Jingjun Lin, Panyang Dai, Changjin Che, Xiaomei Lin, Yao Li, Jiangfei Yang, Yutao Huang, Yongkang Ren, Xin Zhen, Xingyue Yang
{"title":"利用激光诱导击穿光谱和两步聚类算法的汽车废金属分类方法研究","authors":"Jingjun Lin, Panyang Dai, Changjin Che, Xiaomei Lin, Yao Li, Jiangfei Yang, Yutao Huang, Yongkang Ren, Xin Zhen, Xingyue Yang","doi":"10.2351/7.0001289","DOIUrl":null,"url":null,"abstract":"In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing laser-induced breakdown spectroscopy (LIBS) and two-step clustering algorithm (K-means, hierarchical clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the multiplicative scatter correction to the spectral data for calibration, the initial clustering clusters were determined using the Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient. Then, the Kruskal–Wallis test was conducted on each cluster to check the significance. The clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.","PeriodicalId":50168,"journal":{"name":"Journal of Laser Applications","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on automotive scrap metal classification method using laser-induced breakdown spectroscopy and two-step clustering algorithm\",\"authors\":\"Jingjun Lin, Panyang Dai, Changjin Che, Xiaomei Lin, Yao Li, Jiangfei Yang, Yutao Huang, Yongkang Ren, Xin Zhen, Xingyue Yang\",\"doi\":\"10.2351/7.0001289\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing laser-induced breakdown spectroscopy (LIBS) and two-step clustering algorithm (K-means, hierarchical clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the multiplicative scatter correction to the spectral data for calibration, the initial clustering clusters were determined using the Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient. 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Research on automotive scrap metal classification method using laser-induced breakdown spectroscopy and two-step clustering algorithm
In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing laser-induced breakdown spectroscopy (LIBS) and two-step clustering algorithm (K-means, hierarchical clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the multiplicative scatter correction to the spectral data for calibration, the initial clustering clusters were determined using the Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient. Then, the Kruskal–Wallis test was conducted on each cluster to check the significance. The clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.
期刊介绍:
The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety.
The following international and well known first-class scientists serve as allocated Editors in 9 new categories:
High Precision Materials Processing with Ultrafast Lasers
Laser Additive Manufacturing
High Power Materials Processing with High Brightness Lasers
Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures
Surface Modification
Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology
Spectroscopy / Imaging / Diagnostics / Measurements
Laser Systems and Markets
Medical Applications & Safety
Thermal Transportation
Nanomaterials and Nanoprocessing
Laser applications in Microelectronics.