Grain boundary detection in microstructure images using computational intelligence

Two computational intelligence approaches, a fuzzy logic algorithm and a neural network (NN) algorithm, for grain boundary detection in images of superalloy steel microstructure during sintering are presented in this paper. The images are obtained from an optical microscope and are quite noisy, which adversely affects the performance of common image processing tools. The only known way to accurately determine the grain boundaries is digitizing by hand. This is a very time-consuming process, causes operator fatigue, and it is prone to human errors and inconsistency. An automated system is therefore needed to complete as much work as possible and we consider a fuzzy approach and a neural approach. Both methods performed better than the widely available standard image processing tools with the neural approach superior on images similar to those trained while the fuzzy approach showed more tolerance of disparate images.