• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.1
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• pp.1-7
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• 2006

A method of the volumetric error measurement and calibration of NC machine tools is studied using an artifact method. In this study, a hole-pate is designed and machined using stainless steel. We tested and applied the hole-plate artifact in a commercial CMM(Coordinate Measuring Machine), after calibration of the hole-plate using a precise CMM. It has been shown that not only the measurement of geometric error components but also the 2D length error calculation in a working volume is available using the hole-pate artifact method. The results of study can also be used in NC machine with touch probe as the same method in CMM.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.5
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• pp.7-13
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• 2010

The integrity and accuracy of the drilling hole are decided by positional error, diameter error, the roundness, the straightness, the cylindericity, size of the burr, the surface roundness and others. Among these parameters, positional error and diameter error have the most important parameters. The diameter error has been widely studied, but there has been little research done about the positional error due to the difficulty of measuring it. The measurement of hole location and diameter would be performed by CMM(Coordinate Measurement Machine). However, the usage of CMM requires much time and cost. In order to overcome the difficulties, we have developed a hole location and diameter error measuring device using machine vision. The developed measurement device attached to a CNC machine can determine hole quality quickly and easily.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2475-2482
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• 2002

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is compensated using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could compensated the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for compensation of the error due to hole eccentricity in hole-drilling method.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.412-418
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• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is corrected using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could corrected the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for correction of the error due to hole eccentricity in hole-drilling method.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.67-74
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• 1996

3차원좌표측정기(Coordinate Measuring Machine)의 공간오차(Volumetric error)의 측정을 위하여 홀-플레이트(Hole-Plate)를 이용하는 방법이 연구되었다. 티타늄 또는 세라믹으로 제작되는 홀-플레이트의 설계 예를 보였다. 홀-플레이트의 측정홀 숫자와 진원도(Roundness)의 영향이 연구되었으며, 또한 홀-플레이트의 설치시 발생하는 오차도 검토되었다. 3차원좌표측정기의 공간오차성분 모두를 별도로 측정하는 방법이 제안되었다. 홀-플레이트를 이용 2차원 및 3차원 공간의 길이 오차를 직접적으로 측정하는 방법도 소개되었다.

• Journal of Mechanical Science and Technology
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• v.16 no.11
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• pp.1359-1366
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• 2002

The measurement of residual stresses by the hole-drilling method has been used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, we obtained the magnitude of the error due to eccentricity of a hole through the finite element analysis. To predict the magnitude of the error due to eccentricity of a hole in the biaxial residual stress field, it could be learned through the back propagation neural network. The prediction results of the error using the trained neural network showed good agreement with FE analyzed results.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.956-963
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• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation loaming process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.8 s.179
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• pp.2059-2064
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• 2000

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, one of the source of error is due to the misalignment between the drilling hole and strain gage center. This paper presents a finite element analysis of the influence of such misalignment for the uniaxial residual stress field. The stress error increases proportionally to hole eccentricity. The correction equations which easily obtain the residual stress taking account of the hole eccentricity are derived. The stress error due to the hole eccentricity decreases by approximately one percent using this equations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.3
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• pp.436-444
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• 2002

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation learning process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.11
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• pp.1053-1058
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• 2007

Micro-EDM technology (or the manufacture of miniature parts is used to make a micro hole. Two electrode shaping methods, mechanical electrode grinding and WEDG technique, have been studied. In this study, an electrode shaping method by using previously machined hole is introduced in order to obtain an optimal hole-making condition. Key factors such as applied voltage, capacitance, feedrate, and hole-dimension have an influence on the fabricating error of electrode shaping, which are taper ratio of a hole, electrode form accuracy, and electrode surface. Therefore, we try to investigate the optimal fabricating of electrode shaping from various experiments. Results from experiments, it was able to minimize the electrode fabricating error as voltage increases, and also applied feedrate and capacitance decreases.