An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

Jia Zheng, Chuan Tang, Yuanxi Sun (), Mingchi Feng and Congzhe Wang
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Jia Zheng: School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Chuan Tang: School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Yuanxi Sun: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
Mingchi Feng: School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Congzhe Wang: School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Mathematics, 2022, vol. 10, issue 22, 1-16

Abstract: The hollow turbine blade plays an important role in the propulsion of the aeroengine. However, due to its complex hollow structure and nickel-based superalloys material property, only industrial computed tomography (ICT) could realize its nondestructive detection with sufficient intuitiveness. The ICT detection precision mainly depends on the segmentation accuracy of target ICT images. However, because the hollow turbine blade is made of special superalloys and contains many small unique structures such as film cooling holes, exhaust edges, etc., the ICT image quality of the hollow turbine blades is often deficient, with artifacts, low contrast, and inhomogeneity scattered around the blade contour, making it hard for traditional mathematical model-based methods to acquire satisfying segmentation precision. Therefore, this paper presents a deep learning-based approach, i.e., the enhanced U-net with multiscale inputs, dense blocks, focal loss function, and residual path in the skip connection to realize the high-precision segmentation of the hollow turbine blade. The experimental results show that our proposed enhanced U-net can achieve better segmentation accuracy for practical turbine blades than conventional U-net and traditional mathematical model-based methods.

Keywords: segmentation; computed tomography; U-net; hollow turbine blade (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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