Abstract

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.

Highlights

  • The nickel-based single crystal superalloy blade is widely used as the hot end part of the aero-engine and gas turbine [1,2,3]

  • This work uses the semantic segmentation network U-Net [22] based on deep learning [23,24] to quantitatively and statistically characterize the distribution of γ’ phases in a large area of different transverse sections and different parts of the second-generation single crystal blade DD5

  • The test material is the second-generation heat-treated nickel-based single crystal superalloy DD5 blade independently developed by CISRI-GAONA

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Summary

Introduction

The nickel-based single crystal superalloy blade is widely used as the hot end part of the aero-engine and gas turbine [1,2,3]. Chen [19] established the corresponding relationship between the area fraction and position of γ’ through the quantitative characterization of the γ’ phase in the typical regions of transverse sections with different heights, and studied the evolution and damage degree of the microstructure of different parts of the leaf This process only selects 5 images in each region as representative, which cannot represent the characteristic information of the entire region. The rapid quantitative extraction and statistical distribution characterization of the γ’ phase in the high-precision image of the large-size sample is of great significance for the study of the microstructure inhomogeneity of the single crystal blade. This work uses the semantic segmentation network U-Net [22] based on deep learning [23,24] to quantitatively and statistically characterize the distribution of γ’ phases in a large area of different transverse sections and different parts of the second-generation single crystal blade DD5. This method uses a feature extraction network to extract features, and automatically corrects feature extraction parameters in the iterative process, avoiding the subjective error of manually defining features, and the entire segmentation process is implemented in a highly parallel neural network, which greatly improves the calculation speed

Materials and Experimental Data
Method
Results
Microstructure in the Transverse Sections of the Single Crystal Blad
Conclusions
Full Text
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