|
|
|
|
|
|
| Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM |
| YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1 |
1. Civil Aircraft Composite Materials Research Center, Civil Aviation Flight University of China, Guanghan 618307, China
2. Laser and Optoelectronic Intelligent Manufacturing Research Institute, Wenzhou University, Wenzhou 325035, China
|
|
|
|
|
Abstract Online monitoring of the aircraft skin laser paint removal process is an important means to achieve layered and controllable paint removal and meet airworthiness maintenance requirements. It is also the key technology to promote the industrial application of laser paint removal and aircraft maintenance automation. Currently, the main monitoring methods include surface imaging and process performance parameter measurement methods. However, these methods have inherent limitations, making it difficult to be online and real-time. Laser-induced plasma breakdown spectroscopy (LIBS) technology has the advantages of equipment simplicity, flexibility, quickness and sensitivity, which has been widely used in online monitoring and research of laser cleaning of artworks and cultural relics. Based on the established high-frequency nanosecond infrared pulsed laser paint removal LIBS online monitoring platform, three LIBS spectra (100 frames each) were collected during the removal of topcoat, primer and aluminum alloy substrate under different laser powers. The changes of characteristic spectral lines of various spectral tracer elements under different laser powers were analyzed, and 12 characteristic spectral lines were preliminarily screened as the characteristics of spectral identification. Principal component analysis (PCA) was further performed on these 12 characteristics. The data set composed of the first three principal components (PC1, PC2 and PC3) was used as the input of the support vector machines (SVM) identification model, and the identification model of three types of spectral data was established. A LIBS online monitoring and judgment rule for the controllable removal process of laser layering of multi-paint-layer structure was formed, and the rule's validity was experimentally verified. It can be seen from the results that, compared with the needle-like LIBS spectra collected based on low-frequency pulsed laser single-point action, in general, the LIBS spectra collected based on this platform show a strong continuous background (greater than 5 000 a.u.) and a full width at half maximum of about 1.5 nm; an improved mean smoothing filtering algorithm was designed for this type of spectrum, which effectively avoids the intensity distortion of the characteristic spectral line while removing the background spectrum; under different laser powers, the characteristic spectral line of the tracer element is unstable; the contribution of the first three principal components, i.e., PC1, PC2, and PC3 in the principal component analysis to the explanation of the spectral data reaches 95%. The same type of spectra is clustered regionally in the three-dimensional space formed by them. The recognition accuracy of the PCA-SVM model on the training set and test set is 99.44% and 100%, respectively; the verification experimental results show that the identification models of the three types of spectra and the online monitoring and judgment rules are effective. The established identification model and judgment rules can provide key technical support for the monitoring and automation solutions of the aircraft skin laser layered paint removal process.
|
|
Received: 2022-07-17
Accepted: 2022-11-14
|
|
|
|
[1] WANG Zhen-liang, LI Feng, HUANG Feng(王振良, 李 锋, 黄 锋). Aviation Maintenance & Engineering(航空维修与工程), 2020, (9): 79.
[2] Papanikolaou A, Jtserevelakis G, Melessanaki K, et al. Opto-Electronic Advances, 2020, 3(2): 02190037.
[3] Chen Yin, Deng Guoliang, Zhou Qionghua, et al. Laser Physics, 2020, 30(6): 066001.
[4] Striova J, Fontana R, Barucci M, et al. Microchemical Journal, 2016, 124: 331.
[5] SHI Tian-yi, ZHOU Long-zao, WANG Chun-ming, et al(史天意, 周龙早, 王春明, 等). Chinese Journal of Lasers(中国激光), 2019, 46(4): 0402007.
[6] Senesi G S, Carrara I, Nicolodelli G, et al. Microchemical Journal, 2016, 124: 296.
[7] Wang Wenju, Sun Lanxiang, Lu Ying, et al. Optics & Laser Technology, 2022, 145: 107481.
[8] Veiko V, Samohvalov A, Ageev E. Optics & Laser Technology, 2013, 54: 170.
[9] Staicu A, Apostol I, Pascu A, et al. Optics & Laser Technology, 2016, 77: 187.
[10] TONG Yan-qun, ZHANG Ang, FU Yong-hong, et al(佟艳群, 张 昂, 符永宏, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(8): 2388.
[11] Zhou Qionghua, Deng Guoliang, Chen Yin, et al. Applied Optics, 2019, 58(34): 9421.
[12] SUN Lan-xiang, WANG Wen-ju, QI Li-feng, et al(孙兰香, 王文举, 齐立峰, 等). Chinese Journal of Lasers(中国激光), 2020, 47(11): 1111003.
[13] Zha Rongwei, Bai Yang, Yu Lidong, et al. Applied. Optics, 2022, 61(9): 2147.
[14] Li Xiaohui, Yang Sibo, Fan Rongwei, et al. Optics & Laser Technology, 2018, 102: 233.
[15] Sheng L W, Zhang T L, Niu G H, et al. Journal of Analytical Atomic Spectrometry, 2015, 30(2): 453.
[16] LIN Ze-hao, LI Run-hua, JIANG Yin-hua, et al(林泽浩, 李润华, 姜银花, 等). Chinese Journal of Lasers(中国激光), 2021, 48(24): 2411001.
|
| [1] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy （LIBS）[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
| [2] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [3] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
| [4] |
LI Chang-ming1, CHEN An-min2*, GAO Xun3*, JIN Ming-xing2. Spatially Resolved Laser-Induced Plasma Spectroscopy Under Different Sample Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2032-2036. |
| [5] |
ZHAO Yang1, ZHANG Lei2, 3*, CHENG Nian-kai4, YIN Wang-bao2, 3*, HOU Jia-jia5, BAI Cheng-hua1. Research on Space-Time Evolutionary Mechanisms of Species Distribution in Laser Induced Binary Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2067-2073. |
| [6] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
| [7] |
LI Wen-xia1, DU Yu-jun2, WANG Yue1, LIU Zheng-dong3*, ZHENG Jia-hui1, DU Wen-qian1, WANG Hua-ping4. Research on On-Line Efficient Near-Infrared Spectral Recognition and Automatic Sorting Technology of Waste Textiles Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2139-2145. |
| [8] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
| [9] |
WU Shu-jia1, 2, YAO Ming-yin2, 3, ZENG Jian-hui2, HE Liang2, FU Gang-rong2, ZENG Yu-qi2, XUE Long2, 3, LIU Mu-hua2, 3, LI Jing2, 3*. Laser-Induced Breakdown Spectroscopy Detection of Cu Element in Pig Fodder by Combining Cavity-Confinement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1770-1775. |
| [10] |
YUAN Shu, WU Ding*, WU Hua-ce, LIU Jia-min, LÜ Yan, HAI Ran, LI Cong, FENG Chun-lei, DING Hong-bin. Study on the Temporal and Spatial Evolution of Optical Emission From the Laser Induced Multi-Component Plasma of Tungsten Carbide Copper Alloy in Vacuum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1394-1400. |
| [11] |
WANG Qiu, LI Bin, HAN Zhao-yang, ZHAN Chao-hui, LIAO Jun, LIU Yan-de*. Research on Anthracnose Grade of Camellia Oleifera Based on the Combined LIBS and Fourier Transform NIR Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1450-1458. |
| [12] |
CHAI Shu1, PENG Hai-meng1, WU Wen-dong1, 2*. Acoustic-Based Spectral Correction Method for Laser-Induced Breakdown Spectroscopy in High Temperature Environment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1401-1407. |
| [13] |
NING Qian-qian, YANG Jia-hao, LIU Xiao-lin, HE Yu-han, HUANGFU Zhi-chao, YU Wen-jing, WANG Zhao-hui*. Design and Study of Time-Resolved Femtosecond Laser-Induced
Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1083-1087. |
| [14] |
DING Kun-yan1, HE Chang-tao2, LIU Zhi-gang2*, XIAO Jing1, FENG Guo-ying1, ZHOU Kai-nan3, XIE Na3, HAN Jing-hua1. Research on Particulate Contamination Induced Laser Damage of Optical Material Based on Integrated Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1234-1241. |
| [15] |
SU Yun-peng, HE Chun-jing, LI Ang-ze, XU Ke-mi, QIU Li-rong, CUI Han*. Ore Classification and Recognition Based on Confocal LIBS Combined With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 692-697. |
|
|
|
|
