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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