基于机器学习的隐身涂料设计方法与研究进展  

Research Progress and Design Methods of Stealth Coatings Based on Machine Learning

作　　者：刘旭 刘永豪 齐建涛[2] LIU Xu;LIU Yonghao;QI Jiantao(Naval Aviation University Qingdao Campus,Qingdao,Shandong 264000,China;China University of Petroleum(East China),Qingdao,Shandong 266580,China)

机构地区：[1]海军航空大学青岛校区,山东青岛264000 [2]中国石油大学(华东),山东青岛266580

出　　处：《涂料工业》2025年第3期13-18,共6页Paint & Coatings Industry

基　　金：国防科技173计划技术领域基金项目。

摘　　要：隐身涂料通过对雷达波、红外辐射、可见光及激光信号特性的调控,广泛应用于军事装备与先进技术领域。然而,隐身涂料的设计涉及多种材料和复杂加工参数的耗时实验。为了克服这些限制,数据驱动的涂料设计方法受到广泛关注。文章综述了基于机器学习的隐身涂料设计的最新进展。概括了隐身涂料的主要类型,包括吸波涂料、电磁屏蔽涂料、红外隐身涂料和复合隐身涂料,探讨了传统设计方法面临的挑战。介绍了数据驱动的隐身涂料设计,展示了数据预处理与特征提取策略如何优化模型输入,强调了高质量数据库、模型可解释性与多目标优化的重要性。此外,总结了机器学习在隐身涂料性能预测、材料筛选、结构设计及逆向优化等方面的研究案例。最后,探讨了各领域数据驱动下功能涂料的最新研究,为隐身涂料的智能设计提供参考。Stealth coatings,by regulating radar waves,infrared radiation,visible light,and laser signals,were widely applied in military equipment and advanced technological fields.However,the design of stealth coatings involved time-consuming experiments due to the complexity of material selection and processing parameters.To address these limitations,datadriven coatings design methods had attracted increasing attention.This review highlighted recent advances in stealth coatings design based on machine learning.It summarized the main types of stealth coatings,including radar-absorbing,electromagnetic shielding,infrared stealth,and composite stealth coatings,while discussing the challenges of traditional design methods.The review introduced data-driven stealth coatings design approaches,demonstrating how data preprocessing and feature extraction strategied optimize model inputs.It underscored the significance of high-quality databases,model interpretability,and multi-objective optimization.Additionally,research cases were presented where machine learning had been applied in performance prediction,material screening,structural design,and inversed optimization of stealth coatings.Finally,recent data-driven research advancements in functional coatings across various fields were explored,providing valuable insights into the intelligent design of future stealth coatings.

关 键 词：隐身涂料 机器学习 数据驱动 设计方法 

分 类 号：TQ637.7[化学工程—精细化工]