Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction for the Error due to Role Eccentricity in Hole-drilling Method Using Backpropagation Neural Network

역전파신경망을 이용한 구멍뚫기법의 편심 오차 예측

  • Kim, Cheol (Dept.of Mechanical Design, Graduate School of Sungkyunkwan University) ;
  • Yang, Won-Ho (Dept.of Mechanical Engineering, Sungkyunkwan University) ;
  • Heo, Sung-Pil (Korea Atomic Energy Research Institute) ;
  • Chung, Ki-Hyun (Dept.of Mechanical Design, Graduate School of Sungkyunkwan University)
  • 김철 (성균관대학교 대학원 기계설계학과) ;
  • 양원호 (성균관대학교 기계공학부) ;
  • 허성필 (한국원자력연구소) ;
  • 정기현 (성균관대학교 대학원 기계설계학과)
  • Published : 2002.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

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.

Keywords

References

  1. Mathar, J., 1934, 'Determination of Initial Stresses by Measuring the Deformation Around Drilled Holes,' Trans. ASME, Iron & Steel, Vol 56, pp. 249-254
  2. Soete, W., Vancrombrugge, R., 1950, 'An Industrial Method for the Determination of Residual Stresses,' Proceedings of the Society for Experimental Stress Analysis, Vol. VIII, No. 1, pp. 17-28
  3. 'Standard Test Method for Determining Residual Stresses by the Hole-Drilling Strain-Gage Method,' 1992, ASTM E 837-92
  4. Ajovalasit, A., 1979, 'Measurement of Residual Stresses by the Hole-drilling Method : Influence of Hole Eccentricity,' Journal of Strain Analysis, Vol. 14, No.4, pp. 171-178 https://doi.org/10.1243/03093247V144171
  5. Wang Jia-Yong, 1990, 'Refined Analysis of the Relieved Strain Coefficients for the Off-center Hole-drilling Case,' Experimental Mechanics, pp. 367-371 https://doi.org/10.1007/BF02321506
  6. 김 철, 석창성, 양원호, 2000, '구멍뚫기법을 이용한 잔류응력 측정시 구멍 편심의 영향,' 대한기계학회논문집(A), 제24권, 제8호, pp. 2059-2064
  7. Kobayasi, A. S., 1993, Handbook on Experimental Mechanics, 2nd ed, SEM, pp. 785-790
  8. 주원식, 조석수, 1996, '신경회로망을 이용한 고온 저사이클 피로균열성장 모델링에 관한 연구'. 대한기계학회논문집(A), 제20권, 제9호, pp. 2752-2759
  9. 조석수, 주원식, 2000, '역전파신경회로망을 이용한 피로균열성장과 수명 모델링에 관한 연구,' 대한기계학회논문집(A), 제24권, 제3호, pp. 634-644.
  10. Inamdar, M. V., Data, P. P. and Desai, U. B., 2000, 'Studies on the Preduction of Springback in Air Vee Bending of Metallic Sheets Using an Artificial Neural Network', Journal of Materials Processing Technology, Vol. 108, pp. 45-54 https://doi.org/10.1016/S0924-0136(00)00588-4
  11. Fausett, L., 1994, Fundamentals of Neural Network : Architectures, Algorithms and Applications, Prentice Hall, pp. 3-37
  12. Rumelhart, D. E., Hinton, G. E., Williams, R. J., 1986, 'Learning Internal Representations by Error Propagation in Parallel Distributed Processing,' The MIT Press, Vol. 1, pp. 675-695