DOI QR코드

DOI QR Code

압입축의 손상저감을 위한 최적설계 연구

Optimal Design of Press-Fitted Axle Shaft Considering Stress Relief

  • Ko, Jaechun (Department of Mechanical Engineering Yonsei University) ;
  • Lee, Jongsoo (School of Mechanical Engineering Yonsei University) ;
  • Choi, Ha-Young (Department of Mechanical Engineering Dongyang Mirae University)
  • 투고 : 2013.07.08
  • 심사 : 2013.09.25
  • 발행 : 2013.10.15

초록

Creation of a stress relief groove is a fairly simple yet high-performance method. During the application of this method, it is important to consider the location and size of the groove in order to achieve better performance. Consequently, this research proposes an approach for optimizing the application of the stress relief groove method to a press-fitted assembly. In a boss design, the position and diameter of the groove are configured as design variables and the design of experiments is applied. Based on this information, a 3D model is built and analyzed using the finite element analysis software ABAQUS. Meta-models are created using back-propagation neural networks. Then, deterministic optimization results obtained from a genetic algorithm are compared with the results of the finite element analysis. The temperature sensitivity of the optimized model is analyzed, and finally, reliability-based design optimization is conducted for enhancing the design quality.

키워드

참고문헌

  1. Smith, R. A., Hillmansen, S., 2004, A Brief Historical Overview of the Fatigue of Railway Axles, Proceedings of the Institution of Mechanical Engineers. Part F: Journal of Rail and Rapid Transit, 218:4 267-278. https://doi.org/10.1243/0954409043125932
  2. Pilkey, W. D., Pilkey, D. F., 2008, Stress Concentration Factors, John Wiley & Sons.
  3. Lee, D. H., Kwon, S. J., Choi, J. B., Kim, Y. J., 2007, The Effect of Fretting Wear on Fatigue Life of Press-fitted Shaft, Korean Society of Mechanical Engineering, 34:11 1083-1092. https://doi.org/10.3795/KSME-A.2007.31.11.1083
  4. Lee, D. H., Kwon, S. J., Ham, Y. S., You, W. H., 2010, Characterization of Fretting Damage in a Press-fitted Shaft below the Fretting Fatigue Limit, Procedia Engineering, 2:1 1945-1949. https://doi.org/10.1016/j.proeng.2010.03.209
  5. Wise, S., Burdon, E.S., 1964, The dual roles of design and surface treatment in combating fatigue failures, J. Instn Loco. Engrs, 54 142-177.
  6. Nishioka, K., Komatshu, K., 1967, Researches on Increasing the Fatigue Strength of Press-Fitted Axle, Trans. Jpn. Soc. Mech. Eng., 33:248 503-511. https://doi.org/10.1299/kikai1938.33.503
  7. Peterson, R.E., Wahl, A.M., 1935, Fatigue of Shafts at Fitted Members, With a Related Photoelastic Analysis, J. Appl. Mechs., 57 A.1-A.11.
  8. Lee, D. H., Kwon, S. J., Seo, J. W., Kwon, S. T., You, W. H., 2010, Evaluation of Fatigue Crack Initiation Life according to the Hub Contact Shape in a Press-fitted Shaft, Korean Society for Precision Engineering 2010 spring conference, 1467-1468.
  9. ABAQUS, 2012, ABAQUS Version 6.12, SIMULIA.
  10. HyperMesh, 2011, HyperMesh Version 11.0, Altair Engineering Inc..
  11. Chang, Y. C., Chiu, M. C., Wu, L. W., 2010, Shape Optimization of Mufflers Hybridized with Multiple Connected Tubes Using the Boundary Element Method, Neural Networks, and Genetic Algorithm, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 224:4 901-913. https://doi.org/10.1243/09544062JMES1891
  12. Homil, k., Stinchcombe, M., White, H., 1990, Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feed-forword Networks, Neural Networks, 3:5 551-560. https://doi.org/10.1016/0893-6080(90)90005-6
  13. Goldberg, David E., John, H., 1988, Genetic Algorithms and Machine Learning, Machine learning 3:2 95-99.
  14. Kim, J. H., Kang, K. W., Goo, B. C., You, W. H., 2006, A Study on the Low Temperature Mechanical Characteristics of SM490A for the Railroad Vehicle Structure, Korean Society for Railway, 9:6 695-700.
  15. Youn, B. D., Choi, K. K., Yang, R. J., Gu, L., 2004, Reliability-Based Design Optimization for Crashworthiness of Vehicle Side Impact, Structural and Multidisciplinary Optimization, 26:3-4 272-283. https://doi.org/10.1007/s00158-003-0345-0