• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.135-141
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• 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.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.754-761
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• 2013

Thermal displacement is an important issue in machine tool systems. During the last several decades, thermal error compensation technology has significantly reduced thermal distortion error; this success has been attributed to the development of a precise, robust thermal error model. A major advantage of using the thermal error model is instant compensation for the control variables during the modeling process. However, successful application of thermal error modeling requires correct determination of the temperature sensor placement. In this paper, a procedure for predicting thermal-mode-based thermal error is introduced. Based on this thermal analysis, temperature sensors were positioned for multiple heat-source models. The performance of the sensors based on thermal-mode error analysis, was compared with conventional methods through simulation and experiments, for the case of a slide table in a transient state. Our results show that for predicting thermal error the proposed thermal model is more accurate than the conventional model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.187-192
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• 1993

Thermal deformation causes large amount of machine tool errors. In order to compensate for thermal and geometric errors of the machine tool an off-line geometric adaptive control (GAC) scheme was developed. THe GAC method was realized by using a measuring plate made of precision spheres. Error vectors and volumetric errors were measured by the measuring plate. Error compensation models were obtained from error vectors and a kinematic chain of machine tools. Reliability of the GAC system of thermal and geometric errors were confrimed by large amount of experiments.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.4
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• pp.117-122
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• 2000

This paper presents an indirect compensation of thermal errors during machining , in which thermal error is modelled as a linear regression of temperatures measured at 4 specified positions. In this regression model, weighting coefficients of the measured temperatures were estimated by using the least square method. The grinding test with compensation , after 4 -hour warning-up operation before the test, showed that the maximum machining error of the work pieces was reduced to 12${\mu}{\textrm}{m}$ while it measured by 28${\mu}{\textrm}{m}$ without compensation . Furthermore the standard deviation of machining errors was also reduced from 8${\mu}{\textrm}{m}$ to 2${\mu}{\textrm}{m}$.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.102-111
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• 2000

Methodology of volumetric error identification and compensation is presented to improve the accuracy of NC machine tools by using a reference artifact and a touch trigger probe. Homogeneous transformation matrix and kinematic chain are used for modeling the geo-metric and thermal errors of a three-axis vertical machining center. The reference artifact is designed and fabricated to identify the model parameters by machine tool metrology. Parameters in the error model are able to be identified and updated by direct measurement of the reference artifact on the machine tool under the actual conditions which include the thermal interactions of error sources. A volumetric error compensation system based on IBM/PC is linked with a FANUC CNC controller to compensate for the identified volumetric error in machining workspace.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.375-379
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• 1997

Thermal drift of the machine tool spindle due to temperature increase dominates the major source of the machine tool error. To compensate the thermal errors, software based error correction methods could be implemented. In th~s case, we need model to map the relationship between temperature and thermal distortion. Traditionally, two or three different methods have been trled: step increase of spindle speed, constant, random. The latter two methods are described in the document of ISOlDIS230-3. In this research, three different methods were verified through the experiments from the viewpoint of compensation of thermal distortion. Constant spindle speed turned out good enough for monitoring the behavior of the thermal drift and modeling the relationship between temperature and thermal distortion.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.185-192
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• 2017

Machine tool errors can be divided into geometric error, thermal deformation error, and machining error. In this study, the influence of each error on the total error and the relative size of each error are quantitatively analyzed in 2D machining. The thermal deformation error and the machining error caused a relatively large error compared to the geometric error, which is directly related to the machining accuracy. In order to eliminate the error factors, the possibility of error compensation was examined by analyzing the measured error profile shape. As a result, about 40 ~ 50% error compensation was achieved for each error factor. Through this study, it is possible to construct a basic data base on machining, and it is expected that it will be able to compensate the machining error from the viewpoint of users.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.502-508
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• 2012

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.399-404
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• 2002

Improvement of measuring accuracy is an essential part of quality control manufacturing process. The OMM is less than the CMM in measure accuracy but the OMM system is more efficient, easier to use than other measurement system. About 40~70% of the machine tool errors are induced by the thermal errors. Therefore, a key requirement for improving the measuring accuracy is to reduce the geometric and thermal errors. Thermal errors are measured by a ball bar system and predicted by the thermal error modeling. Furthermore, using the pre-defined thermal error map approach compensates the geometric accuracy of the OMM. Appropriate experiments are performed using ball-bar system, temperature measuring devices and touch-type probe. Compensated results are compared with those obtained using CMM to verify the proposed methods.