Combination of plasma acoustic emission signal and laser-induced breakdown spectroscopy for accurate classification of steel

Shilei Xiong; Nan Yang; Haoyu Guan; Guangyuan Shi; Ming Luo; Yoshihiro Deguchi; Minchao Cui

doi:10.1016/j.aca.2024.343496

Combination of plasma acoustic emission signal and laser-induced breakdown spectroscopy for accurate classification of steel

Anal Chim Acta. 2025 Jan 22:1336:343496. doi: 10.1016/j.aca.2024.343496. Epub 2024 Nov 28.

Authors

Shilei Xiong¹, Nan Yang¹, Haoyu Guan¹, Guangyuan Shi¹, Ming Luo¹, Yoshihiro Deguchi², Minchao Cui³

Affiliations

¹ Key Laboratory of High Performance Manufacturing for Aero Engine (MIIT), Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China.
² Graduate School of Advanced Technology and Science, Tokushima University, 2-1, Minamijyosanjima, Tokushima, 770-8506, Japan.
³ Key Laboratory of High Performance Manufacturing for Aero Engine (MIIT), Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China. Electronic address: cuiminchao@nwpu.edu.cn.

PMID: 39788666
DOI: 10.1016/j.aca.2024.343496

Abstract

Background: Fast and accurate classification of steel can effectively improve industrial production efficiency. In recent years, the use of laser-induced breakdown spectroscopy (LIBS) in conjunction with other techniques for material classification has been developing. Plasma Acoustic Emission Signal (PAES) is a type of modal information separate from spectra that is detected using LIBS, and it can reflect some of the sample's physicochemical information. Existing research has not addressed the use of LIBS in conjunction with PAES for steel classification and identification, thus it is quite interesting to examine a speedy steel classification approach using LIBS and PAES.

Results: In this work, we used LIBS and PAES mid-level data fusion methods to classify and identify eight steel samples. We recorded the LIBS spectral data and PAES data of the eight samples synchronously, respectively, and proposed three novel mid-level data fusion strategies (additive fusion, splicing fusion, and multiplicative fusion). We have discussed the classification results by using machine learning algorithms. The conclusion revealed that the average accuracy of classifying a single LIBS spectrum is 72.5 %, whereas the average accuracy of classifying a single PAES data is 78.75 %. By combining LIBS spectral data and PAES data in the middle layer, the average accuracy of the splicing fusion classification result is 87.5 %, and the average accuracy of the multiplication fusion classification result is 86.25 %. Meanwhile, we have also found that thermal hardness may be an important physical factor affecting the acoustic emission signal of steel plasma.

Significance: Accurate steel classification is achieved by combining spectral and acoustic data. This approach is anticipated to be used in the future to quickly classify large amounts of steel in industrial settings, leading to a notable increase in the efficiency of industrial production.

Keywords: Classification accuracy; LIBS; Mid-level fusion strategy; PAES; Steel.