• Title/Summary/Keyword: Drilling hole error

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Compensation of the Error due to Hole Eccentricity of Hole-drilling Method in Uniaxile Residual Stress Field Using Neural Network (신경망 기법을 이용한 1축 잔류응력장에서 구멍뚫기법의 구멍편심 오차 보정)

  • Kim, Cheol;Yang, Won-Ho;Cho, Myoung-Rae
    • 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.

Correction of Error due to Hole Eccentricity in Hole-drilling Method Using Neural Network (신경망 기법을 이용한 구멍뚫기법에서의 구멍 편심오차 보정)

  • Kim, Cheol;Yang, Won-Ho;Cho, Myoung-Rae;Heo, Sung-Pil
    • 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.

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Influence of the Hole Eccentricity in Residual Stresses Measurement by the Hole-drilling Method (구멍뚫기법에 의한 잔류응력 측정시 구멍 편심의 영향)

  • Kim, Cheol;Seok, Chang-Seong;Yang, Won-Ho
    • 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.

Prediction of Error due to Eccentricity of Hole in Hole-Drilling Method Using Neural Network

  • Kim, Cheol;Yang, Won-Ho
    • 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.

Prediction for the Error of Hole Eccentricity in Hole-drilling Method Using Neural Network (신경회로망을 이용한 구멍뚫기법의 편심 오차 예측)

  • Kim, Cheol;Yang, Won-Ho;Chung, Ki-Hyun;Hyun, Cheol-Seung
    • 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.

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Drilled Hole Variation of Air Bearing Spindle for PCB according to RUNOUT (PCB드릴링용 공기베어링 스핀들의 런아웃(RunOut)에 따른 가공 홀의 형상변화)

  • Bae M.I.;Kim S.J.;Kim H.C.;Kim K.S.
    • 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.

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Prediction for the Error due to Role Eccentricity in Hole-drilling Method Using Backpropagation Neural Network (역전파신경망을 이용한 구멍뚫기법의 편심 오차 예측)

  • Kim, Cheol;Yang, Won-Ho;Heo, Sung-Pil;Chung, Ki-Hyun
    • 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.

Influence of Inclined Holes in Measurement of Residual Stress by the Hole Drilling Method

  • Kim, Cheol;Yang, Won-Ho;Heo, Sung-Pil
    • 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.

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Influence of the Inclined Hole in Residual Stresses Measurement Using the Hole-Drilling Method (구멍뚫기법을 이용한 잔류응력 측정시 경사구멍의 영향)

  • Kim, Cheol;Yang, Won-Ho;Seok, Chang-Sung;Heo, Sung-Pil
    • Transactions of the Korean Society of Automotive Engineers
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    • v.9 no.6
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    • pp.201-206
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    • 2001
  • The hole-drilling method makes a little hole through the metal surface that has residual stress and measures the relieved stress with a strain gage. It is used widely in measuring the residual stress of surfaces. In this method, the inclined hole is one of the source of error. This paper presents a finite element analysis of influence of the inclined hole for the uniaxial residual stress field. The stress differences between measured and applied residual stress increase proportionally to inclined angle of the hole. The correction equations which easily obtain the residual stress taking account of the inclined angle and direction are derived. The measurement error of stress due to the inclined hole can be reduced to around 1% through this study.

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On-Machine Measurement System Development of Hole Accuracy using Machine Vision (머신비젼을 이용한 구멍 정밀도의 기상측정시스템 개발)

  • Kim, Min-Ho;Kim, Tae-Yeong
    • 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.