The near-net-shaped blade is adopted in the aero engine as it’s material-saving and efficient. However, the leading edge shape’s curvature is sharply changed in its machining process and the deformation trend of each cross section has slight differences. Using the traditional machining method is exhausting and time-consuming. Furthermore, it brings more errors during the whole machining process. Therefore, adaptive machining is imported in the machining of the near-net-shaped blade and the leading edge is to be reconstructed during this process. Besides, it is necessary to know whether the reconstructed leading edge is qualified. To address these two issues, a novel approach is proposed to discriminate and classify leading edges. In this paper, we trained a style transform model of generative adversarial networks with theoretical leading edges and used its discriminator network to evaluate the similarity of reconstructed leading edges we had accomplished in our previous work to establish a standard for the qualified reconstructed leading edge. Then, as the curvature of the near-net-shaped blade changes sharply and has complex features, which requires high accuracy of classification, different DenseNet models were adopted to classify whether these reconstructed images are qualified. We experimented on our LDEG dataset and the highest accuracy on the test set was 88.7%. The experiment results demonstrated that the proposed method is effective in evaluating and classifying leading edges in the machining process.

航空发动机采用近净形叶片，节省材料，效率高。然而，前缘形状的曲率在其加工过程中发生了急剧的变化，每个截面的变形趋势也略有不同。使用传统的加工方法既费力又费时。此外，它在整个加工过程中带来了更多的误差。因此，在近净形叶片的加工中引入自适应加工，并在此过程中重建前缘。此外，还需要知道重建的前沿是否合格。为了解决这两个问题，提出了一种新的方法来区分和分类前沿。在本文中，我们训练了一个具有理论前沿的生成对抗网络的风格变换模型，并使用其鉴别器网络来评估我们在之前的工作中完成的重建前沿的相似性，以建立合格的重建前沿的标准。然后，由于近净形叶片曲率变化剧烈，特征复杂，对分类精度要求高，采用不同的DenseNet模型对这些重建图像是否合格进行分类。我们在 LDEG 数据集上进行了实验，测试集的最高准确率为 88.7%。实验结果表明，所提出的方法对加工过程中的前缘进行评估和分类是有效的。