When sapphire crystal is prepared with Kyropoulos method, the necking-down growth process is a key stage. Sapphire growth defect is a big problem in this stage. However, diagnosing growth defects is subject to the interference of workers subjectivity and accuracy always goes down. To address the problem, a novel defect diagnosis method is proposed for necking-down growth process in this paper. Industrial CCD sensors replace eyes of skilled workers to observe in this method. A new Defect-Diagnosing Siamese network (DDSN) is used in this method. We use Siamese architecture to learn similarity through pairs of images. We use the deep separable convolution (DSC) into the DDSN to optimize running speed and model size. In experiment, dataset is acquired by industrial CCD sensors in the necking-down growth process. The accuracy of defect diagnosis can reach up to 94.5%. The method significantly improves the traditional way.

中文翻译：

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基于泡生法的蓝宝石生长的一种新的缺陷诊断方法


采用泡生法制备蓝宝石晶体时，颈缩生长过程是一个关键阶段。蓝宝石生长缺陷是现阶段的一个大问题。但生长缺陷的诊断容易受到工作人员主观性的干扰，准确性往往会下降。为了解决这个问题，本文提出了一种新的颈缩生长过程缺陷诊断方法。这种方法是用工业CCD传感器代替技术工人的眼睛进行观察。该方法使用了一种新的缺陷诊断连体网络（DDSN）。我们使用 Siamese 架构通过图像对来学习相似性。我们在 DDSN 中使用深度可分离卷积 (DSC) 来优化运行速度和模型大小。在实验中，数据集是由工业CCD传感器在颈缩生长过程中获取的。缺陷诊断准确率可达94.5%。该方法对传统方法有显着改进。