1. College of Metallurgical Engineering, Xi’an University of Architecture ＆ Technology, Xi’an 710055, China
2. Shaanxi Key Laboratory of Nanomaterial and Technology, Xi’an 710055,China
In recent years, with the development of computer technology, the implementation of “Material Genome Initiative(MGI)” has promoted the application and development of data-driven technology in material processing. Artificial neural networks are widely used in material science and technology research such as material design, material performance prediction, and optimal parameter determination of process conditions because of their capabilities of self-learning, information storage, associative memory, and high-speed search for optimal solutions. It is difficult to use the “trial and error method” to carry out experimental research. In this paper, the basic theory and development history of artificial neural network at home and abroad are reviewed. The application development of material performance prediction, material design optimization and phase change rule prediction are summarized. The shortcomings of artificial neural network in material processing are explored and the future development is prospected.