基于激光诱导击穿光谱法的铝合金中Mg元素定量分析

Quantitative Analysis of Mg Element in Aluminium Alloy Based on Laser-Induced Breakdown Spectroscopy

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%.