Abstract

The paper investigates the efficacy of developing a whole-field imaging sensor based on the principles of photoelasticity. A flatbed sensor has been designed that produces colored fringes under white light when load is applied. These fringes can be analyzed using conventional photoelastic techniques; however, for whole-field sensing applications the loading has to be on the plane rather than the conventional in-plane photoelasticity. This requires some new strategies to be developed to analyze the out of plane loading situations. Further, for such applications low modulus materials are to be used for appreciable in-plane deformation under vertical load and for pronounced photoelastic effect. The paper discusses efficacy of both RGB calibration and phase shifting techniques in sensing applications and proposes a neural network based approach in an expert system environment that can extract load information from photoelastic images in real time. The characteristics of fringe patterns obtained under vertical and shear forces have been studied and the results obtained under these conditions are discussed with their limitations. Finally, a case study has been conducted to analyze a foot image and conclusions drawn from this have been reported together with future research directions.