Identification of Aluminum Alloy Grades by Fiber-Laser Induced Breakdown Spectroscopy Combined with Support Vector Machine

Zhonghan Zhou; Xueyong Tian; Lanxiang Sun; Peng Zhang; Zhiwei Guo; Lifeng Qi

doi:10.3788/LOP55.063002

Laser & Optoelectronics Progress, Volume. 55, Issue 6, 063002(2018)

Identification of Aluminum Alloy Grades by Fiber-Laser Induced Breakdown Spectroscopy Combined with Support Vector Machine

Zhonghan Zhou^{1,2、1; 2;}, Xueyong Tian^3、3;, Lanxiang Sun^1、1;, Peng Zhang^{1,2、1; 2;}, Zhiwei Guo^{1,4、1; 4;}, and Lifeng Qi^1、1;

Author Affiliations

¹ Shenyang Institute Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Central Research Institute, SIASUN Robot and Automation Co., Ltd., Shenyang, Liaoning 110168, China

⁴ College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China

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Figures & Tables(11)

Fig. 1. Fiber-LIBS experimental system

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Fig. 2. Schematic of control sequence of laser and spectrometer

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Fig. 3. Aluminum alloy samples

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Fig. 4. Prediction results of original spectral test sets

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Fig. 5. Invalid spectra, saturated spectra and normal spectra

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Fig. 6. Prediction results of filtered spectral test sets

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Fig. 7. Prediction results of normalized spectral test sets

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Fig. 8. Prediction results of spectral test sets after filter, normalization and principal component analysis

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Fig. 9. Verification of SVM with different inputs using leave-one-out. (a) Prediction accuracy; (b) modeling time

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Table 1. Grades and serial number of aluminum alloy samples
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Table 1. Grades and serial number of aluminum alloy samples
Category Grade Serial number
1 BBA.AlSi7MgCu(0.5)Fe(0.2) 1, 2
2 DC.360Y.6 3, 4, 5, 6, 7
3 HB.SF36 8, 9, 10,11, 12, 13
4 WD.AlSi7MgCu 14, 15, 16
5 WD.AlSi9Cu3(Fe) 17, 18, 19
6 YD.ZAlSi7Mg0.3 20, 21, 22, 23, 24

Table 2. Average prediction accuracy and mean modeling time of SVM with different inputs
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Table 2. Average prediction accuracy and mean modeling time of SVM with different inputs
Input Average prediction accuracy /% Mean modeling time /s
Original spectra 92.34 47.91
Filter 98.69 8.06
Filter+normalization 99.79 7.45
Filter+normalization+PCA 99.83 0.14

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Zhonghan Zhou, Xueyong Tian, Lanxiang Sun, Peng Zhang, Zhiwei Guo, Lifeng Qi. Identification of Aluminum Alloy Grades by Fiber-Laser Induced Breakdown Spectroscopy Combined with Support Vector Machine[J]. Laser & Optoelectronics Progress, 2018, 55(6): 063002

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