• 한국생산제조학회지
• /
• 제8권4호
• /
• pp.19-28
• /
• 1999

In order to control thermal deformation of the machine origin of machine tools a empirical model and a compensation system have been developed, Prior to empirical modeling the volumetric error considering shape errors and joint errors of slides is formulated through the homogeneous transformation matrix (HTM) and kinematic chain. Simulation results of the HTM method show that the thermal error of the machine origin is more critical than position-dependent errors. In order to make a stable and effective software error compensation system the GMDH (Group Method of Data Handling) models are constructed to estimate the thermal deformation of the machine origin by measuring deformation data and temperature data. A test bar and gap sensors are used to measure the deformation data. In order to compensate the estimated error the work origin shift method is developed by implementing a digital I/O interface board between a CNC controller and an IBM PC. The method shifts the work origin as much as the amounts which are calculated by the pre-established thermal error model. The experiment results for a vertical machining center show that the thermal deformation of the machine origin is reduced within $\pm$5$mu extrm{m}$.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
• /
• pp.345-350
• /
• 1999

The effectiveness of software error compensation for thermally induced machine tool errors relies on the prediction accuracy of the pre-established thermal error models. The selection of optimal sensor locations is the most important in establishing these empirical models. In this paper, a methodology for the selection of optimal sensor locations is proposed to establish a robust linear model which is not subjected to collinearity. Correlation coefficient and time delay are used as thermal parameters for optimal sensor location. Firstly, thermal deformation and temperatures are measured with machine tools being excited by sinusoidal heat input. And then, after correlation coefficient and time delays are calculated from the measured data, the optimal sensor location is selected through hard c-means clustering and sequential selection method. The validity of the proposed methodology is verified through the estimation of thermal expansion along Z-axis by spindle rotation.

• 한국항공우주학회지
• /
• 제44권1호
• /
• pp.80-87
• /
• 2016

본 논문에서는 계절 및 주야의 온도변화를 고려한 관측위성의 열지향오차해석을 실시한다. 관측위성은 임무수행기간 동안 다채널의 관측센서를 이용해서 지구표면의 영상을 촬영한다. 그러나 주야 및 계절별로 최대 200도의 온도환경 차이가 발생하며 이로 인해 관측센서 및 별추적기의 시선벡터가 변화되고 정해진 목표지점의 영상촬영이 어렵다. 이런 문제를 사전예측하고 대응하기 위해서 열지향오차해석을 실시한다. 우선 궤도열환경해석으로부터 도출된 성긴 온도장 정보를 상세한 구조유한요소모델에 PAT기법을 이용해 보간하여 온도변화에 따른 열변형해석을 수행하였다. PAT로 보간된 온도분포의 정확도를 검증하였으며, 열변형해석결과로부터 열지향오차를 도출하였다.