欢迎访问沈阳真空杂志社 Email Alert    RSS服务

真空 ›› 2025, Vol. 62 ›› Issue (5): 44-52.doi: 10.13385/j.cnki.vacuum.2025.05.07

• 薄膜 • 上一篇    下一篇

多维注意力机制驱动的航天器柔性热控镀膜表面缺陷检测方法

倪俊1,2, 郭腾1,2, 李灿伦1,2, 何恒扬3, 李荣义3   

  1. 1.上海卫星装备研究所,上海 200240;
    2.上海航天裕达科技有限公司,上海 200240;
    3.哈尔滨理工大学 先进制造智能化技术教育部重点实验室,黑龙江 哈尔滨 150080
  • 收稿日期:2024-12-02 发布日期:2025-09-29
  • 通讯作者: 李灿伦,研究员。
  • 作者简介:倪俊(1982-),女,四川北川人,高级工程师,博士。

Surface Defect Detection Method of Spacecraft Flexible Thermal Control Coating Driven by Multidimensional Attention Mechanism

NI Jun1,2, GUO Teng1,2, LI Canlun1,2, HE Hengyang3, LI Rongyi3   

  1. 1. Shanghai Institute of Spacecraft Equipment, Shanghai 200240, China;
    2. Shanghai Aerospace Yuda Technology Co., Ltd., Shanghai 200240, China;
    3. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
  • Received:2024-12-02 Published:2025-09-29

摘要: 柔性热控薄膜作为多层隔热组件包覆于航天器外表面,基于自身热物理特性实现对航天器表面热平衡的控制。在其磁控溅射制备过程中,工艺不稳定性、张力控制等问题常导致各种镀膜缺陷的出现,进而影响镀膜质量。传统磁控溅射镀膜缺陷需要停机检测,检测效率低、反应不及时。针对此问题,提出一种多维注意力机制驱动的图像检测深度学习算法,构建了基于监测数据-检测图像敏感特征-镀膜缺陷的关联映射模型,以实现缺陷的高效、高准确率智能辨识。经实验验证,模型对破裂、电火花、漏镀三种缺陷的识别精度达到96%以上,为实现航天器柔性热控镀膜在线检测奠定了坚实基础。

关键词: 热控薄膜, 磁控溅射, 多维注意力机制, 智能辨识

Abstract: Flexible thermal control coatings are applied to the outer surface of spacecraft to control the thermal balance of the spacecraft surface based on their own thermo-physical properties. During the magnetron sputtering process, process instability often leads to various defects in the coating, which in turn affects the coating quality. Traditional magnetron sputtering coating defect detection requires shutdown for inspection, which is inefficient and does not respond in a timely manner. This paper proposes a deep learning algorithm for image detection driven by a multi-dimensional attention mechanism, constructing a correlation mapping model between monitoring data, detection image sensitive features, and coating defects, to achieve efficient and high-accuracy intelligent identification. The experimental verification shows that the model recognition accuracy for cracks, electric sparks, and underplating defects is above 96%, laying a solid foundation for the online detection of flexible thermal control coatings on spacecrafts.

Key words: thermal control coating, magnetron sputtering, multi-dimensional attention mechanism, intelligent identification

中图分类号:  TB43

[1] 张东,张有玮,张家强,等. 低吸收高发射热控涂层及填料发展展望[J]. 新技术新工艺,2021,401(5):1-6.
[2] HOŁYŃSKA M, TIGHE A, SEMPRIMOSCHNIG C. Coatings and thin films for spacecraft thermos-optical and related functional applications[J]. Advanced Materials Interfaces, 2018, 5(11): 1701644.
[3] DUNN B D.Materials and processes: for spacecraft and high reliability applications[M]. Switzerland: Springer, 2016:667.
[4] WANG X J, LIU S H, ZHANG H, et al.Defects detection of lithium-ion battery electrode coatings based on background reconstruction and improved canny algorithm[J]. Coatings, 2024, 14(4): 392.
[5] ZHANG H, GAO B, TIAN G Y, et al.Metal defects sizing and detection under thick coating using microwave NDT[J]. NDT & E International, 2013, 60: 52-61.
[6] PATIL S S, SHALIGRAM A D.On-line defect detection of aluminum coating using fiber optic sensor[J]. Photonic Sensors, 2015, 5:72-78.
[7] WIN M, BUSHROA A R, HASSAN M A, et al.A contrast adjustment thresholding method for surface defect detection based on mesoscopy[J]. IEEE Transactions on Industrial Informatics, 2017, 11(3):642-649.
[8] LUO M T, ZHONG S C, HUANG Y, et al.Combined terahertz pulsed imaging and optical coherence tomography detection method for multiple defects in thermal barrier coatings[J]. Coatings, 2024, 14(4): 380.
[9] LIN E, WANG H D, DONG L H, et al.Surface crack detection of the abradable seal coating by laser bidirectional scanning thermography[J]. Infrared Physics & Technology, 2023, 128: 104498.
[10] LEE I G, YOON Y J, CHOI K S, et al.Design of an optical transparent absorber and defect diagnostics analysis based on near-field measurement[J]. Sensors, 2021, 21(9): 3076.
[11] HE D, KUSANO M, WATANABE M.Detecting the defects of warm-sprayed Ti-6Al-4V coating using Eddy current testing method[J]. NDT & E International, 2022, 125:102565.
[12] WANG H, HSIEH S J, ZHOU X, et al.Using active thermography to inspect pin-hole defects in anti-reflective coating with k-mean clustering[J]. Ndt & E International, 2015, 76: 66-72.
[13] TANG Q J, DAI J M, BU C W, et al.Experimental study on debonding defects detection in thermal barrier coating structure using infrared lock-in thermographic technique[J]. Applied Thermal Engineering, 2016, 107:463-468.
[14] ZHANG Z H, SHI T T, HUANG Y, et al.Defect detection method for self-lubricating sliding bearing coating using terahertz total variation image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 74: 4500115.
[15] XIA D H, SONG Y, SONG S Z, et al.Identifying defect levels in organic coatings with electrochemical noise (EN) measured in single cell (SC) mode[J]. Progress in Organic Coatings, 2019, 126: 53-61.
[16] 雷震霆,朱兴龙,孙进,等. 融合坐标注意力和自适应特征的YOLOv5陶瓷膜缺陷检测方法[J]. 电子测量技术,2023,46(7):133-137.
[17] TANG K, ZI B, XU F, et al.Coating defect detection method based on data augmentation and network optimization design[J]. IEEE Sensors Journal, 2023, 23(13): 14522-14533.
[18] TAO X R, GAO H J, WU Q, et al.Detection of Defects in Adhesive Coating Based on Machine Vision[J]. IEEE Sensors Journal, 2023, 24(4): 5172-5185.
[19] XIAO D, XIE F T, GAO Y, et al.A detection method of spangle defects on zinc-coated steel surfaces based on improved YOLO-v5[J]. The International Journal of Advanced Manufacturing Technology, 2023, 128(1-2): 937-951.
[20] YU Y, HOSHYAR A N, SAMALI B, et al.Corrosion and coating defect assessment of coal handling and preparation plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion[J]. Neural Computing and Applications, 2023, 35(25): 18697-18718.
[21] CHANG F, LIU M, DONG M Y, et al.A mobile vision inspection system for tiny defect detection on smooth car-body surfaces based on deep ensemble learning[J]. Measurement Science and Technology, 2019, 30(12): 125905.
[22] ZHOU W J, WANG Z J, WANG L W, et al.Wind turbine actual defects detection based on visible and infrared image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-8.
[23] ZUBAYER M H, ZHANG C Q, LIU W, et al.Automatic defect detection of jet engine turbine and compressor blade surface coatings using a deep learning-based algorithm[J]. Coatings, 2024, 14(4):501.
[24] TAO X R, GAO H J, YANG K, et al.Expanding the defect image dataset of composite material coating with enhanced image-to-image translation[J]. Engineering Applications of Artificial Intelligence, 2024, 133: 108590.
[25] YAN A, RUPNOWSKI P, GUBA N, et al.Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials[J]. International Journal of Hydrogen Energy, 2023, 48(50): 18978-18995.
[26] MARTÍNEZ S S, VÁZQUEZ C O, GARCÍA J G, et al. Quality inspection of machined metal parts using an image fusion technique[J]. Measurement, 2017, 111: 374-383.
[27] LEE Y, YUN J, LEE S, et al.Image data-centric visual feature selection on roll-to-roll slot-die coating systems for edge wave coating defect detection[J]. Polymers, 2024, 16(8): 1156.
[1] 赵颖, 刘沅东, 林冰, 张海龙. 磁控溅射In2Se3薄膜缓冲层性能研究*[J]. 真空, 2025, 62(5): 53-57.
[2] 倪俊, 郭腾, 李灿伦, 侯凯霖, 李荣义. 基于深度学习的真空镀膜生产缺陷检测方法研究[J]. 真空, 2025, 62(4): 69-74.
[3] 罗军文. 超薄柔性基材真空双面磁控溅射卷绕镀铜关键技术研究[J]. 真空, 2025, 62(3): 53-57.
[4] 孙冰成, 张贤旺, 张健. 射频功率对ITO薄膜结构及性能影响的研究[J]. 真空, 2025, 62(2): 62-67.
[5] 陈玉云, 王晓旭, 陈远明, 沈奕, 黄锐. 磁控溅射氧化硅和氧化硅/氮化硅/氧化硅薄膜绝缘性能的研究*[J]. 真空, 2024, 61(6): 15-20.
[6] 白皓宇, 姚春龙, 董明, 秦瑞, 白永浩, 王奕楠. 超高陡度长波通拉曼滤光片的研制[J]. 真空, 2024, 61(4): 12-16.
[7] 赵凡, 项燕雄, 邹长伟, 于云江, 梁枫. 磁控溅射镀膜技术在(Cr,Ti,Al)N涂层上的应用*[J]. 真空, 2024, 61(4): 22-29.
[8] 纪建超, 颜悦, 哈恩华. 沉积参数对TiO2纳米薄膜的显微结构和光学性能的影响*[J]. 真空, 2024, 61(3): 57-62.
[9] 李灿伦, 倪俊, 郭腾, 韩玮, 王飞, 祁松松, 李辉, 范孝鹏, 范秋林. 无色聚酰亚胺薄膜二次表面镜的光学特性研究[J]. 真空, 2024, 61(3): 70-73.
[10] 李灿民, 董中林, 夏正卫, 张心凤, 魏荣华. 等离子增强磁控溅射制备TiCr基纳米复合涂层的显微组织和性能[J]. 真空, 2024, 61(2): 10-15.
[11] 刘文丽, 刘旭, 尹翔. 动态磁场矩形平面磁控靶开发[J]. 真空, 2023, 60(5): 47-50.
[12] 李灿民, 张心凤, 魏荣华. 等离子增强磁控溅射制备TiCr基纳米复合涂层的耐冲蚀耐腐蚀性能[J]. 真空, 2023, 60(5): 37-41.
[13] 张艳鹏, 曹志强, 付强, 曹磊, 刘旭. 卷绕镀铜工艺对复合集流体电学性能影响研究[J]. 真空, 2023, 60(4): 8-12.
[14] 黄传鑫, 辛纪英, 田中俊, 王猛, 吕凯凯, 梁兰菊, 刘云云. 氧气等离子体处理提升InZnO材料及TFT电学性能和稳定性研究*[J]. 真空, 2023, 60(4): 24-28.
[15] 余康元, 何玉丹, 杨波, 罗江山. 溅射电压对高功率脉冲磁控溅射Cu箔微观结构及性能的影响*[J]. 真空, 2023, 60(3): 1-4.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 李得天, 成永军, 张虎忠, 孙雯君, 王永军, 孙 健, 李 刚, 裴晓强. 碳纳米管场发射阴极制备及其应用研究[J]. 真空, 2018, 55(5): 1 -9 .
[2] 周彬彬, 张 建, 何剑锋, 董长昆. 基于 CVD 直接生长法的碳纳米管场发射阴极[J]. 真空, 2018, 55(5): 10 -14 .
[3] 柴晓彤, 汪 亮, 王永庆, 刘明昆, 刘星洲, 干蜀毅. 基于 STM32F103 单片机的单泵运行参数数据采集系统[J]. 真空, 2018, 55(5): 15 -18 .
[4] 李民久, 熊 涛, 姜亚南, 贺岩斌, 陈庆川. 基于双管正激式变换器的金属表面去毛刺 20kV 高压脉冲电源[J]. 真空, 2018, 55(5): 19 -24 .
[5] 刘燕文, 孟宪展, 田 宏, 李 芬, 石文奇, 朱 虹, 谷 兵, 王小霞 . 空间行波管极高真空的获得与测量[J]. 真空, 2018, 55(5): 25 -28 .
[6] 徐法俭, 王海雷, 赵彩霞, 黄志婷. 化学气体真空 - 压缩回收系统在环境工程中应用研究[J]. 真空, 2018, 55(5): 29 -33 .
[7] 谢元华, 韩 进, 张志军, 徐成海. 真空输送的现状与发展趋势探讨(五)[J]. 真空, 2018, 55(5): 34 -37 .
[8] 孙立志, 闫荣鑫, 李天野, 贾瑞金, 李 征, 孙立臣, 王 勇, 王 健, 张 强. 放样氙气在大型收集室内分布规律研究[J]. 真空, 2018, 55(5): 38 -41 .
[9] 黄 思 , 王学谦 , 莫宇石 , 张展发 , 应 冰 . 液环压缩机性能相似定律的实验研究[J]. 真空, 2018, 55(5): 42 -45 .
[10] 常振东, 牟仁德, 何利民, 黄光宏, 李建平. EB-PVD 制备热障涂层的反射光谱特性研究[J]. 真空, 2018, 55(5): 46 -50 .