Inverse problem of photoelastic fringe mapping using neural networks

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Grewal, Gurtej Singh ; Dubey, Venketesh N. (2007)

This paper presents an enhanced technique for inverse analysis of photoelastic fringes using neural networks to determine the applied load. The technique may be useful in whole-field analysis of photoelastic images obtained due to external loading, which may find application in a variety of specialized areas including robotics and biomedical engineering. The presented technique is easy to implement, does not require much computation and can cope well within slight experimental variations. The technique requires image acquisition, filtering and data extraction, which is then fed to the neural network to provide load as output. This technique can be efficiently implemented for determining the applied load in applications where repeated loading is one of the main considerations. The results presented in this paper demonstrate the novelty of this technique to solve the inverse problem from direct image data. It has been shown that the presented technique offers better result for the inverse photoelastic problems than previously published works.
  • References (17)
    17 references, page 1 of 2

    [1] Ramesh K 2000 Digital Photoelasticity: Advanced Techniques and Applications (Berlin: Springer)

    [2] Ajovalasit A, Barone S and Petrucci G 1995 Towards RGB photoelasticity: full-field automated photoelasticity in white light Exp. Mech. 35 193-200

    [3] Quiroga J, Botella A and Gomez-Pedrero J 2002 Improved method for isochromatic demodulation by RGB calibration Appl. Opt. 41 3461-8

    [4] Ramesh K and Deshmukh S 1996 Three fringe photoelasticity-use of colour image processing hardware to automate ordering of isochromatics Strain 32 79-86

    [5] Patterson E A 2002 Digital photoelasticity: principles, practice and potential Strain 38 27-39

    [6] Patterson E A, Hobbs J and Greene R 2003 A novel instrument for transient photoelasticity Exp. Mech. 43 403-9

    [7] Patterson D 1996 Artificial Neural Networks: Theory and Applications (Englewood Cliffs, NJ: Prentice Hall)

    [8] Bishop C 1995 Neural Networks for Pattern Recognition (Oxford: Oxford University Press)

    [9] Noroozi S, Amali R and Vinney J 2003 Inverse problem approach using photoelastic analysis and artificial neural networks in tandem Strain 40 73-7

    [10] Brooks F, Ouh-Young M, Batter J and Kilpatrick P 1990 Project GROPE-haptic displays for scientific visualization Comput. Graph. 24 177-85

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