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| Classification of Copper Alloys Based on Microjoule High Repetition
Laser-Induced Breakdown Spectra |
| QU Dong-ming, ZHANG Zi-yi, LIANG Jun-xuan, LIAO Hai-wen, YANG Guang* |
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China
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Abstract For the industrial application scenario of waste copper alloy recycling and classification, two machine learning algorithms based on microjoule high-frequency laser-induced breakdown spectroscopy (MH-LIBS) combined with artificial neural network (ANN) and support vector machine (SVM) are used. Seven copper alloy samples (H59, H62, H70, H85, H96, HPb59-1, HPb62) collected in point and motion modes were classified and recognized, respectively. The results show that ANN and SVM can achieve 100% accuracy in classifying the copper alloys collected in point mode. The classification accuracy for the copper alloys collected in motion mode is 100% and 99.86%, respectively. It can be seen that the microfocus high-frequency laser-induced breakdown spectroscopy system combined with machine learning algorithms can realize the fine classification of copper alloys, which is suitable for the rapid analysis of waste copper alloys on site.
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Received: 2023-05-05
Accepted: 2023-12-27
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Corresponding Authors:
YANG Guang
E-mail: yangguang_jlu@163.com
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