In this research, we studied the influence of process parameters on the quality of selective laser melting of 18Ni300 maraging steel. The effects of laser power and scanning speed on the relative density and hardness of 18Ni300 were studied by single-factor experiment and the orthogonal experimental method. The relative optimal process parameters of 18Ni300 were obtained when the layer thickness was 0.03 mm, and the hatch space was 0.1 mm. The microstructures and mechanical properties of the samples formed under different process parameters were characterized. The results showed that the optimal hardness and relative density of the sample were 44.7 HRC and 99.98% when the laser power was 230 W and the scanning speed was 1100 mm/s, respectively; the microstructure of the material was uniform and dense, exhibiting few pores. Some columnar crystals appeared along the boundary of the molten pool due to vertical epitaxial growth. The orientation of fine grains at the boundary of the molten pool was random, and some coarse columnar crystals in the molten pool exhibited a certain orientational preference along the orientation. In the case of optimal process parameters, the SLM-formed 18Ni300 was composed of 99.5% martensite and 0.5% retained austenite; the indentation hardness was distributed in the range of 3.2–5 GPa. The indentation modulus was between 142.8–223.4 GPa, exhibiting stronger fluctuations than the indentation hardness. The sample’s mechanical properties showed obvious anisotropy, while the tensile fracture characteristics exhibited necking. The tensile fracture morphology was ductile, and large equiaxed dimples and holes could be observed in the fiber area, accompanied by tearing characteristics.

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 optimal SLM conditions of 304L stainless steel were obtained by single factor and orthogonal tests. Results indicated that the optimal hardness (75 HRB) and Relative Density (RD 99.24%) could be obtained when the laser output power was 190 W, the scanning distance was 0.09 mm and the scanning speed was 800 mm/s. The microstructure of fish scales was uniform and compact with a few pores in the optimal sample. The fine particles were randomly distributed near the edge of the molten pool, and some preferred granular columnar crystal structures were formed. Abundant entanglement dislocations were observed between cell structures, forming dislocation clusters. Spherical nano-precipitates, rich in Si, Mn, and O, were also observed near cell structures. The mechanical properties of the specimens were highly anisotropic, and there were obvious necking and ductility at the tensile fracture.

The quantitative study of the relationship between material composition, microstructure and properties is of great importance for the improvement in material properties. In this study, the continuous data of elemental composition, recrystallization, hardness and undissolved phase distribution of the same sample in the range of 60 to 150 square millimeters were obtained by high-throughput testing instrument. The distribution characteristics and rules of a single data set were analyzed. In addition, each data set was divided into micro-areas according to the corresponding relationship of location, and the mapping between multi-source heterogeneous micro-area data sets was established to analyze and quantify the correlation between material composition, structure and hardness. The conclusions are as follows: (1) the average size of the insoluble phase in the middle of the two materials is larger than that of the surface, but due to the existence of central segregation, the average area of the T4 insoluble phase showed an abnormal decrease; (2) there was positive micro-segregation of Al, Cr, Ti, and Zr elements, and negative micro-segregation of Zn, Cu, and Fe elements in the recrystallized grains of the T5 middle segregation zone; (3) the growth process of the insoluble phase was synchronous with the recrystallization proportion and the size of the recrystallized grains; (4) the composition segregation and recrystallized coarse grains were the main reasons for the formation of low hardness zone in T4 and T5 materials, respectively.