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    飞机典型结构冲击类损伤的智能检测和评估

    原赛男, 于闯, 陈少敏

    原赛男, 于闯, 陈少敏. 飞机典型结构冲击类损伤的智能检测和评估[J]. 无损检测, 2023, 45(5): 51-55. DOI: 10.11973/wsjc202305010
    引用本文: 原赛男, 于闯, 陈少敏. 飞机典型结构冲击类损伤的智能检测和评估[J]. 无损检测, 2023, 45(5): 51-55. DOI: 10.11973/wsjc202305010
    YUAN Sainan, YU Chuang, CHEN Shaomin. Intelligent detection and evaluation of impact damage of typical aircraft structures[J]. Nondestructive Testing, 2023, 45(5): 51-55. DOI: 10.11973/wsjc202305010
    Citation: YUAN Sainan, YU Chuang, CHEN Shaomin. Intelligent detection and evaluation of impact damage of typical aircraft structures[J]. Nondestructive Testing, 2023, 45(5): 51-55. DOI: 10.11973/wsjc202305010

    飞机典型结构冲击类损伤的智能检测和评估

    基金项目: 

    北京市科技计划(Z201100004520031)

    详细信息
      作者简介:

      原赛男(1986-),女,博士,高级工程师,主要从事航空智能检测技术研究工作

      通讯作者:

      原赛男, E-mail:yuansainan@comac.cc

    • 中图分类号: TG115.28

    Intelligent detection and evaluation of impact damage of typical aircraft structures

    • 摘要: 以飞机碳纤维增强复合材料典型结构的低能量冲击损伤为切入点,根据无损检测缺陷评估需求,构建航空复合材料冲击损伤专业检测人员标注数据集,开展高效的人工智能辅助检测算法工具研究,对超声图像进行数据增强,训练典型航空复合材料结构超声冲击损伤目标检测模型,实现冲击类损伤的智能检测和评估。
      Abstract: The low energy impact damage of typical carbon fiber reinforced composite structures was taken as a breakthrough point. According to the nondestructive testing requirement of defect evaluation, data set of aviation composite impact damage labelled by professionals was constructed. The research of an efficient artificial intelligent assistant inspection tool was conducted to enhance the data of ultrasonic images, train the target detection model of ultrasonic impact damages, and achieve intelligent detection and evaluation of impact damages.
    • [1] 宁荣昌.复合材料冲击损伤问题的研究现状[J].玻璃钢/复合材料, 1992(6):35-40, 34.
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      WYRICK D A, ADAMS D F.Residual strength of a carbon/epoxy composite material subjected to repeated impact[J].Journal of Composite Materials, 1988, 22(8):749-765.

      [3]

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      [4] 张风翻.树脂增韧及韧性复合材料[J].材料工程, 1995, 23(5):3-6.
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      QIN X, ZHANG Z, HUANG C, et al. U2-Net:Going deeper with nested U-structure for salient object detection[J]. Pattern Recognition, 2020, 106:107404.

      [6] 陈其浩, 孙林, 张倩.基于改进U2-Net的透明件划痕检测方法[J].科学技术与工程, 2022, 22(2):620-627.
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    • 被引次数: 0
    出版历程
    • 收稿日期:  2022-10-18
    • 刊出日期:  2023-05-09

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