No abstract

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 λ¯ (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.

No abstract

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.

Nickel-based single crystal superalloy blades have excellent high-temperature performance as the hot end part of the aero-engine turbine. The most important strengthening phase in the single crystal blade is the γ’ phase, and its morphology and size distribution directly affect the high temperature performance of the single crystal blade. In this work, scanning electron microscopy (SEM) was used to obtain the microscopic images of the γ’ phase in multiple large continuous fields of view in the transverse sections of single crystal blades, and the quantitative statistical characterization of the γ’ phase was performed by image segmentation method based on deep learning. The 20 μm × 20 μm region was selected from the primary dendrite arm, the secondary dendrite arm, and the interdendrite to statistically analyze the γ’ phases. The statistical results show that the average size of the γ’ phase at the position of the interdendrite is significantly larger than the average size of the γ’ phase at the position of the dendrite; the sizes of the γ’ phase at the primary dendrite arm, the secondary dendrite arm and the interdendrite all obey the normal distribution; about 3.17 × 107 γ’ phases are counted in 20 positions in the 5 transverse sections of the single crystal blade in a total area of 5 mm2, and the size, geometric morphology and area fraction of all γ’ phases are respectively counted. In this work, the quantitative parameters of the γ’ phases at 4 different positions of the section of the single crystal superalloy DD5 blade were compared, the size and area fraction of the γ’ phases at the leading edge and the trailing edge were smaller, and the shape of the γ’ phase of the leading edge and the trailing edge is closer to the cube.