Effect of the distance between focusing lens and target surface on quantitative analysis of Mn element in aluminum alloys by using filament-induced breakdown spectroscopy*

Lu, Xue-Tong; Zhao, Shengzhi; Gao, Xun; Guo, Kang; Lin, Jingquan

doi:10.1088/1674-1056/abb3ef

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…In our previous work, we found that the quantitative analysis accuracy and spectral stability of the filament area are better than the pre-and post-filament areas. [26] The intensity clamping effect of the filament leads to better laser power stability, sensitivity, and radiation plasma stability in the filament area. The filament carries a small part of the laser energy, and most of the energy is released into the energy reservoir around the filament.…”

Section: Results Of Svm/knn Combined Pcamentioning

confidence: 99%

“…This results in changes in laser-induced plasma spectral radiation characteristics, ablation efficiency, and FIBS analysis performance. [24,25] In our previous work, [26] we studied the effect of the distance between focusing lens and target surface on quantitative analysis of Mn in aluminum alloys by using FIBS. It is found that the distance between the focusing lens and target surface affects spectral stability and accuracy of quantitative analysis, and the filament is better than the pre-and post-filaments.…”

Section: Introductionmentioning

confidence: 99%

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Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Li¹,

Lu²,

Zhang³

et al. 2022

Chinese Phys. B

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Filament-induced breakdown spectroscopy (FIBS) combined with machine learning algorithms was used to identify five aluminum alloys. To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys, principal component analysis (PCA) combined with support vector machine (SVM) and K-nearest neighbor (KNN) was used. The intensity and intensity ratio of fifteen lines of six elements (Fe, Si, Mg, Cu, Zn, and Mn) in the FIBS spectrum were selected. The distances between the focusing lens and the target surface in the pre-filament, filament, and post-filament were 958 mm, 976 mm, and 1000 mm, respectively. The source data set was fifteen spectral line intensity ratios, and the cumulative interpretation rates of PC1, PC2, and PC3 were 97.22%, 98.17%, and 95.31%, respectively. The first three PCs obtained by PCA were the input variables of SVM and KNN. The identification accuracy of the different positions of focusing lens and target surface was obtained, and the identification accuracy of SVM and KNN in the filament was 100% and 90%, respectively. The source data set of the filament was obtained by PCA for the first three PCs, which were randomly selected as the training set and test set of SVM and KNN in 3:2. The identification accuracy of SVM and KNN was 97.5% and 92.5%, respectively. The research results can provide a reference for the identification of aluminum alloys by FIBS.

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Section: Results Of Svm/knn Combined Pcamentioning

confidence: 99%