基于PSO-SVR的复合漆层激光清洗厚度预测方法  

Prediction Method of Laser Cleaning Composite Coating Thickness Based on PSO-SVR

作　　者：侯兴强 成巍 任远 苏桢巍 张延鲁 高秋玲 戴娜 Hou Xingqiang;Cheng Wei;Ren Yuan;Su Zhenwei;Zhang Yanlu;Gao Qiuling;Dai Na(Institute of Laser,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,Shandong,China)

机构地区：[1]齐鲁工业大学(山东省科学院)激光研究所,山东济南250353

出　　处：《应用激光》2025年第1期75-85,共11页Applied Laser

基　　金：山东省重大科技创新工程(2022CXGC020205);山东省重点研发计划(2020JMRH0504);山东省科技型中小企业能力提升项目(2022TSGC2064);济南市新高校20条资助项目(202228019)。

摘　　要：为预测复合漆层激光清洗厚度进而实现复合漆层的可控性除漆,提出一种基于粒子群优化-支持向量回归的复合漆层激光清洗厚度预测方法。以铝合金基材表面均匀涂覆20μm绿色环氧底漆和40μm白色聚氨酯面漆为实验样件,进行激光清洗实验;基于实验结果建立了工艺参数与复合漆层除漆厚度间的支持向量机回归(support vector regression,SVR)模型,并运用粒子群算法(particle swarm optimization,PSO)优化SVR模型的惩罚系数C与核函数参数g。实验结果表明:与SVR模型和BP神经网络模型相比,该模型对复合漆层激光清洗厚度的预测结果更准确,该预测模型的决定系数为0.96171,均方根误差为1.738,平均绝对误差为1.5162。研究可以获得精度较高的除漆厚度预测模型,实现对激光除漆厚度的有效预测,为进一步的复合漆层激光清洗的智能控制研究奠定了模型基础。This paper presents a prediction method for the laser cleaning thickness of composite coatings using a support vector regression(SVR)model optimized by a particle swarm optimization(PSO)algorithm.Laser cleaning experiments were conducted on aluminum alloy substrates coated with a 20μm green epoxy primer and a 40μm white polyurethane topcoat.An SVR model was developed to establish the correlation between process parameters and the thickness of paint removal for composite coatings.The PSO algorithm was employed to optimize the SVR model′s penalty coefficient C and kernel function parameter g.The experimental results show that compared with SVR model and BP neural network model,the model is more accurate in predicting the laser cleaning thickness of composite coating.The coefficient of determination of the model is 0.96171,the root mean square error is 1.738,and the average absolute error is 1.5162.In this study,a prediction model of paint removal thickness with high precision can be obtained,which can effectively predict the thickness of laser paint removal,and lays a foundation for further research on intelligent control of composite paint layer.

关 键 词：激光清洗 除漆厚度 支持向量机 粒子群算法 

分 类 号：TN249[电子电信—物理电子学]