摘要
激光分层除漆的可靠性与可控性依赖于有效的在线监测技术,采用激光诱导击穿光谱(LIBS)技术能有效监控激光除漆过程。本文采用激光去除飞机碳纤维复合材料(CFRP)表面漆层,并基于高重频激光除漆LIBS在线监测平台,在线采集除漆过程所激发的面漆和底漆2类光谱共60组。分别建立了基于主成分分析(PCA)和偏最小二乘法(PLS)的判别和预测模型,研究了激光分层除漆过程中LIBS光谱的分类判别。PCA模型前两个主成分累计贡献率达到了79.2%,PLS-DA模型前两个主成分累计贡献率达到了85.5%。PLS回归模型校正标准差(RMSEE)为0.142923,均方根误差(RMSEcv)为0.152053,模型的预测标准差(RMSEP)为0.142421,对20组激光清洗面漆和底漆的混合数据集进行预测,预测准确率达100%。结果表明PLS判别模型比PCA模型分类判别效果更好,PLS预测模型实时评估和自动分类漆层具有较好的预测精度。本研究可为LIBS在线监测激光除漆过程,实现自动化、智能化的激光除漆提供技术支持。
The reliability and controllability of laser delamination paint removal depend on effective online monitoring technology.The laser induced breakdown spectroscopy(LIBS)technology can effectively monitor the laser paint removal process.In this paper,laser is used to remove the paint layer on the surface of aircraft carbon fiber composite(CFRP).Based on the high repetition frequency laser paint removal LIBS online monitoring platform,60 groups of two kinds of spectra of topcoat and primer excited by the paint removal process are collected online.The discrimination and prediction models based on principal component analysis(PCA)and partial least squares(PLS)were established respectively,and the classification discrimination of LIBS spectrum in laser delamination paint removal process was studied.The cumulative contribution rate of the first two principal components of PCA model reached 79.2%,and the cumulative contribution rate of the first two principal components of PLS-DA model reached 85.5%.The correction standard deviation(RMSEE)of the PLS regression model was 0.142923,the root mean square error(RMSEcv)was 0.152053,and the prediction standard deviation(RMSEP)of the model was 0.142421.The prediction accuracy was 100%for 20 groups of mixed data sets of laser cleaning topcoat and primer.The results show that PLS discrimination model is better than PCA model in classification discrimination,and PLS prediction model has better prediction accuracy in real-time evaluation and automatic classification of paint layers.This research can provide technical support for LIBS to monitor the laser paint removal process online and realize automatic and intelligent laser paint removal.
作者
李绍龙
高韶华
林德惠
胡月
杨翌锴
郑鑫
杨文锋
LI Shao-long;GAO Shao-hua;LIN De-hui;HU Yue;YANG Yi-kai;ZHENG Xin;YANG Wen-feng(Aviation Engineering Institute,Civil Aviation Flight University of China,Guanghan 618307,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2023年第5期706-711,共6页
Laser & Infrared
基金
中央高校基本科研业务费基金项目(No.ZJ2022-006)
四川省科技计划项目(No.2022NSFSC1903)
德阳市科技计划项目(No.2022GZ011)
大学生创新创业训练计划项目(No.S202110624004)资助。
关键词
激光除漆
在线监测
激光诱导击穿光谱
偏最小二乘法
laser paint removal
online monitoring
laser induced breakdown spectroscopy
partial least squares
