OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Grewal, Gurtej Singh; Dubey, Venketesh N. (2007)
Languages: English
Types: Article
Subjects: ge
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [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
    • [11] Chung D 1998 Neural net based torque sensor using birefringent materials Sensors Actuators A 70 243-9
    • [12] Freeman J and Skapura D 1992 Neural Networks: Algorithms, Applications and Programming Techniques (Reading, MA: Addison-Wesley)
    • [13] Cameron A, Danial R and Durrant-White H 1988 Touch and motion Proc. IEEE Int. Conf. Robot. Autom. 1-3 1062-7
    • [14] Eghtedari F, Hopkins S and Pham D 1993 Model of a slip sensor Proc. Int. Mech. Eng. 207 55-64
    • [15] Vishay Measurement Groups 1992 Photoelastic materials Bulletin S-116-G
    • [16] Bharath R and Drosen J 1994 Neural networks and statistical analysis Neural Network Computing (New York: McGraw-Hill)
    • [17] Mathworks, MatLAB 2002
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok