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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

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稀有金属：英文版 2019年第12期38卷 1124-1130页

作者： Ji-Hui Luo Xue-Feng Liu Zhang-Zhi Shi Yi-Fei Liu School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China Beijing Laboratory of Metallic Materials and Processing for Modem Transportation University of Science and Technology BeijingBeijing 100083China

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.

关键词： Two-phase zone continuous casting Cu-Sn alloy Grains-covered grains microstructure quantification Back propagation artificial neural network

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A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning

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Journal of Materials Science & Technology 2021年第34期93卷 191-204页

作者： Chunguang Shen Chenchong Wang Minghao Huang Ning Xu Sybrand van der Zwaag Wei Xu State key laboratory of rolling and automation Northeastern UniversityShenyangLiaoning 110819China Novel Aerospace Materials Group Faculty of Aerospace EngineeringDelft University of Technology2629 HS Delftthe Netherlands

We present an electron backscattered diffraction(EBSD)-trained deep learning(DL)method integrating traditional material characterization informatics and artificial intelligence for a more accurate classification and quantification of complex microstructures using only regular scanning electron microscope(SEM)images.In this method,EBSD analysis is applied to produce accurate ground truth data for guiding the DL model training.An U-Net architecture is used to establish the correlation between SEM input images and EBSD ground truth data using only small experimental datasets.The proposed method is successfully applied to two engineering steels with complex microstructures,i.e.,a dual-phase(DP)steel and a quenching and partitioning(Q&P)steel,to segment different phases and quantify phase content and grain size.Alternatively,once properly trained the method can also produce quasi-EBSD maps by inputting regular SEM images.The good generality of the trained models is demonstrated by using DP and Q&P steels not associated with the model training.Finally,the method is applied to SEM images with various states,i.e.,different imaging modes,image qualities and magnifications,demonstrating its good robustness and strong application ability.Furthermore,the visualization of feature maps during the segmenting process is utilised to explain the mechanism of this method’s good performance.

关键词： microstructure quantification Deep learning Electron backscatter diffraction Small sample problem

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