Research on rapid and accurate identification of steel grades based on laser-induced breakdown spectroscopy and restricted Boltzmann machine-back propagation algorithm

Steel is a pillar industry of the national economy, in which scrap, as an important secondary raw material for steel production, is of great significance for its rapid identification in order to realize accurate classification for recycling and reuse. In this paper, Laser-Induced Breakdown Spectroscopy (LIBS) combined with the Restricted Boltzmann machine-Backpropagation algorithm (RBM-BP) is used for the rapid identification of 13 steel samples. Based on the collected spectral data, spectral preprocessing was performed using the discrete wavelet transform (DWT) to eliminate redundant information such as spectral interference and background noise. In particular, the number of DWT decomposition layers was 10, the wavelet function was selected as db2, and the calibration RMSEC was 0.99%. The preprocessed data were subjected to downscaling and feature extraction using Restricted Boltzmann machine (RBM) and Principal Component Analysis (PCA), respectively, and then the back propagation algorithm (BP) was used to classify and model the steel samples and compare the performance of the two models, RBM-BP and PCA-BP. The results show that the classification accuracy of the RBM-BP model is up to 99.88%, and the dimensionality reduction time is 16.74 s, which is much lower than the 78.73 s of the PCA-BP model. The measured results show that LIBS combined with the RBM-BP algorithm can realize the fast and accurate classification of steel, and this technology has great potential in the accurate classification of scrap steel for recycling and reuse, which can provide important support for the sustainable development of the steel industry and the construction of a resource-saving and environment-friendly society.