基于深度学习的真空镀膜生产缺陷检测方法研究

doi:10.13385/j.cnki.vacuum.2025.04.13

Abstract

Abstract: Flexible thermal control coating is the key for controlling the surface thermal balance of spacecraft. During the vacuum magnetron sputtering preparation process, various coating defects often occur due to real-time process fluctuations of the equipment. The traditional manual defect detection methods have the problems of relying on manual experience, low efficiency, high labor intensity, low accuracy, and poor real-time performance. To ensure the quality and efficiency of coating production, a coating defect detection model based on efficient adaptive convolution and channel space pyramid pooling was proposed, and the optimization design method of each key module was introduced in detail. The results show that this model can autonomously learn and model multi-layer representations of potential distributions, and extract features of the detected targets layer by layer through deep neural networks. The average recognition accuracy for 8 types of defects reaches about 93%, and the recognition speed FPS increases to 140. This method can effectively improve the quality of product production and meet the demand for efficient mass production.

Key words: thermal control coating, vacuum coating, deep learning, defect detection

CLC Number: TB43

NI Jun, GUO Teng, LI Canlun, HOU Kailin, LI Rongyi. The Surface Defect Detection Method of Vacuum Coating Production Based on Deep Learning[J].VACUUM, 2025, 62(4): 69-74.

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The Surface Defect Detection Method of Vacuum Coating Production Based on Deep Learning

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