|
|
|
|
|
|
| The Accuracy Improvement of Fe Element in Aluminum Alloy by Millisecond Laser Induced Breakdown Spectroscopy Under Spatial Confinement Combined With Support Vector Machine |
| QIN Shuang1, LI Ming-liang1, DAI Yu-jia1, GAO Xun1*, SONG Chao2*, LIN Jing-quan1 |
1. School of Science, Changchun University of Science and Technology，Changchun 130022, China
2. School of Chemistry and Environmental Engineering, Changchun University of Science and Technology, Changchun 130022, China
|
|
|
|
|
Abstract The content of Fe will affect the hardness of the aluminum alloy, and further affect the working life of aluminum alloy devices. Therefore, the detection accuracy of Fe content in the aluminum alloy is very important. In this paper, the detection accuracy improvement of Fe element in the aluminum alloy by millisecond laser-induced breakdown spectroscopy (ms-LIBS) under spatial confinement combined with support vector machine (SVM) was employed. Under the confinement of the plate space, the millisecond laser-induced aluminum plasma spectrum achieves spectral enhancement and the plasma radiation spectrum stability was improved. The spectral enhancement factors of the four characteristic spectral lines Fe Ⅰ 345.99 nm, Fe Ⅰ 369.51 nm, Al Ⅰ 394.40 nm, and Al Ⅰ 396.15 nm are 2.20, 2.14, 2.28, and 2.41, respectively. The calibration models for quantitative analysis of Fe in aluminum alloy based on external standard method and SVM were established. The standard external method was used to calculate the R2, RMSEC, RMSEP, and ARE of the Fe element with the plate space constraint of 0.893, 0.261 Wt%, 0.156 Wt%, 40.977%, and R2, RMSEC, RMSEP, and ARE without the plate space constraint were 0.852, 0.337 Wt%, 0.274 Wt%, 42.947% respectively. Under the constraints, the RMSEC of the SVM model was 0.086 Wt%, and the RMSEP was 0.043 Wt%. The SVM method was used to calculate the R2, RMSEC, RMSEP, and ARE of the Fe element with the plate space constraint of 0.984, 0.086 Wt%, 0.043 Wt%, 3.715%, and R2, RMSEC, RMSEP, and ARE without the plate space constraint were 0.941, 0.134 Wt%, 0.051 Wt%, and 12.353%. The results show that under space constraints, the use of ms-LIBS combined with the SVM method can greatly improve the quantitative analysis accuracy and experimental repeatability of ms-LIBs, and effectively reduce the matrix effect of aluminum alloy, which can meet the trace elements of aluminum alloy Quick check.
|
|
Received: 2020-12-29
Accepted: 2021-01-21
|
|
|
|
Corresponding Authors:
GAO Xun, SONG Chao
|
|
[1] Jinwoo Lee, Hyuk Jong Bong, Daeyong Kim, et al. Journal of Metals, 2019, 71(12): 4393.
[2] Chia-Ting Chen, Daniela Banaru, Thierry Sarnet, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2018, 150: 77.
[3] Zhang Yuqing, Sun Chen, Gao Liang, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2020, 166: 105802.
[4] Liu Xiaodan, Liu Fei, Huang Weihao, et al. Molecules, 2018, 23(10): 2492.
[5] LIN Xiao-mei, HUANG Yu-tao, LIN Jing-jun, et al(林晓梅, 黄玉涛, 林京君，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(5): 1523.
[6] Fu Yangting, Hou Zongyu, Wang Zhe. Optics Express, 2016, 24(3): 3055.
[7] Wang Ying, Chen Anmin, Wang Qiuyun, et al. Plasma Science and Technology, 2019, 21(3): 102.
[8] Shen X K, Sun J, Ling H, et al. Journal of Applied Physics, 2007, 102(9): 093301.
[9] Guo L B, Li C M, Hu W, et al. Applied Physics Letters, 2011, 98(13): 131501.
[10] Gao Xun, Liu Lu, Song Chao, et al. Journal of Physics D: Applied Physics, 2015, 48: 175205.
[11] Zhao Shangyong, Gao Xun, Chen Anmin, et al. Applied Physics B, 2019, 126(1): 7.
[12] LI He, CAI Ji-xing, TAN Yong, et al(李 贺, 蔡继兴, 谭 勇，等). Acta Optica Sinica(光学学报), 2016, 36(2): 171.
[13] Li Haochen, Huang Meizhen, Xu Huidi. Optics Express, 2020, 28(2): 2142.
[14] SHA Wen, LI Jiang-tao, LU Cui-ping, et al(沙 文, 李江涛, 鲁翠萍，等). Chinese Journal of Laser(中国激光), 2020, 47(5): 514.
[15] Zakuskin A S, Popov A M, Zorov N B, et al. Technical Physics Letters, 2018, 44(1): 73.
|
| [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] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [4] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [5] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [6] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [7] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [8] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
| [9] |
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. |
| [10] |
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. |
| [11] |
LIU Pan1, 2, 3, DU Mi-fang1*, LI Bin1, LI Jing-bin1, ZENG Lei1, LIU Guo-yuan1, ZHANG Xin-yao1, 4, ZHA Xiao-qin1, 4. Determination of Trace Tellurium Content in Aluminium Alloy by
Inductively Coupled Plasma-Atomic Emission Spectrometry Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3125-3131. |
| [12] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
| [13] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [14] |
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1. Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2894-2901. |
| [15] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
|
|
|
|
