Lijian Pan, Weifang Chen, Rongfang Cui, Miaomiao Li. Quantitative Analysis of Aluminum Alloy Based on Laser-Induced Breakdown Spectroscopy and Radial Basis Function Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(19): 193002
- Laser & Optoelectronics Progress
- Vol. 57, Issue 19, 193002 (2020)
Fig. 1. Structure of neural network
Fig. 2. Schematic of LIBS experimental system
Fig. 3. LIBS spectrum of aluminum alloy
Fig. 4. Univariate linear calibration curves of five main nonaluminum elements. (a) Mg; (b) Si; (c) Fe; (d) Mn; (e) Cu
Fig. 5. Effect of vspread on the performance of RBF model
Fig. 6. Prediction of five main nonaluminum elements by RBF neural networks. (a) Mg; (b) Si; (c) Fe; (d) Mn; (e) Cu
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Table 1. Content of main non-Al elements in different standard samples of aluminum alloy
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Table 2. Spectral lines for analysis
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Table 3. Quantitative analysis results calculated by different models for standard samples of aluminum alloy
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