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

  • 제24권9호
  • /
  • Pages.2283-2291
  • /
  • 2000
  • /
  • 1226-4873(pISSN)
  • /
  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
권호 표지
DOI QR코드

DOI QR Code

신경회로망을 이용한 변동하중 하에서의 균열열림점 자동측정

Automatic Determination of Crack Opening Loading under Random Loading by the Use of Neural Network

  • 발행 : 2000.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The neural network method is applied to automatically measure the crack opening load under random loading. The crack opening results obtained are compared with the visual measured results. Fatigue crack growth under random loading is predicted using the crack opening data measured by the neural network method, and the prediction results are compared with experimental ones. It is found that the neural network method can be successfully applied to consistently measure the crack opening load under random loading and also gives some results different from the results by visual measurement.

키워드

참고문헌

  1. Elber, W., 1971, 'The Significance of Fatigue Crack Closure,' ASTM STP 486, pp. 230-242
  2. Kikukawa, M., Jono, M. and Tanaka, K., 1976, 'Fatigue Crack Closure Behavior at Low Stress Intensity Levels,' Proc. ICM2, pp. 254-277
  3. Carman, C. D., Turner, C. C. and Hillberry, B. M., 1988, 'A Method for Determining Crack Opening Load from Load-Displacement Data,' Mechanics of Fatigue Crack Closure, ASTM STP 982, pp. 214-221 https://doi.org/10.1520/STP27210S
  4. Donald, J. K., 1988, 'A Procedure for Standardizing Crack Closure Levels,' Mechanics of Fatigue Crack Closure, ASTM STP 982, pp. 222-229 https://doi.org/10.1520/STP27211S
  5. Roberson, L. J. and Kirk, M. T., 1988, 'A Statistical Approach to Crack Closure Determination https://doi.org/10.1520/STP27212S
  6. Kim, C. Y. and Song, J. H., 1993, 'An Automated Procedure for Determining Crack Opening Level From Differential Displacement Signal Data,' Int. J. Fatigue, Vol. 15, pp. 301-309 https://doi.org/10.1016/0142-1123(93)90379-5
  7. ASTM, 1995, Annual Book of ASTM standards, ASTM E647-95, Philadelphia, Vol. 03.01, pp. 603-606
  8. Kang, J. Y. and Song, J. H., 1998, 'Neural Network Applications in Determining the Fatigue Crack Opening Load,' Int. J. Fatigue, Vol. 20, No. 1, pp. 57-69 https://doi.org/10.1016/S0142-1123(97)00119-9
  9. 이재규, 1996, 'UNIK를 이용한 전문가시스템의 개발,' 법영사, 서울, pp. 181-253
  10. Fausett, L., 1994, Fundamentals of Neural Networks, Prentice-Hall, Inc., New Jersey
  11. Haykin, S., 1994, Neural Networks, Macmillan, New York
  12. 최상우, 이준현, 1997, '저면산란 초음파 신호 및 신경회로망을 이용한 균열크기 결정,' 대한기계학회논문집 추계학술대회 논문지(A), pp. 35-40
  13. 주원식, 조석수, 1996, '신경회로망을 이용한 고온 저사이클 피로균열성장 모델링에 관한 연구,' 대한기계학회논문집, 제20권, 제9호, pp. 2752-2759
  14. Kim, C.Y. and Song, J.H., 1994, 'Fatigue Crack Closure and Growth Behavior Under Random Loading,' Engng. Fract. Mech., Vol. 40, pp. 105-120 https://doi.org/10.1016/0013-7944(94)90115-5
  15. Jono, M., Song, J. and Kikukawa, M., 1984, 'Fatigue Crack Growth and Crack Closure of Structural Materials Under Random Loading,' Proc. ICM, Vol. 6, pp. 1735-1742
  16. ASTM, 1995, Annual Book of ASTM standards, ASTM E1049-85, Philadelphia, Vol. 03.01, pp. 726-734