한국정밀공학회지 (Journal of the Korean Society for Precision Engineering)

  • 제17권4호
  • /
  • Pages.135-141
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  • 2000
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  • 1225-9071(pISSN)
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  • 2287-8769(eISSN)
한국정밀공학회 (Korean Society for Precision Engineering)
권호 표지

CNC 공작기계에서 열변형 오차 보정 시스템의 고장진단 및 복구

Fault Diagnosis and Recovery of a Thermal Error Compensation System in a CNC Machine Tool

  • 황석현 (경북대학교 기계공학부 대학원) ;
  • 이진현 (안동정보대학 기계) ;
  • 양승한 (경북대학교 기계공학부)
  • 발행 : 2000.04.01
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⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. S. Yang, J. Yuan and J. Ni, 'The Improvement of Thermal Error Modeling and Compensation on Machine Tools by CMAC Neural Network,' Int. J. Mach. Tools Manufact., Vol. 36, No. 4, pp. 527-537, 1996 https://doi.org/10.1016/0890-6955(95)00040-2
  2. S. Yang, J. Yuan and J. Ni, 'Accuracy Enhancement of a Horizontal Machining Center by Real-Time Error Compensation,' Journal of Manufacturing Systems, Vol. 15, No. 2, pp. 113-124, 1996
  3. A. M. Agoging and A. Rege, 'IDES : Influence Diagram based Expert System,' Mathematical Modeling, Vol. 8, pp. 227-233, 1987 https://doi.org/10.1016/0270-0255(87)90579-3
  4. A. M. Agogino and K. Ramamurthi, 'Real Time Influence Diagrams for Monitoring and Controlling Mechanical Systems,' Influence Diagrams, Belief Nets and Decision Analysis, R. M. Oliver and J. Q. Smith(Ed.), John Wiley and Sons Ltd., pp. 199-228, 1990
  5. P. R. Drake and D. Pan, 'Multiple Fault Diagnosis for a Machine Tool's Flood Coolant System Using a Neural Network,' Int. J. Mach. Tools Manufact., Vol. 36, No. 11, pp. 1247-1251, 1996 https://doi.org/10.1016/0890-6955(95)00114-X
  6. J. Zhang and J. Morris, 'Process Modelling and Fault Diagnosis Using Fuzzy Neural Network,' Fuzzy Sets and Systems, Vol. 79, pp. 127-140, 1996 https://doi.org/10.1016/0165-0114(95)00295-2
  7. I. Giraud, P. Laurence, G. L. Gissinger and M. Fervel, 'Real Time Fault Detection and Diagnosis : Application to Critical Behaviour fo a Road Vehicle, AVEC'96 International Symposium on Advanced Vehicle Control, pp. 1268-1275, 1996
  8. D. Neogi and C. E. Schlags, 'Multivariate Statistical Analysis of an Emulsion Batch Process,' Ind. Eng. Chem. Res., Vol. 37, pp. 3971-3979, 1998 https://doi.org/10.1021/ie980243o
  9. 황석현, 이진현, 양승한, 실시간 오차 보정을 위한 열변형 오차 모델의 최적 변수 선택, 한국정밀학회지, 제16권, 제3호, pp. 215-221, 1999
  10. R. A. Fisher, 'Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Infinitely Large Population,' Biometrika, Vol. 10, pp. 507-521, 1915 https://doi.org/10.1093/biomet/10.4.507