文献类型：期刊文献

英文题名：A Novel Strategy for Identification of Magnesium Alloy Via Femtosecond Laser-Ablationspark-Induced Breakdown Spectroscopy Combine with Machine Learning

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

机构：[1] Department of Information Science, Zhanjiang Preschool Education College, Guangdong, Zhanjiang, 524084, China; [2] College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China; [3] Guangdong Provincial Smart Ocean Sensing Network, Equipment Engineering Technology Research Center, Zhanjiang, 524088, China; [4] School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, 523808, China

年份：2024

外文期刊名：SSRN

收录：EI(收录号：20240357141)

语种：英文

外文关键词：Atomic emission spectroscopy - Error statistics - k-nearest neighbors - Lanthanum alloys - Laser ablation - Laser induced breakdown spectroscopy - Magnesium alloys - Manganese alloys - Mercury amalgams - Nickel 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 Regression (RFR), 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, accuracy, and micro-average AUC metrics, conducted through 5-fold cross-validation and independent predictions. The results indicate that the RFR model performs optimally for regression tasks, while the RFR 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. ? 2024, The Authors. All rights reserved.