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Random-Forest Analysis of Carbon Levels in Steels Using Laser-Induced Breakdown Spectroscopy
Abstract
The carbon levels in low-alloy steel samples were measured using laser-induced breakdown spectroscopy (LIBS) and a random forest (RF) method. When employing the RF method, the root-mean-square error of cross-validation (RMSECV) criterion was first used to select the spectral range of the spectral variables for RF model input, to prevent over-fitting of the RF model when only a few relevant variables are accompanied by many other variables. Second, the out-of-bag (OOB) error criterion was used to optimize the numbers of decision trees (ntree) and characteristic variables (mtry) in the RF model, which optimizes the RF structure. The availability of a large amount of relevant spectral information, coupled with the remarkable regression capacity of RF, greatly improved the carbon analytical accuracy. The results showed that the root-mean-square error of prediction (RMSEP) was 0.034 wt. % for the calibration curve method and 0.023 wt. % for the RF method; the reduction afforded by the latter method was 32.4 %. Thus, the RF method improved the carbon analytical accuracy for low-alloy steels.
Supporting agencies: This research was financially supported by the National Special Fund for the Development of Major Research Equipment and Instruments (Grant No. 2011YQ160017), the Science and Technology Project of Henan Science and Technology Department (Grant Nos. 232102220014, 232102210055, and 222102210214), the Changzhou Sci&Tech Program (Grant No.CE20232020), the Youth Fund Project of Pingdingshan University (Grant No. PXY-QNJJ-202001), and the Key Research Projects program of the Institute of Higher Learning of Henan Province (Grant No. 19A470004)
About the Authors
K. Li
School of Electrical and Mechanical Engineering, and Henan Key Laboratory of Research for Central Plains Ancient Ceramics, Pingdingshan University
China
China
Henan; Pingdingshan
X. Wang
School of Electrical and Mechanical Engineering, and Henan Key Laboratory of Research for Central Plains Ancient Ceramics, Pingdingshan University
China
China
Henan; Pingdingshan
J. Wang
School of Electrical and Mechanical Engineering, and Henan Key Laboratory of Research for Central Plains Ancient Ceramics, Pingdingshan University
China
China
Henan; Pingdingshan
P. Yang
Institute of Electronic, Changzhou College of Information Technology
China
China
Jiangsu; Changzhou
G. Tian
School of Electrical and Mechanical Engineering, and Henan Key Laboratory of Research for Central Plains Ancient Ceramics, Pingdingshan University
China
China
Henan; Pingdingshan
X. Li
Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology
China
China
Hubei; Wuhan
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Review
For citations:
Li K., Wang X., Wang J., Yang P., Tian G., Li X. Random-Forest Analysis of Carbon Levels in Steels Using Laser-Induced Breakdown Spectroscopy. Zhurnal Prikladnoii Spektroskopii. 2024;91(5):764.
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