首页|Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

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Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy(LIBS).In this situation,a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size.In the present work,we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes.Specifically,two unified multivariate calibration models are constructed based on back-propagation neural network(BPNN)algorithms using feature selection strategies with and without considering prior information.By detailed analysis of the performances of the two multivariate models,it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers.It was also found that the model constructed with a prior-guided feature selection strategy had better prediction performance.This study has practical significance in developing the technology for material analysis using LIBS,especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

laser-induced breakdown spectroscopymachine learningrandomly packed microgranular materials

张朝、李亚举、杨光辉、曾强、李小龙、陈良文、钱东斌、孙对兄、苏茂根、杨磊、张少锋、马新文

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Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province,College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,People's Republic of China

Advanced Energy Science and Technology,Guangdong Laboratory,Huizhou 516000,People's Republic of China

Institute of Modern Physics,Chinese Academy of Sciences,Lanzhou 730000,People's Republic of China

University of the Chinese Academy of Sciences,Beijing 100049,People's Republic of China

School of Nuclear Science and Technology,University of the Chinese Academy of Sciences,Beijing 100049,People's Republic of China

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国家重点研发计划国家自然科学基金国家自然科学基金Major Science and Technology Project of Gansu Province

2017YFA0402300U22412881197435922ZD6FA021-5

2024

10.1088/2058-6272/ad1792

等离子体科学和技术(英文版)
中国科学院合肥物质科学研究所 中国力学学会

等离子体科学和技术(英文版)

EI
影响因子：0.297
ISSN：1009-0630
年,卷(期)：2024.26(5)
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