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首页> 外文期刊>Journal of Nondestructive Evaluation >Aluminum Casting Inspection Using Deep Learning: A Method Based on Convolutional Neural Networks
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Aluminum Casting Inspection Using Deep Learning: A Method Based on Convolutional Neural Networks

机译：铝制铸造检查采用深度学习：一种基于卷积神经网络的方法

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Castings produced for the automotive industry are considered important components for overall roadworthiness. To ensure the safety of construction, it is necessary to check every part thoroughly using non-destructive testing. X-ray testing rapidly became the accepted way of controlling the quality of die-cast pieces. In this paper, we present a Convolutional Neural Network (CNN) for defect detection in castings. In order to train the CNN model, a large dataset is necessary. We build the dataset by using synthetic defects. They are simulated using 3D ellipsoidal models and Generative Adversarial Networks (GAN). We compare different portions of ellipsoidal/GAN defects in the training subset. In our experiments, the use of GAN defects does not play a relevant role in this solution. However, ellipsoidal defects helped to achieve better performance. Ellipsoidal defects from any size and orientation could be superimposed onto real X-ray images in any location. In addition, we tested several CNN configurations, the best one, that we call Xnet-II, has 30 layers and more than 1,350,000 parameters. It has been trained using a dataset with around 640,000 patches containing 50% of ellipsoidal defects and 50% of real background captured from different casting types. The model was tested using sliding-windows methodology on whole X-ray images achieving promising results (mPA = 0.7102): the model was able to detect real defects from different casting types. We believe that the methodology presented could be used in similar projects that have to deal with automated detection of defects.
机译：为汽车工业生产的铸件被认为是整体可行性的重要组成部分。为确保建设的安全性，有必要使用非破坏性测试彻底检查每个部分。 X射线测试迅速成为控制压铸件质量的接受方式。在本文中，我们提出了一种卷积神经网络（CNN），用于铸件中的缺陷检测。为了训练CNN模型，需要大型数据集。我们使用合成缺陷构建数据集。它们使用3D椭圆模型和生成的对抗网络（GaN）进行模拟。我们在训练子集中比较不同部分的椭圆形/ GaN缺陷。在我们的实验中，使用GaN缺陷在该解决方案中没有发挥相关作用。然而，椭圆形缺陷有助于实现更好的性能。可以将来自任何尺寸和取向的椭圆缺陷叠加在任何位置的真实X射线图像上。此外，我们测试了几个CNN配置，最好的CNN配置，我们称之为XNET-II，具有30层和超过1,350,000个参数。它已经通过数据集进行培训，其中包含大约640,000个贴片，其中包含50％的椭圆缺陷和50％的实际背景从不同的铸造类型捕获。使用滑动窗口方法在实现有前途的结果的全X射线图像上进行测试（MPA = 0.7102）：该模型能够检测来自不同铸造类型的实际缺陷。我们认为，所提出的方法可以用于类似的项目，这些项目必须用于处理自动检测缺陷。

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