文献类型：期刊文献

英文题名：Quantitative analysis and identification of magnesium alloys using fs-LA-SIBS combined with machine learning methods

作者：Liu, Jun[1];Wang, Ji[2,3,4];Li, Xiaopei[1];Lin, Hai[1];Liu, Tiancheng[1];Zhou, Bingyan[5];He, Xiaoyong[5]

机构：[1]Zhanjiang Presch Educ Coll, Dept Informat Sci, Zhanjiang 524084, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]Guangdong Prov Smart Ocean Sensing Network, Zhanjiang 524088, Peoples R China;[4]Equipment Engn Technol Res Ctr, Zhanjiang 524088, Peoples R China;[5]Dongguan Univ Technol, Sch Elect Engn & Intelligentizat, Dongguan 523808, Peoples R China

年份：2025

卷号：15

期号：3

起止页码：1549

外文期刊名：RSC ADVANCES

收录：SCI-EXPANDED(收录号：WOS:001396872900001)、、EI(收录号：20250417723488)、Scopus(收录号：2-s2.0-85215410597)、WOS

基金：This work was financially supported the Key-Area Research and Development Program of Guangdong Province (2022ZDZX1077), by the National Natural Science Foundation of China (62405052), the Guangdong University Featured Innovation Program Project (2023KTSCX378), and Key Disciplines in Next-Generation Information Technology in General Higher Education Institutions in Guangdong Province (2020ZDZX3008).

语种：英文

外文关键词：Atomic emission spectroscopy - Error statistics - Femtosecond lasers - k-nearest neighbors - Lanthanum alloys - Laser induced breakdown spectroscopy - Magnesium alloys - Manganese alloys - Potassium alloys - Support vector regression - Zinc alloys

外文摘要：This work employs the femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) technique for the quantitative analysis of magnesium alloy samples. It integrates four machine learning models: Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), and k-Nearest Neighbors (KNN) to evaluate their classification performance in identifying magnesium alloys. In regression tasks, the models aim to predict the content of four elements: manganese (Mn), aluminum (Al), zinc (Zn), and nickel (Ni) in the samples. For classification tasks, the models are trained to recognize different types of magnesium alloy samples. Performance evaluation is based on sensitivity, specificity, and accuracy. The results indicate that the RFR model performs optimally for regression tasks, while the Random Forest Classification (RFC) model outperforms other models in classification tasks. This work confirms the feasibility of quantitative analysis and identification of magnesium alloys using the fs-LA-SIBS technique combined with machine learning methods. It establishes a technical foundation for real-time monitoring of alloys in subsequent laser-induced breakdown spectroscopy (LIBS) instruments.