| Citation: | WANG Donghuan, YU Aiyang, XIAO Hong. Defect detection method for casting turbine blades in aeroengines based on unsupervised learning[J]. Journal of Aerospace Power, 2025, 40（6）：20230800 doi: 10.13224/j.cnki.jasp.20230800
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To achieve the automation and intelligence of the radiographic inspection of turbine blades in aeroengines, and to effectively address the time-consuming, labor-intensive, and inefficient problems in traditional radiographic inspection methods, a research initiative was undertaken to develop a defect detection method for X-ray images of turbine blade based on unsupervised learning. A defect inspection algorithm suitable for X-ray images of aeroengine turbine blades was proposed based on an unsupervised generative adversarial network. It consisted of a generator network, a discriminator network, and an extra encoder network. Reconstruction, discrimination, encoding, and intermediate encoding loss were designed, and the weighted sum of the four losses was used to construct the objective function. Using non-defective X-ray images for model training. A defect inspection model for X-ray images of aeroengine turbine blades was established based on the trained generator network. The effects of input image size, encoding size, and type of reconstruction loss on the performance of the defect detection model were studied. Results showed that the proposed model with an input image size of 128 pixel×128 pixel, 600 encoding size, and
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