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

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. 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. 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. [Display omitted] •Three mid-level data fusion strategies are proposed and validated for the first time.•Thermal hardness of steel may be an important factor in plasma acoustic emission signals.•Fusion of LIBS spectral and plasma acoustic emission data improves steel classification accuracy.