Image enhancement method for laser infrared thermography defect detection in aviation composites

被引:11
|
作者
Wang, Qiang [1 ]
Hu, Qiuping [1 ]
Qiu, Jinxing [2 ]
Pei, Cuixiang [2 ]
Li, Xinyi [1 ]
Zhou, Hongbin [1 ]
Xia, Ruicong [1 ]
Liu, Jia [1 ]
机构
[1] Air Force Engn Univ, Xian, Shanxi, Peoples R China
[2] Xi An Jiao Tong Univ, Xian, Shaanxi, Peoples R China
关键词
laser infrared thermography; sequence differential preprocessing; aviation composites; internal defect; IMPACT DAMAGE DETECTION; LINE THERMOGRAPHY; CFRP; INSPECTION; CRACKS; PCA;
D O I
10.1117/1.OE.58.10.103104
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
A new infrared preprocessing modality called sequence differential processing is proposed. Different from the cold image subtraction which only decreases the background noise, the proposed modality takes the parallel test into account to preprocess the raw thermal data sequence prior to applying the advanced postprocessing techniques such as principal component thermography and pulsed phase thermography. The results show that the preprocessing method not only effectively reduces the influence of uneven laser energy distribution on detection efficiency but also enhances internal defect information. Moreover, the combination of the proposed preprocessing method with the pulsed phase thermography and principal component analysis algorithms improves the ability of laser infrared thermography to detect defects inside aviation carbon-fiber-reinforced plastics. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Laser infrared thermography detection for aviation carbon fibre composites
    Wang Qiang
    Hu Qiuping
    Liu Ming
    Yang Liu
    Li Xinyi
    Zhou Hongbin
    [J]. 2019 3RD INTERNATIONAL WORKSHOP ON RENEWABLE ENERGY AND DEVELOPMENT (IWRED 2019), 2019, 267
  • [2] Subsurface defect detection in FRP composites using infrared thermography
    Halabe, UB
    Vasudevan, A
    GangaRao, HVS
    Klinkhachom, P
    Lonkar, G
    [J]. Review of Progress in Quantitative Nondestructive Evaluation, Vols 24A and 24B, 2005, 760 : 1477 - 1484
  • [3] Defect detection and evaluation of ultrasonic infrared thermography for aerospace CFRP composites
    Yang, Bo
    Huang, Yaoda
    Cheng, Long
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2013, 60 : 166 - 173
  • [4] Defect Detection Method for CFRP Based on Line Laser Thermography
    Wang, Quan
    Zhang, Zhijie
    Yin, Wuliang
    Chen, Haoze
    Liu, Yushan
    [J]. MICROMACHINES, 2022, 13 (04)
  • [5] Defect detection in CFRP by infrared thermography with CO2 Laser excitation compared to conventional lock-in infrared thermography
    Keo, Sam Ang
    Brachelet, Franck
    Breaban, Florin
    Defer, Didier
    [J]. COMPOSITES PART B-ENGINEERING, 2015, 69 : 1 - 5
  • [6] Quantitative detection of defect size based on infrared thermography: temperature integral method
    Zhu, Pengfei
    Wu, Dan
    Yin, Lingxiao
    Han, Wei
    [J]. OPTICS EXPRESS, 2022, 30 (06): : 9119 - 9136
  • [7] Advanced image processing for defect visualization in infrared thermography
    Plotnikov, YA
    Winfree, WP
    [J]. THERMOSENSE XX, 1998, 3361 : 331 - 338
  • [8] Detection of internal defects in aviation composites with differential laser infrared thermal imaging
    Wang, Qiang
    Hu, Qiuping
    Qiu, Jinxing
    Pei, Cuixiang
    Liu, Ming
    Li, Xinyi
    Zhou, Hongbin
    [J]. Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2019, 48 (05):
  • [9] In-situ monitoring and defect detection for laser metal deposition by using infrared thermography
    Hassler, Ulf
    Gruber, Daniel
    Hentschel, Oliver
    Sukowski, Frank
    Grulich, Tobias
    Seifert, Lars
    [J]. LASER ASSISTED NET SHAPE ENGINEERING 9 INTERNATIONAL CONFERENCE ON PHOTONIC TECHNOLOGIES PROCEEDINGS OF THE LANE 2016, 2016, 83 : 1244 - 1252
  • [10] Stacked denoising autoencoder for infrared thermography image enhancement
    Wei, Ziang
    Fernandes, Henrique
    Tarpani, Jose Ricardo
    Osman, Ahmad
    Maldague, Xavier
    [J]. 2021 IEEE 19TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2021,