Zhao Zhang, Ya-ju Li, Guanghui Yang, Qiang Zeng, Xiaolong Li, Liangwen Chen, D. Qian, Dui-xiong Sun, Maogen Su, Lei Yang, Shaofeng Zhang, Xinwen Ma
{"title":"利用激光诱导击穿光谱与机器学习算法相结合估算微晶粒材料的晶粒尺寸","authors":"Zhao Zhang, Ya-ju Li, Guanghui Yang, Qiang Zeng, Xiaolong Li, Liangwen Chen, D. Qian, Dui-xiong Sun, Maogen Su, Lei Yang, Shaofeng Zhang, Xinwen Ma","doi":"10.1088/2058-6272/ad1792","DOIUrl":null,"url":null,"abstract":"\n Recent work validated a new method for estimating grain size of microgranular materials in the range of tens-to-hundreds micrometers using laser-induced breakdown spectroscopy (LIBS). In that situation, univariate analysis was performed and a piecewise model has to be constructed for achieving the estimation of the grain size within such a wide range. This is due to the fact that a complex dependence of plasma formation environment (i.e., the status of luminous plasma and therefore LIBS signal to be measured) on grain size occurs in the size range studied there. 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 the feature selection strategies with and without considering physical prior knowledge, respectively. By detailed analysis of the performances of the two multivariate models, it was found that, a unified calibration model can be constructed successfully based on BPNN algorithms for estimating the grain size in the range of tens-to-hundreds micrometers. It was also found that this model constructed with a physics-guided feature selection strategy has better prediction performances. This study has practical significance in developing the technology for material analysis using LIBS, especially in the case that LIBS signal exhibits a complex dependence on the material parameter to be estimated.","PeriodicalId":20250,"journal":{"name":"Plasma Science & Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms\",\"authors\":\"Zhao Zhang, Ya-ju Li, Guanghui Yang, Qiang Zeng, Xiaolong Li, Liangwen Chen, D. Qian, Dui-xiong Sun, Maogen Su, Lei Yang, Shaofeng Zhang, Xinwen Ma\",\"doi\":\"10.1088/2058-6272/ad1792\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Recent work validated a new method for estimating grain size of microgranular materials in the range of tens-to-hundreds micrometers using laser-induced breakdown spectroscopy (LIBS). In that situation, univariate analysis was performed and a piecewise model has to be constructed for achieving the estimation of the grain size within such a wide range. This is due to the fact that a complex dependence of plasma formation environment (i.e., the status of luminous plasma and therefore LIBS signal to be measured) on grain size occurs in the size range studied there. 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 the feature selection strategies with and without considering physical prior knowledge, respectively. By detailed analysis of the performances of the two multivariate models, it was found that, a unified calibration model can be constructed successfully based on BPNN algorithms for estimating the grain size in the range of tens-to-hundreds micrometers. It was also found that this model constructed with a physics-guided feature selection strategy has better prediction performances. This study has practical significance in developing the technology for material analysis using LIBS, especially in the case that LIBS signal exhibits a complex dependence on the material parameter to be estimated.\",\"PeriodicalId\":20250,\"journal\":{\"name\":\"Plasma Science & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plasma Science & Technology\",\"FirstCategoryId\":\"1089\",\"ListUrlMain\":\"https://doi.org/10.1088/2058-6272/ad1792\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHYSICS, FLUIDS & PLASMAS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plasma Science & Technology","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1088/2058-6272/ad1792","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, FLUIDS & PLASMAS","Score":null,"Total":0}
Estimating grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms
Recent work validated a new method for estimating grain size of microgranular materials in the range of tens-to-hundreds micrometers using laser-induced breakdown spectroscopy (LIBS). In that situation, univariate analysis was performed and a piecewise model has to be constructed for achieving the estimation of the grain size within such a wide range. This is due to the fact that a complex dependence of plasma formation environment (i.e., the status of luminous plasma and therefore LIBS signal to be measured) on grain size occurs in the size range studied there. 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 the feature selection strategies with and without considering physical prior knowledge, respectively. By detailed analysis of the performances of the two multivariate models, it was found that, a unified calibration model can be constructed successfully based on BPNN algorithms for estimating the grain size in the range of tens-to-hundreds micrometers. It was also found that this model constructed with a physics-guided feature selection strategy has better prediction performances. This study has practical significance in developing the technology for material analysis using LIBS, especially in the case that LIBS signal exhibits a complex dependence on the material parameter to be estimated.
期刊介绍:
PST assists in advancing plasma science and technology by reporting important, novel, helpful and thought-provoking progress in this strongly multidisciplinary and interdisciplinary field, in a timely manner.
A Publication of the Institute of Plasma Physics, Chinese Academy of Sciences and the Chinese Society of Theoretical and Applied Mechanics.