Load extraction from photoelastic images using neural networks

This source preferred by Venky Dubey

Authors: Dubey, V.N., Grewal, G.S. and Claremont, D.J.

Journal: Experimental Mechanics

Volume: 47

Pages: 263-270

ISSN: 0014-4851

DOI: 10.1007/s11340-006-9002-z

Photoelastic materials develop colored fringes under white light when subjected to mechanical stresses, which can be viewed through a polariscope. This technique has traditionally been used for stress analysis of loaded components, however, this can also be potentially used in sensing applications where the requirement may be measurement of the stimulating forces causing the generation of fringes. This leads to inverse photoelastic problem where the developed image can be analyzed for the input forces.

However, there could be infinite number of possible solutions which cannot be determined by conventional techniques. This paper presents neural networks based approach to solve this problem. Experiments conducted to prove the principle have been verified with theoretical results and finite element analysis of loaded specimens. The developed technique, if generalized, can be implemented for whole-field analysis of the stress patterns involving complex fringes under different loading conditions. This can also provide direct visualization of the stress field, which may find application in a variety of specialized areas including biomedical engineering and robotics.

This data was imported from Scopus:

Authors: Dubey, V.N., Grewal, G.S. and Claremont, D.J.

Journal: Experimental Mechanics

Volume: 47

Issue: 2

Pages: 263-270

eISSN: 1741-2765

ISSN: 0014-4851

DOI: 10.1007/s11340-006-9002-z

Photoelastic materials develop colored fringes under white light when subjected to mechanical stresses, which can be viewed through a polariscope. This technique has traditionally been used for stress analysis of loaded components, however, this can also be potentially used in sensing applications where the requirement may be measurement of the stimulating forces causing the generation of fringes. This leads to inverse photoelastic problem where the developed image can be analyzed for the input forces. However, there could be infinite number of possible solutions which cannot be determined by conventional techniques. This paper presents neural networks based approach to solve this problem. Experiments conducted to prove the principle have been verified with theoretical results and finite element analysis of loaded specimens. The developed technique, if generalized, can be implemented for whole-field analysis of the stress patterns involving complex fringes under different loading conditions. This can also provide direct visualization of the stress field, which may find application in a variety of specialized areas including biomedical engineering and robotics. © Society for Experimental Mechanics 2007.

This data was imported from Web of Science (Lite):

Authors: Dubey, V.N., Grewal, G.S. and Claremont, D.J.

Journal: EXPERIMENTAL MECHANICS

Volume: 47

Issue: 2

Pages: 263-270

ISSN: 0014-4851

DOI: 10.1007/s11340-006-9002-z

The data on this page was last updated at 04:43 on November 23, 2017.