A Novel Defect Diagnosis Method for Kyropoulos Process Based Sapphire Growth

Journal article (2020)

Authors

Wei Zhang Taiyuan University of Technology
Tiezhu Qiao Taiyuan University of Technology
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Yi Yang Taiyuan University of Technology
Hong Chen Shanxi Zhongjujingke Semiconductor Co.
Guirong Hao Shanxi Zhongjujingke Semiconductor Co.
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2020 Wei Zhang, Tiezhu Qiao, Y. Pang, Yi Yang, Hong Chen, Guirong Hao
DOI
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https://doi.org/10.1109/JSEN.2020.2969963
CCD sensor Defect-Diagnosing Siamese network Necking-down process Sapphire Growth Defects
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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

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.