Image analysis of ceramic burning based on cellular automata

In order to effectively improve the flexural strength and fracture toughness of ceramics, two methods can be used to control the growth rate of grain during sintering of ceramic materials by adding a certain amount of (W,Ti)C material. Therefore, on the one hand, the paper simulates the composite ceramic materials based on cellular automata (CA) to optimize the ratio of two formulations. On the other hand, in order to optimize sintering process, CA modeling is also carried out for the sintering process of composite ceramics. Finally, in order to detect the temperature of the firing zone of the ceramic kiln by using the characteristics of the flame image of the firing zone, the K-means clustering method is used for the color segmentation of the flame image of the firing zone of the ceramic kiln. The experimental results show that the size of the grains is in accordance with the actual situation of the simulation and the microstructure evolution of the composites can be simulated well by using the CA theory to simulate the composites; with the increase of simulation time, the grain size distribution is basically unchanged, which accords with the normal distribution, and the simulation process of grain growth is very stable. Based on the K-mean clustering segmentation method, the segmentation of the flame image of ceramic kiln firing zone is realized. This method also provides a good technical means for feature extraction of flame image in ceramic kiln firing zone.