Dendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribution of dendrites. In this work, the second-generation single crystal superalloy HT901 with a diameter of 15 mm was imaged under a metallurgical microscope. An automatic dendrite core identification and full-field quantitative statistical analysis method is proposed to automatically detect the dendrite core and calculate the local PDAS. The Faster R-CNN algorithm combined with test time augmentation (TTA) technology is used to automatically identify the dendrite cores. The local multi-directional algorithm combined with Voronoi tessellation is used to determine the local nearest neighbor dendrite and calculate the local PDAS and coordination number. The accuracy of using Faster R-CNN combined with TTA to detect the dendrite core of HT901 reaches 98.4%, which is 15.9% higher than using Faster R-CNN alone. The algorithm calculates the local PDAS of all dendrites in H901 and captures the Gaussian distribution of the local PDAS. The average PDAS determined by the Gaussian distribution is 415 μm, which is only a small difference from the average spacing $\overline{\lambda }$ (420 μm) calculated by the traditional method. The technology analyzes the relationship between the local PDAS and the distance from the center of the sample. The local PDAS near the center of HT901 are larger than those near the edge. The results suggests that the method enables the rapid, accurate and quantitative dendritic distribution characterization.

Inconel 718 (IN718) nickel-based superalloy is widely used in aerospace and nuclear applications owing to its excellent comprehensive mechanical properties, oxidation resistance, and hot corrosion resistance. However, the elemental segregation caused by heterogeneous solidification during casting has great influence on the mechanical properties. Therefore, accurately characterizing the segregation behavior is necessary. Traditional quantitative characterization of elemental segregation uses various sampling methods, in which only macroscopic segregation results are obtained. In this study, micro-beam X-ray fluorescence (μ-XRF) is used for the quantitative characterization of element micro-segregation in IN718 superalloy. The concentration distributions of Cr, Fe, Mo, Nb, and Ti in IN718 alloy are determined with optimized testing parameters, and the degree of elemental segregation in different regions of the analytical area is calculated. It is found that the segregation degree of Nb and Ti in the testing area is larger than other alloying elements. The correlation between the microstructure distribution and the segregation degree of Nb and Ti has been studied using scanning electron microscopy (SEM) combined with energy-dispersive spectrometry (EDS). There is severe segregation of Nb and Ti in areas where Nb-containing precipitates are accumulated. The distribution of abnormal signals of Nb with a high fluorescence intensity has a close relationship with the area of precipitates-enriched Nb.

The railway wheel is the key component of high-speed railway train. To assure the safety in service, higher requirements are put forward in this study for the composition, microstructure uniformity, and comprehensive properties of wheel materials. In this paper, the high throughput quantitative distribution characterization methods of composition, microstructure, inclusions and Vickers hardness of high-speed railway wheel materials based on the spark source original position analysis technique, high throughput scanning electron microscope (SEM) combined with image batch processing technology, and automatic two-dimensional quantitative distribution analysis technique of inclusions and micro hardness have been studied. The distribution trend of the content of nine elements, size and quantity of sulfides and oxides, ferrite area fraction, and Vickers hardness from the wheel tread surface to the radial depth of about 50 mm below the surface has been discussed. The influence of inclusions distribution on the element segregation and the effect of rim-chilling process with different water spraying angle on the distribution of microstructure and micro hardness have been investigated. It was found that unsynchronized cooling on both sides of the rim altered the phase behavior of ferrite and pearlite and obvious inhomogeneity distribution of ferrite appeared, which led to the asymmetrical Vickers hardness in areas near or away from the flange. Based on the quantitative characterization of area fraction and micro hardness on the same location of wheel rim, a statistical mapping relationship between ferrite area fraction and Vickers hardness was established.

Heat-resistant P91 martensitic steel is used to produce high-temperature steam pipelines in (ultra) critical power plants. However, non-metallic inclusions are inevitably produced in the metallurgical process. The type, composition, morphology, quantities, size and distribution of these inclusions have significant influences on the properties of materials. The present work developed an original position statistical distribution analysis method to characterize non-metallic inclusions in P91 steel based on scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS). Two samples from P91 steel pipes fabricated by different processes were examined. The non-metallic inclusions in these samples could be divided into spherical oxides, strand-shaped oxides, spherical sulfides, spherical oxygen–sulfur compounds, strip sulfides and other inclusions. The amount, proportional area, particle sizes, original positions and statistical distribution results of non-metallic inclusions in different processed samples were assessed, and the causes and effects of these inclusions were analyzed. This novel method could provide diversified information on inclusions, which is helpful for the improvement of metallurgical processes and service performance evaluations.

A new strategy for the high-throughput characterization of the mechanical homogeneity of metallurgical materials is proposed. Based on the principle of hydrostatic transmission and the synergistic analysis of the composition, microstructure, defects, and surface profile of the chosen material, the microstrain characteristics and changes in surface roughness after isostatic pressing were analyzed. After isostatic pressing, two types of microstrains were produced: low microstrain (surface smoothening with decreasing roughness) and large microstrain (surface roughening with increasing roughness). Furthermore, the roughness of the roughened microregions could be further classified based on the strain degree. The phenomenon of weak-interface damage with a large microstrain (plastic deformation, cleavage fracture, and tearing near nonmetallic inclusions) indicated that the surface microstrain analysis could be a new method of high-throughput characterization for microregions with relatively poor micromechanical properties. In general, the effect of isostatic pressing on the surface microstrain of heat-resistant steel provides a promising strategy for achieving high-throughput screening and statistically characterizing microregions with poor micromechanical properties, such as microregions containing microcracks, nonmetallic inclusions, pores, and other surface defects.