Journal of Mechanical Science and Technology

The Korean Society of Mechanical Engineers (대한기계학회)
권호 표지

Dynamic Analysis and Structural Optimization of a Fiber Optic Sensor Using Neural Networks

  • Kim Yong-Yook (The Center for Healthcare Technology Development, Chonbuk National University) ;
  • Kapania Rakesh K. (Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University) ;
  • Johnson Eric R. (Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University) ;
  • Palmer Matthew E. (Luna Innovations Inc.,) ;
  • Kwon Tae-Kyu (Division of Bionics and Bioinformatics, Chonbuk National University) ;
  • Hong Chul-Un (Division of Bionics and Bioinformatics, Chonbuk National University) ;
  • Kim Nam-Gyun (Division of Bionics and Bioinformatics, Chonbuk National University)
  • Published : 2006.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this work is to apply artificial neural networks for solving inverse problems in the structural optimization of a fiber optic pressure sensor. For the sensor under investigation to achieve a desired accuracy, the change in the distance between the tips of the two fibers due to the applied pressure should not interfere with the phase change due to the change in the density of the air between the two fibers. Therefore, accurate dynamic analysis and structural optimization of the sensor is essential to ensure the accuracy of the measurements provided by the sensor. To this end, a normal mode analysis and a transient response analysis of the sensor were performed by combining commercial finite element analysis package, MSC/NASTRAN, and MATLAB. Furthermore, a parametric study on the design of the sensor was performed to minimize the size of the sensor while fulfilling a number of constraints. In performing the parametric study, the need for a relationship between the design parameters and the response of the sensor was fulfilled by using a neural network. The whole process of the dynamic analysis using commercial finite element analysis package and the parameter optimization of the sensor were automated within the MATLAB environment.

Keywords

References

  1. Baker, W. E., 1973, Explosions in Air, University of Texas Press, Austin
  2. Cho, J. R., Jeong, H. S., Yoo, W. S. and Shin, S. W., 2004, 'Optimum Tire Contour Design Using Systematic STOM and Neural Network,' KSME International Journal, Vol. 18 No. 8, pp. 1327-1337 https://doi.org/10.1007/BF02984247
  3. Craig, R. R., 1981, Structural Dynamics : An Introduction to Computer Methods, Wiley, New York
  4. Demuth, H. and Beale, M., 2000, Neural Network Toolbox for Use with MATLAB, The Math Works Inc., pp. 5.16-5.17
  5. Greenman, R. M., Stepniewski, S. W., Jorgensen, C. C. and Roth, K. R., 2002, 'Designing Compact Feedforward Neural Models with Small Training Data Sets,' Journal of Aircraft, Vol. 39, No. 3, pp. 452-459 https://doi.org/10.2514/2.2950
  6. Ha, I. C. and Han, M. C., 2004, 'A Robust Control with a Neural Network Structure for Uncertain Robot Manipulator,' KSME International Journal, Vol. 18 No. 11, pp. 1916-1922 https://doi.org/10.1007/BF02990433
  7. Hadi, M. N. S., 2003, 'Neural Networks Applications in Concrete Structures,' Computers and Structures, Vol. 81, pp. 373-381 https://doi.org/10.1016/S0045-7949(02)00451-0
  8. Hagan, M. T., 1996, Demuth, H. B. and Beale, M., Neural Network Design, PWS Publishing Company, Boston, MA
  9. Haykin, S. S., 1998, Neural Networks : Fundamental Foundation, 2nd Ed, Prentice Hall, Upper Saddle River
  10. Kaufman, K. R., Wavering, T., Morrow, D., Davis, J. and Lieber, R. L., 2003, 'Performance Characteristics of a pressure microsensor,' Journal of Biomechanics, Vol. 36, pp. 283-287 https://doi.org/10.1016/S0021-9290(02)00321-4
  11. Kaveh, A. and Servati, H., 2001, 'Design of Double layer Grids Using Backpropagation Neural Networks,' Computers and Structures, Vol. 79, pp. 1561-1568 https://doi.org/10.1016/S0045-7949(01)00034-7
  12. Kersey, A. D., 1996, 'A Review of Recent Developments in Fiber Optic Sensor Technology,' Optical Fiber Technology, Vol. 2, pp. 291-317 https://doi.org/10.1006/ofte.1996.0036
  13. Kim, S. Y., Moon, B. Y. and Kim, D. E., 2004, 'Optimum Design of Ship Design System Using Neural Network Method in Initial Design of Hull Plate,' KSME International Journal, Vol. 18, No. 11, pp. 1923-1931 https://doi.org/10.1007/BF02990434
  14. Lee, B., 2003, 'Review of the Present Status of Optical Fiber Sensors,' Optical Fiber Technology, Vol. 9, pp. 57-79 https://doi.org/10.1016/S1068-5200(02)00527-8
  15. Lee, E. D., Sim, J. H., Kweon, H. J. and Paik, I. H., 2004, 'Determination of Process Parameters in Stereolithography Using Neural Network,' KSME International Journal, Vol. 18, No. 3, pp. 443-452 https://doi.org/10.1007/BF02996109
  16. Leung, C. K. Y., Yang, Z., Ying, X., Tong, P. and Lee, S. K. L., 2005, 'Delamination Detection in Laminate Composites with an Embedded Fiber Optical Interferometric Sensor,' Sensors and Actuators A-Physical, Vol. 119, pp. 336-344 https://doi.org/10.1016/j.sna.2004.10.007
  17. Li, H. N., Li, D. S. and Song, G. B., 2004, 'Recent Applications of Fiber Optic Sensors to Health Monitoring in Civil Engineering,' Engineering Structures, Vol. 26, pp. 1647-1657 https://doi.org/10.1016/j.engstruct.2004.05.018
  18. Li, Z., Kapania, R. K. and Spillman, W. B., 2004, 'Placement Optimization of Fiber Optic Sensors for a Smart Bed Using Genetic Algorithms,' Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY
  19. Mukherjee, A. and Deshpande, J. M., 1995, 'Application of Artificial Neural Networks in Structural Design Expert Systems,' Computers and Structures, Vol. 54, No. 3, pp. 367-375 https://doi.org/10.1016/0045-7949(94)00342-Z
  20. Nikolaidis, E., Long, L. and Ling, Q., 2000, 'Neural Networks and Response Surface Polynomials for Design of Vehicle Joints,' Computers and Structures, Vol. 75, pp. 593-607 https://doi.org/10.1016/S0045-7949(99)00113-3
  21. Park, J. H. and Seo, K. K., 2003, 'Approximate Life Cycle Assessment of Product Concepts Using Multiple Regression Analysis and Artificial Neural Networks,' KSME International Journal, Vol. 17, No. 12, pp. 1969-1976 https://doi.org/10.1007/BF02982436
  22. Rai, M. M. and Madavan N. K., 2000, 'Aerodynamic Design Using Neural Networks,' AIAA Journal, Vol. 38, No. 1, pp. 173-182 https://doi.org/10.2514/2.938
  23. Ramasamy, J. V. and Rajasekaran, S., 1996, 'Artificial Neural Network and Genetic Algorithm for the Design Optimization of Industrial Roofs,' Computers and Structures, Vol. 58, No. 4, pp. 747-755 https://doi.org/10.1016/0045-7949(95)00179-K
  24. Spillman, W. B., Mayers, M., Bennett, J., Gong, J., Meissner, K. E., Davis, B., Claus, R. O., Muelenaer Jr, A. A. and Xu, X., 2004, 'A 'Smart' Bed for Non-intrusive Monitoring of Patient Physiological Factors,' Measurement Science and Technology, Vol. 15, pp. 1614-1620 https://doi.org/10.1088/0957-0233/15/8/032
  25. Yang, C., Zhao, C.. Wold, L. and Kaufman, K. R., 2003, 'Biocompatibility of a Physiological Pressure Sensor,' Biosensors and Bioelectronics, Vol. 19, pp. 51-58 https://doi.org/10.1016/S0956-5663(03)00131-3