Neural network approach to recognize the grid patterns in experimental mechanics

In this paper, we develop a neural network system that applies to the work of experimental mechanics. Two procedures are presented: one is the color calibration that used to filter 256 colors into three colors by adaptive-competitive learning network (ACLN). The other is the pattern recognition that is used to recognize the grid patterns and search the intersection points of grids by back propagation neural network (BPNN). In this paper, plane extrusion through a cosine-curved die is adopted to illustrate as example. The results show that the correlative points between the calculation and experiment are shown quite well agreement.