Rare Metals

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Microstructure quantification of Cu–4.7Sn alloys prepared by two-phase zone continuous casting and a BP artificial neural network model for microstructure prediction

  • Ji-Hui Luo
  • Xue-Feng Liu
  • Zhang-Zhi Shi
  • Yi-Fei Liu


Microstructures of Cu–4.7Sn (%) alloys prepared by two-phase zone continuous casting (TZCC) technology contain large columnar grains and small grains. A compound grain structure, composed of a large columnar grain and at least one small grain within it, is observed and called as grain-covered grains (GCGs). Distribution of small grains, their numbers and sizes as well as numbers and sizes of columnar grains were characterized quantitatively by metallographic microscope. Back propagation (BP) artificial neural network was employed to build a model to predict microstructures produced by different processing parameters. Inputs of the model are five processing parameters, which are temperatures of melt, mold and cooling water, speed of TZCC, and cooling distance. Outputs of the model are nine microstructure quantities, which are numbers of small grains within columnar grains, at the boundaries of the columnar grains, or at the surface of the alloy, the maximum and the minimum numbers of small grains within a columnar grain, numbers of columnar grains with or without small grains, and sizes of small grains and columnar grains. The model yields precise prediction, which lays foundation for controlling microstructures of alloys prepared by TZCC.


Two-phase zone continuous casting Cu–Sn alloy Grains-covered grains Microstructure quantification Back propagation artificial neural network 



This work was financially supported by the National Key Research and Development Plan of China (No. 2016YFB0301300), the National Natural Science Foundation of China (Nos. 51374025, 51674027 and U1703131), and the Beijing Municipal Natural Science Foundation (No. 2152020).


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Copyright information

© The Nonferrous Metals Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina
  2. 2.Beijing Laboratory of Metallic Materials and Processing for Modern TransportationUniversity of Science and Technology BeijingBeijingChina

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