Discarded aluminum alloys are a form of recyclable metal materials, and their classification and identification are highly important. In this work, laser‐induced breakdown spectroscopy (LIBS) technique combined with principal component analysis (PCA) and least‐squares support‐vector machine (LSSVM) algorithm were used to classify and identify five types of aluminum alloys. Exploratory analysis of five types of aluminum alloys by PCA was performed to achieve better segregation. The identification accuracy of the support‐vector machine (SVM) and LSSVM for aluminum alloy were 98.33% and 100%, respectively. The higher identification success rate was obtained using the LSSVM algorithm. Therefore, the LIBS technique combined with the PCA and LSSVM algorithms represents an efficient approach to identifying aluminum alloys.

中文翻译：

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激光诱导击穿光谱结合机器算法识别铝合金

丢弃的铝合金是可回收金属材料的一种形式，其分类和识别非常重要。在这项工作中，激光诱导击穿光谱技术（LIBS）技术与主成分分析（PCA）和最小二乘支持向量机（LSSVM）算法相结合被用于分类和识别五种铝合金。为了实现更好的偏析，对五种类型的铝合金进行了PCA探索性分析。支持向量机（SVM）和LSSVM对铝合金的识别精度分别为98.33％和100％。使用LSSVM算法可获得较高的识别成功率。因此，结合了PCA和LSSVM算法的LIBS技术代表了一种识别铝合金的有效方法。