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| Classification of Special Steel Based on LIBS Combined With Particle Swarm Optimization and Support Vector Machine |
| ZENG Qing-dong1, 2, CHEN Guang-hui1, 3, LI Wen-xin1, MENG Jiu-ling1, LI Geng1, TONG Ju-hong1, TIAN Zhi-hui1, ZHANG Xiao-lin1, LI Guo-hui1, GUO Lian-bo2, XIAO Yong-jun1* |
1. School of Physics and Electronic Information Engineering, Hubei Engineering University, Xiaogan 432000, China
2. Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan 430074, China
3. Faculty of Physics and Electronic Science, Hubei University, Wuhan 430062, China
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Abstract The steel industry has become a mainstay of the Chinese national economy. Due to the limitation of production technology, Chinese steel products are mainly concentrated in the middle and low-end products of uneven quality. It could result in the severe waste of steel resources and the pollutionofmetal garbage wastes. Therefore, the rapid identification and classification method of steel products is significant for environmental protection and for improving steel resources' recycling rate. This work utilised laser-induced breakdown spectroscopy (LIBS) to quickly collect the spectral data of 10 kinds of special steels. Then, a support vector machine (SVM) learned and modelled the spectral data to obtain the rapid steel classification model. However, due to the element composition of different special steels being complex and similar, the performance of classification results may be directly and significantly affected by SVM model parameters. To realise the rapid classification and detection of different grades of steel alloys, the two different methods of particle swarm optimisation (PSO) and grid search optimization were used to optimize the model parameters and speed up the training efficiency. Then, the spectral intensity of 17 characteristic lines of 6 major trace elements (Mn, Cr, Cu, V, Mo and Ti) in samples and 17 feature information variables extracted from the LIBS spectrum data with full variables by principal component analysis (PCA) were chosen as the input to establish the PSO-SVM, PSO-PCA-SVM, PCA-SVM and SVM models for steel classification respectively. The experimental results show that compared with the SVM model's optimization time of 115.64 s, the shortest optimization time of PSO-SVM is 11.5 s, and its classification accuracy (96.67%) is not significantly inferior to the accuracy of the PCA-SVM model (97.5%). The results show that LIBS combined with the PSO-SVM algorithm can achieve rapid and high-precision steel classification, which provides a new solution to detect and classifythedifferent steel products rapidly and precisely.
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Received: 2022-07-23
Accepted: 2023-12-13
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Corresponding Authors:
XIAO Yong-jun
E-mail: 346917126@qq.com
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