摘要：High-strength steels are mainly composed of medium-or low-temperature microstructures,such as bainite or martensite,with coherent transformation *** type of microstructure has a high density of dislocations and fine crystallographic structural units,which ease the coordinated matching of high strength,toughness,and ***,given its excellent welding perform-ance,high-strength steel has been widely used in major engineering constructions,such as pipelines,ships,and ***,visual-ization and digitization of the effective units of these coherent transformation structures using traditional methods(optical microscopy and scanning electron microscopy)is difficult due to their complex ***,the establishment of quantitative relationships with macroscopic mechanical properties and key process parameters presents additional *** article reviews the latest progress in microstructural visualization and digitization of high-strength steel,with a focus on the application of crystallographic methods in the development of high-strength steel plates and *** obtained the crystallographic data(Euler angle)of the transformed microstruc-tures through electron back-scattering diffraction and combined them with the calculation of inverse transformation from bainite or martensite to austenite to determine the reconstruction of high-temperature parent austenite and orientation relationship(OR)during con-tinuous cooling ***,visualization of crystallographic packets,blocks,and variants based on actual OR and digit-ization of various grain boundaries can be effectively completed to establish quantitative relationships with alloy composition and key process parameters,thereby providing reverse design guidance for the development of high-strength steel.

摘要：With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials *** learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data *** with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel *** review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model ***,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and *** application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also ***,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.