摘要
Mg元素能使铝合金获得更好的力学性能并在合金表面形成抗腐蚀的尖晶石膜,使合金具备较好的抗腐蚀性能,因此,探索能快速准确定量分析铝合金中Mg元素含量的方法具有重要意义。首先,基于激光诱导击穿光谱(LIBS)技术对17个铝合金样品中的Mg元素进行检测分析。然后,用Nd∶YAG激光器作为光源,分别建立了偏最小二乘(PLS)和随机森林(RF)模型,并对模型的预测性能进行了分析。实验结果表明,针对相同的测试集,PLS模型的相关系数(R_(p)^(2))为0.6809,均方根误差(RMSE)为1.2042;RF模型的R_(p)^(2)为0.8571,RMSE为1.0918。为了提高RF模型的预测性能,根据变量重要性对输入波长进行筛选。选取变量重要性大于0.11的波长点时,基于变量重要性的RF模型R_(p)^(2)为0.9461,RMSE为0.9534,相比RF模型的预测结果,R_(p)^(2)提升了10.38%,RMSE降低了12.68%,且建模时间减少了91.67%。
Mg element can make the aluminum alloy to obtain better mechanical properties and form a corrosion-resistant spinel film on the surface of the alloy,so that the alloy has better corrosion resistance.Therefore,exploring a method that can quickly and accurately detect the content of magnesium in aluminum alloy quantitatively is of great significance.In this paper,first,the Mg element in 17 aluminum alloy samples is detected and analyzed based on laser-induced breakdown spectroscopy(LIBS)technology.Then,Nd:YAG laser is used as light source,and the partial least squares(PLS)and random forest(RF)models are respectively established,and the prediction performance of the models is analyzed.The experimental results show that for the same test set,the correlation coefficient(R_(p)^(2))of the PLS model is 0.6809,and the root mean square error(RMSE)is 1.2042;the R_(p)^(2)of the RF model is 0.8571 and the RMSE is 1.0918.In order to improve the prediction accuracy of the random forest model,this experiment screened the input variables according to the importance of the variables.When the wavelength point with variable importance greater than 0.11 is selected,R_(p)^(2)of the RF model based on variable importance is 0.9461,and the RMSE is 0.9534.Compared with the prediction result of the RF model,R_(p)^(2) is increased by 10.38%,RMSE is reduced by 12.68%,and the modeling time is reduced by 91.67%.
作者
丁宇
杨淋玉
陈靖
王星雨
郭晓冉
徐瑄晨
赵兴强
罗勇
陈文杰
Ding Yu;Yang Linyu;Chen Jing;Wang Xingyu;Guo Xiaoran;Xu Xuanchen;Zhao Xingqiang;Luo Yong;Chen Wenjie(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Jiangsu Engineering Research Center on Meteorological Energy Using and Control,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;The No.32181^(st)Troop of PLA,Shijiazhuang 050000,Hebei,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第13期305-310,共6页
Laser & Optoelectronics Progress
基金
国家自然科学基金(62105160)。
关键词
激光光学
铝合金
激光诱导击穿光谱
随机森林
定量分析
laser optics
aluminium alloy
laser-induced breakdown spectroscopy
random forests
quantitative analysis