{"title":"基于激光诱导击穿光谱和 PLS-DA 模型协同作用的 CFRP 激光分层脱漆实时监测系统","authors":"Ying Zhao, Xiaoyong Zhuo, Yanqun Tong, Jianyu Huang, Shuai Wang, Wangfan Zhou, Liang Chen, Yu Chen, Wen Shi","doi":"10.1007/s10946-024-10221-6","DOIUrl":null,"url":null,"abstract":"<div><p>To achieve precise removal of different coatings from carbon fiber-reinforced polymer (CFRP), we propose real-time monitoring for laser-layered paint removal. Current methods for laser paint removal on CFRP surfaces primarily focus on temperature control to safeguard the CFRP against potential damage, yet encounter challenges in providing real-time monitoring capabilities. In this study, we present laser-induced breakdown spectroscopy (LIBS) combined with partial least-squares discriminant analysis (PLS-DA) models as a promising approach. Initially, in this study, we analyze the elemental composition of carbon fiber substrates, primer, and topcoat to identify key characteristic elements for evaluating the laser-layered paint removal effectiveness. Subsequently, we explore changes in the intensities of characteristic spectral lines associated with the characteristic elements in different layers. Lastly, we develop PLS-DA models to effectively identify and classify the carbon fiber substrates, primer, and topcoat, enabling real-time monitoring of laser-layered paint removal. Based on the measured LIBS characteristic intensities and PLS-DA models, we accurately identified materials using Al I (396.164 nm) and Cr I (428.984 nm), or exclusively Cr I (428.984 nm), with 100% accuracy. The results demonstrate the feasibility of integrating LIBS with PLS-DA for monitoring laser-layered paint removal and show its potential in high-quality surface cleaning and automation.</p></div>","PeriodicalId":663,"journal":{"name":"Journal of Russian Laser Research","volume":"45 3","pages":"354 - 364"},"PeriodicalIF":0.7000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-Time Monitoring of Laser-Layered Paint Removal from CFRP Based on the Synergy of Laser-Induced Breakdown Spectroscopy and PLS-DA Models\",\"authors\":\"Ying Zhao, Xiaoyong Zhuo, Yanqun Tong, Jianyu Huang, Shuai Wang, Wangfan Zhou, Liang Chen, Yu Chen, Wen Shi\",\"doi\":\"10.1007/s10946-024-10221-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To achieve precise removal of different coatings from carbon fiber-reinforced polymer (CFRP), we propose real-time monitoring for laser-layered paint removal. Current methods for laser paint removal on CFRP surfaces primarily focus on temperature control to safeguard the CFRP against potential damage, yet encounter challenges in providing real-time monitoring capabilities. In this study, we present laser-induced breakdown spectroscopy (LIBS) combined with partial least-squares discriminant analysis (PLS-DA) models as a promising approach. Initially, in this study, we analyze the elemental composition of carbon fiber substrates, primer, and topcoat to identify key characteristic elements for evaluating the laser-layered paint removal effectiveness. Subsequently, we explore changes in the intensities of characteristic spectral lines associated with the characteristic elements in different layers. Lastly, we develop PLS-DA models to effectively identify and classify the carbon fiber substrates, primer, and topcoat, enabling real-time monitoring of laser-layered paint removal. Based on the measured LIBS characteristic intensities and PLS-DA models, we accurately identified materials using Al I (396.164 nm) and Cr I (428.984 nm), or exclusively Cr I (428.984 nm), with 100% accuracy. The results demonstrate the feasibility of integrating LIBS with PLS-DA for monitoring laser-layered paint removal and show its potential in high-quality surface cleaning and automation.</p></div>\",\"PeriodicalId\":663,\"journal\":{\"name\":\"Journal of Russian Laser Research\",\"volume\":\"45 3\",\"pages\":\"354 - 364\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Russian Laser Research\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10946-024-10221-6\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Russian Laser Research","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s10946-024-10221-6","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
摘要
为了精确去除碳纤维增强聚合物(CFRP)上的不同涂层,我们建议对激光分层除漆进行实时监控。目前在 CFRP 表面进行激光除漆的方法主要集中在温度控制上,以保护 CFRP 免受潜在的损坏,但在提供实时监控功能方面遇到了挑战。在本研究中,我们将激光诱导击穿光谱(LIBS)与偏最小二乘判别分析(PLS-DA)模型相结合,作为一种很有前景的方法。在本研究中,我们首先分析了碳纤维基材、底漆和面漆的元素组成,以确定评估激光分层除漆效果的关键特征元素。随后,我们探讨了不同层中与特征元素相关的特征光谱线强度的变化。最后,我们建立了 PLS-DA 模型,以有效识别碳纤维基材、底漆和面漆并对其进行分类,从而实现对激光分层除漆的实时监控。根据测得的 LIBS 特性强度和 PLS-DA 模型,我们准确识别了使用 Al I (396.164 nm) 和 Cr I (428.984 nm) 或完全使用 Cr I (428.984 nm) 的材料,准确率达到 100%。这些结果证明了将 LIBS 与 PLS-DA 集成用于监测激光分层除漆的可行性,并显示了其在高质量表面清洁和自动化方面的潜力。
Real-Time Monitoring of Laser-Layered Paint Removal from CFRP Based on the Synergy of Laser-Induced Breakdown Spectroscopy and PLS-DA Models
To achieve precise removal of different coatings from carbon fiber-reinforced polymer (CFRP), we propose real-time monitoring for laser-layered paint removal. Current methods for laser paint removal on CFRP surfaces primarily focus on temperature control to safeguard the CFRP against potential damage, yet encounter challenges in providing real-time monitoring capabilities. In this study, we present laser-induced breakdown spectroscopy (LIBS) combined with partial least-squares discriminant analysis (PLS-DA) models as a promising approach. Initially, in this study, we analyze the elemental composition of carbon fiber substrates, primer, and topcoat to identify key characteristic elements for evaluating the laser-layered paint removal effectiveness. Subsequently, we explore changes in the intensities of characteristic spectral lines associated with the characteristic elements in different layers. Lastly, we develop PLS-DA models to effectively identify and classify the carbon fiber substrates, primer, and topcoat, enabling real-time monitoring of laser-layered paint removal. Based on the measured LIBS characteristic intensities and PLS-DA models, we accurately identified materials using Al I (396.164 nm) and Cr I (428.984 nm), or exclusively Cr I (428.984 nm), with 100% accuracy. The results demonstrate the feasibility of integrating LIBS with PLS-DA for monitoring laser-layered paint removal and show its potential in high-quality surface cleaning and automation.
期刊介绍:
The journal publishes original, high-quality articles that follow new developments in all areas of laser research, including:
laser physics;
laser interaction with matter;
properties of laser beams;
laser thermonuclear fusion;
laser chemistry;
quantum and nonlinear optics;
optoelectronics;
solid state, gas, liquid, chemical, and semiconductor lasers.