The invention discloses an aluminum
alloy component detection method and
system based on LIBS and PLS, and belongs to the technical field of
metal material
component analysis. The method comprises the following steps: 1, carrying out deoxidation flattening treatment on an aluminum
alloy sample, and focusing on the surface of the aluminum
alloy sample by using an aluminum alloy component detection
system to obtain an LIBS spectrum of the aluminum alloy sample; 2, carrying out abnormal spectrum
elimination on the LIBS spectrum of the aluminum alloy sample through a Pauta method, then carrying out background
interference elimination and spectrum normalization by adopting a window translation minimum value method and sub-channel background
intensity normalization, and extracting a characteristic peak of a normalized spectrum strongly related to the components of the aluminum alloy sample; 3, using PLS to construct and
train an aluminum alloy component prediction model; and 4, inputting the characteristic peak of the normalized spectrum of the aluminum alloy sample into the trained aluminum alloy component prediction model to obtain a component prediction result of the aluminum alloy sample. Compared with the prior art, non-destructive rapid high-precision second-level detection of aluminum alloy components is realized.