高重频火花放电增强激光诱导击穿光谱(LIBS)结合机器学习对铝合金中镁元素的定量分析  

Quantitative Analysis of Magnesium in Aluminum Alloys by High Repetition Rate Spark Discharge Enhanced Laser-Induced Breakdown Spectroscopy Combined With Machine Learning

作　　者：王亚蕊 王朝勇 吴诗磊 WANG Yarui;WANG Chaoyong;WU Shilei(School of Mathematics and Physics,Henan University of Urban Construction,Pingdingshan,Henan 467036,China)

机构地区：[1]河南城建学院数理学院,河南平顶山467036

出　　处：《中国无机分析化学》2024年第10期1457-1464,共8页Chinese Journal of Inorganic Analytical Chemistry

基　　金：河南省高等学校重点研发项目(23A140026);河南城建学院博士科研启动资金资助项目(K-Z2023009)。

摘　　要：铝合金中的镁元素会影响铝合金的性能,因此亟需一种快速、高准确度定量分析铝合金中镁元素含量的方法。采用基于微芯片激光器的高重频火花放电辅助激光诱导击穿光谱技术(HRR SD-LIBS)对铝合金中镁元素进行定量分析。采用移动中位数法对采集的光谱数据进行基线校正以获取净光谱数据,分别建立单变量定标模型和反向传播神经网络(BP)、支持向量机(SVM)和随机森林(RF)等多变量定标模型。结果表明,HRR SD-LIBS技术结合机器学习算法可建立更稳健的多变量定标模型。与常规单变量定标法相比,BP、SVM和RF模型的预测集相关系数(R_(P)^(2))从0.810升高到0.985、0.997和0.994,均方根误差(RMSEP)从0.283下降到0.059 8、0.041 0和0.054 2,平均相对误差从26.62%下降到3.13%、2.82%和3.61%。因此采用基于机器学习的多变量定标法能够显著提升LIBS定量分析准确度。基于小型化的微芯片激光器的HRR SD-LIBS技术结合机器学习算法能够实现对合金样品和其他材料的便携、快速、准确的定量元素分析。The performance of aluminum alloys can be affected by the element of magnesium.Therefore,it is necessary to realize fast and accurate quantitative analysis of magnesium in aluminum alloys.Herein,high repetition rate spark discharge assisted laser-induced breakdown spectroscopy based on microchip lasers(HRR SD-LIBS) was applied to quantitatively analyze magnesium elements in aluminum alloys.Baseline correction was performed using the moving median method to obtain the net spectral data.Multivariate calibration models such as back-propagation neural network(BP),support vector machine(SVM),and random forest(RF) were established,respectively.Compared with the conventional univariate calibration model,the correlation coefficients(R_(P)^(2)) of BP,SVM and RF models increased from 0.810 to 0.985,0.997 and 0.994,while the root mean square error(RMSEP) decreased from 0.283 to 0.059 8,0.041 0 and 0.054 2,the average relative error decreased from 26.62% to 3.13%,2.82%,and 3.61%.It indicated that machine learning combined microchip laser-based HRR SD-LIBS significantly improved the quantitative analysis ability of LIBS.In conclusion,it is promising to achieve portable,fast,and accurate quantitative elemental analysis of alloy samples and other materials.

关 键 词：火花放电 激光诱导击穿光谱 微芯片激光器 机器学习 定量分析 

分 类 号：O657.3[理学—分析化学] O433.4[理学—化学]