• Tunnel and Underground Space
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• v.24 no.5
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• pp.386-394
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• 2014

Drilling accuracy is known to be one of the most important factors determining blasting efficiency in mining by blast operation. Therefore analysing the causes of drilling error and preparing a countermeasure for minimizing drilling error are very important for blasting efficiency and safety. In this study, causes of drilling error are analyzed with dividing them into controllable factors and uncontrollable factors, and relationship between each cause is also comprehended through field measurement and AHP analysis. Finally, effective measures to help lower the drilling error are proposed with the results from weighting analysis for each factor.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1555-1558
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• 2005

In this study, we measured cylindricity and Runout of the air bearing spindle, and tested PCB(printed circuit boards) drilling with 0.4mm micro drill at 90,000rpm and 110,000rpm in order to obtain drilling hole error. Results are as follows; The air bearing spindle's Runout was not so high within $10\mu{m}$ from 20,000rpm to 80,000rpm but it grew after 80,000rpm. Drilling hole size error was 9% at 80,000rpm and 12% at 110,000rpm because of spindle's Run out. Drilled hole shape falsified more 110,000rpm than 90,000rpm.

• 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.

• 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.

• 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.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• Journal of Mechanical Science and Technology
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• v.15 no.12
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• pp.1647-1654
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• 2001

The hole drilling method is widely used in measuring residual stress in surfaces. In this method, the inclination of holes is one of the sources of error. This paper presents a finite element analysis of the influence of inclined holes on the uniaxial residual stress field. The error in stress has been found to increase proportionally to the correct inclined angle of the hole. The correction equations by which one may easily obtain the residual stress, taking account of the inclined angle and direction, have been derived. The error of stress due to the inclined hole has been reduced to around 1% using the correction equations.