TY - JOUR
T1 - 基于激光诱导击穿光谱法的铝合金中 Mg 元素定量分析
AU - Yu, Ding
AU - Linyu, Yang
AU - Jing, Chen
AU - Xingyu, Wang
AU - Xiaoran, Guo
AU - Xuanchen, Xu
AU - Xingqiang, Zhao
AU - Yong, Luo
AU - Wenjie, Chen
N1 - Publisher Copyright:
© 2022 Universitat zu Koln. All rights reserved.
PY - 2022/7
Y1 - 2022/7
N2 - 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%.
AB - 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%.
KW - aluminium alloy
KW - laser optics
KW - laser-induced breakdown spectroscopy
KW - quantitative analysis
KW - random forests
UR - https://www.scopus.com/pages/publications/85133447330
U2 - 10.3788/LOP202259.1314006
DO - 10.3788/LOP202259.1314006
M3 - 文章
AN - SCOPUS:85133447330
SN - 1006-4125
VL - 59
JO - Laser and Optoelectronics Progress
JF - Laser and Optoelectronics Progress
IS - 13
M1 - 1314006
ER -