Load estimation from photoelastic fringe patterns under combined normal and shear forces.

Abstract

Recently there has been some spurt of interests to use photoelastic materials for sensing applications. This has been successfully applied for designing a number of signal-based sensors, however, there have been limited efforts to design image-based sensors on photoelasticity which can have wider applications in term of actual loading and visualisation. The main difficulty in achieving this is the infinite loading conditions that may generate same image on the material surface. This, however, can be useful for known loading situations as this can provide dynamic and actual conditions of loading in real time. This is particularly useful for separating components of forces in and out of the loading plane. One such application is the separation of normal and shear forces acting on the plantar surface of foot of diabetic patients for predicting ulceration. In our earlier work we have used neural networks to extract normal force information from the fringe patterns using image intensity. This paper considers geometric and various other statistical parameters in addition to the image intensity to extract normal as well as shear force information from the fringe pattern in a controlled experimental environment. The results of neural network output with the above parameters and their combinations are compared and discussed. The aim is to generalise the technique for a range of loading conditions that can be exploited for whole-field load visualisation and sensing applications in biomedical field.