Research On Spectral Characteristics Of Femtosecond Filament Induced Breakdown Of Aluminum Alloys

This paper adopts a combination of theoretical analysis and experimental research,and uses femtosecond filament induced breakdown spectroscopy(FIBS)to quantitatively analyze the Mn in aluminum alloy samples and classify and identify aluminum alloy samples.The quantitative analysis rule and classification capabilities at different positions of the filament are studied,and the advantages of FIBS in quantitative analysis of trace elements and aluminum alloy classification are proved.The research results in this paper provide experimental and theoretical basis for the rapid detection and identification of aluminum alloys in industrial sites.The main work of this paper is as follows:First,an experimental measurement system for FIBS was established.We use focusing lenses with different focal lengths to form filament in the air to study the effect of focal length on filament.Then the aluminum alloy standard sample was ablated using femtosecond filament to obtain the time evolution and spatial evolution of the spectrum.The time evolution and spatial evolution of the spectral line intensities of Mg I 383.83 nm?Al I 396.15 nm and Mn I 403.31 nm,as well as the time evolution and spatial evolution of Al plasma electron density are analyzed.Secondly,using femtosecond filament induced breakdown spectroscopy technology,quantitative analysis of Mn in aluminum alloy is carried out by changing the distance between the focusing lens and the target surface.The spectral stability and calibration curve of Mn I 403.31 nm at different positions of the filament and the influence of different positions of the filament on the quantitative analysis results are studied.The conclusion is that the quantitative analysis ability of the filament part is better than before and after filament formation.Finally,FIBS was used to classify five aluminum alloy samples,and the classification parameters were optimized.The result of the principal component analysis of different positions of the filament and the influence of the percentage of the training set on the classification accuracy are studied.Support vector machine and K-nearest neighbor method combined with principal component analysis were used to classify and identify different positions of aluminum alloy in filaments.The results show that the identification accuracy of principal component analysis combined with support vector machine is higher than that of principal component analysis combined with K-nearest neighbor method,and the recognition accuracy of the filament region is higher than before and after the filament.