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Quantitative analysis of metal scraps using laser-induced breakdown spectroscopy measurements via unsupervised adversarial domain adaptation.

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Title: Quantitative analysis of metal scraps using laser-induced breakdown spectroscopy measurements via unsupervised adversarial domain adaptation.
Authors: Srivastava, Ekta1 (AUTHOR), Kim, Hyebin1,2 (AUTHOR), Lee, Jaepil3 (AUTHOR), Shin, Sungho4 (AUTHOR), Jeong, Sungho3 (AUTHOR), Hwang, Euiseok1,5 (AUTHOR) euiseokh@gist.ac.kr
Source: Chemometrics & Intelligent Laboratory Systems. Nov2022, Vol. 230, pN.PAG-N.PAG. 1p.
Subjects: Laser-induced breakdown spectroscopy, Scrap metals, Metal analysis, Standard deviations, Quantitative research
Abstract: In this study, we propose a domain adaptation-based scheme for the compositional analysis of metal scraps using laser-induced breakdown spectroscopy (LIBS) measurements. LIBS analysis along with machine learning models has demonstrated feasibility for estimating elemental compositions of various materials such as metal scraps. However, LIBS-captured spectra can significantly vary depending on the physical and chemical properties of samples, configuration of the experimental setup, and prevailing environmental conditions such as temperature or humidity levels. Consequently, schemes based on calibration methods and the formulation of experimental parameters have been previously proposed by assuming that the training set is acquired under the same setup and conditions as the test set. Furthermore, current transfer learning solutions simply limit the pretrained model on the new test set, thus requiring a labeled test set in advance for finetuning. To address these limitations, this study proposes a novel transfer learning-based approach that depends on the domain adaptation of a source domain to transfer knowledge and reduce the difference between domains, and thus improving the performance of the target learner. We used LIBS measurements from certified reference materials (CRMs) to pretrain a source model to estimate the elemental concentration with known compositions of CRM metal samples. Then, the pretrained model is adapted in an unsupervised manner for the test/target dataset, and the adapted target model is used to quantify the elemental concentration of metal scraps. For evaluation, the CRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass of known compositions, are measured under a typical LIBS setup to develop the pretrained model. For unsupervised domain adaptation and testing, metal scraps from actual industrial fields are tested using LIBS under different conditions such as lasers of different wavelengths and moving stages. The experimental results demonstrate that the proposed scheme achieves a root mean square error of 1.47 wt% or less for all test datasets, which is significantly better than the results of conventional regression schemes. The proposed quantitative analysis approach can be generalized to the regression of metal scraps and other industrial applications using LIBS. [Display omitted] • Regressor for LIBS spectra of CRMs adapted for quantitative analysis of metal scraps. • Target encoder learns from source encoder in unsupervised adversarial training. • Proposed model shows comparable results with supervised quantitative analysis models. • Quantitative analysis of metal scraps adds extra benefit in efficient recycling. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In this study, we propose a domain adaptation-based scheme for the compositional analysis of metal scraps using laser-induced breakdown spectroscopy (LIBS) measurements. LIBS analysis along with machine learning models has demonstrated feasibility for estimating elemental compositions of various materials such as metal scraps. However, LIBS-captured spectra can significantly vary depending on the physical and chemical properties of samples, configuration of the experimental setup, and prevailing environmental conditions such as temperature or humidity levels. Consequently, schemes based on calibration methods and the formulation of experimental parameters have been previously proposed by assuming that the training set is acquired under the same setup and conditions as the test set. Furthermore, current transfer learning solutions simply limit the pretrained model on the new test set, thus requiring a labeled test set in advance for finetuning. To address these limitations, this study proposes a novel transfer learning-based approach that depends on the domain adaptation of a source domain to transfer knowledge and reduce the difference between domains, and thus improving the performance of the target learner. We used LIBS measurements from certified reference materials (CRMs) to pretrain a source model to estimate the elemental concentration with known compositions of CRM metal samples. Then, the pretrained model is adapted in an unsupervised manner for the test/target dataset, and the adapted target model is used to quantify the elemental concentration of metal scraps. For evaluation, the CRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass of known compositions, are measured under a typical LIBS setup to develop the pretrained model. For unsupervised domain adaptation and testing, metal scraps from actual industrial fields are tested using LIBS under different conditions such as lasers of different wavelengths and moving stages. The experimental results demonstrate that the proposed scheme achieves a root mean square error of 1.47 wt% or less for all test datasets, which is significantly better than the results of conventional regression schemes. The proposed quantitative analysis approach can be generalized to the regression of metal scraps and other industrial applications using LIBS. [Display omitted] • Regressor for LIBS spectra of CRMs adapted for quantitative analysis of metal scraps. • Target encoder learns from source encoder in unsupervised adversarial training. • Proposed model shows comparable results with supervised quantitative analysis models. • Quantitative analysis of metal scraps adds extra benefit in efficient recycling. [ABSTRACT FROM AUTHOR]
ISSN:01697439
DOI:10.1016/j.chemolab.2022.104667