Classification of Aviation Alloys Using Laser-Induced Breakdown Spectroscopy Based on a WT-PSO-LSSVM Model

It is well-known that aviation alloys of different grades show large differences in mechanical properties. At present, alloys must be strictly distinguished in the manufacturing plant because their close appearance and density are easily confused In this work, the wavelet transform (WT) method combined with the least squares support vector machine (LSSVM) is applied to the classification and identification of aviation alloys by laser-induced breakdown spectroscopy (LIBS). This experiment employed six different grades of aviation alloy as the classification samples and obtained 100 sets of spectral data for each sample. This research included the steps of preprocessing the obtained spectral data, model training, and parameter optimization. Finally, the accuracy of the training set was 99.98%, and the accuracy of the test set was 99.56%. Therefore, it is concluded that the model has superior generalization capacity and portability. The result of this work illustrates that LIBS technology can be adopted for the rapid identification of aviation alloys, which is of great significance for on-site quality control and efficiency improvement of aerospace parts manufacturing.