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基于激光诱导击穿光谱法的铝合金中 Mg 元素定量分析

  • Ding Yu
  • , Yang Linyu
  • , Chen Jing
  • , Wang Xingyu
  • , Guo Xiaoran
  • , Xu Xuanchen
  • , Zhao Xingqiang
  • , Luo Yong
  • , Chen Wenjie
  • Nanjing University of Information Science & Technology
  • The No. 32181st Troop of PLA

科研成果: 期刊稿件文章同行评审

7 引用 (Scopus)

摘要

Mg element can make the aluminum alloy to obtain better mechanical properties and form a corrosion- resistant spinel film on the surface of the alloy, so that the alloy has better corrosion resistance. Therefore, exploring a method that can quickly and accurately detect the content of magnesium in aluminum alloy quantitatively is of great significance. In this paper, first, the Mg element in 17 aluminum alloy samples is detected and analyzed based on laser- induced breakdown spectroscopy (LIBS) technology. Then, Nd: YAG laser is used as light source, and the partial least squares (PLS) and random forest (RF) models are respectively established, and the prediction performance of the models is analyzed. The experimental results show that for the same test set, the correlation coefficient (R2p) of the PLS model is 0. 6809, and the root mean square error (RMSE) is 1. 2042; the R2p of the RF model is 0. 8571 and the RMSE is 1. 0918. In order to improve the prediction accuracy of the random forest model, this experiment screened the input variables according to the importance of the variables. When the wavelength point with variable importance greater than 0. 11 is selected, R2p of the RF model based on variable importance is 0. 9461, and the RMSE is 0. 9534. Compared with the prediction result of the RF model, R2p is increased by 10. 38%, RMSE is reduced by 12. 68%, and the modeling time is reduced by 91. 67%.

投稿的翻译标题Quantitative Analysis of Mg Element in Aluminium Alloy Based on Laser-Induced Breakdown Spectroscopy
源语言繁体中文
文章编号1314006
期刊Laser and Optoelectronics Progress
59
13
DOI
出版状态已出版 - 7月 2022
已对外发布

关键词

  • aluminium alloy
  • laser optics
  • laser-induced breakdown spectroscopy
  • quantitative analysis
  • random forests

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