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

作　　者：Chunguang Shen Chenchong Wang Minghao Huang Ning Xu Sybrand van der Zwaag Wei Xu 

机构地区：[1]State key laboratory of rolling and automation,Northeastern University,Shenyang,Liaoning 110819,China [2]Novel Aerospace Materials Group,Faculty of Aerospace Engineering,Delft University of Technology,2629 HS Delft,the Netherlands

出　　处：《Journal of Materials Science & Technology》2021年第34期191-204,共14页材料科学技术（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(Grants No.51722101,U1808208);financial support provided by the National Key R&D Program(Grant No.2017YFB0703001);major scientific and technological innovation projects of Shandong Province(Grant No.2019TSLH0103)。

摘　　要：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 

分 类 号：TG142.1[一般工业技术—材料科学与工程] TP18[金属学及工艺—金属材料]