Extraction of Load Information From Photoelastic Images Using Neural Networks

Abstract

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 the 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 obtained 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 the loaded specimens. The technique, if fully developed, can be implemented for any generalized case involving complex fringe patterns under different loading conditions for whole-field analysis of the stress pattern, which may find application in a variety of specialized areas including biomedical engineering and robotics.