WAM数据集与电磁波吸波材料分类方法及应用研究

袁宇洋; 张峻晗; 李丹丹; 沙建军

doi:10.11999/JEIT250166

WAM数据集与电磁波吸波材料分类方法及应用研究

doi: 10.11999/JEIT250166 cstr: 32379.14.JEIT250166

袁宇洋^{1, 2},
张峻晗^{1, 2},
李丹丹^{1, 2},
沙建军^{1, 2, 3, ,}

1.
大连理工大学力学与航空航天学院大连 116024
2.
大连理工大学工业装备结构分析国家重点实验室大连 116024
3.
辽宁省飞行器前沿技术重点实验室大连 116024

详细信息

作者简介:
袁宇洋：男，研究生，主要研究方向为吸波材料与隐身技术

张峻晗：男，研究生，主要研究方向为复合材料力学

李丹丹：女，研究生，主要研究方向为金属材料学

沙建军：男，教授，主要研究方向为航空航天先进复合材料优化设计及快速制造

通讯作者:
沙建军　jjsha@dlut.edu.cn

中图分类号: TP391.4
计量
- 文章访问数: 40
- HTML全文浏览量: 22
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2025-03-17
- 修回日期: 2025-11-03
- 录用日期: 2025-11-03
- 网络出版日期: 2025-11-13

Application of WAM Data Set and Classification Method of Electromagnetic Wave Absorbing Materials

YUAN Yuyang^{1, 2},
ZHANG Junhan^{1, 2},
LI Dandan^{1, 2},
SHA Jian jun^{1, 2, 3
, ,}

1.
College of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 110624
2.
State Key Laboratory of Industrial Equipment and Structural Analysis, Dalian University of Technology, Dalian 116024
3.
Key Laboratory of Advanced Technology for Aerospace Vehicles, Liaoning Province, Dalian 116024

摘要

摘要: 电磁辐射防护吸波材料的性能主要由厚度、最大反射损耗和有效吸收带宽决定。研究集中在金属有机框架、碳基和陶瓷吸波材料上，利用弱人工智能分析WAM(Wave absorption materials)数据集。划分训练集和测试集后进行数据增强以及相关性和主成分分析。采用决策树算法制定分类指标，发现MOFs(Metal organic frameworks)类材料的反射损耗优于碳基材料类，MOFs类材料容易满足最大反射损耗值小于-45dB。多次训练后，随机森林算法泛化性能比决策树算法好，ROC-AUC值更高。运用神经网络进行分类研究，结果表明自组织映射神经网络在分类上表现更佳，而概率神经网络效果较差。将二分类问题扩展到三分类问题后，使用非线性分类、聚类和Boosting算法，发现最大反射损耗是关键指标。进一步分析表明，WAM数据集非线性可分，模糊聚类效果较好。人工智能有助于揭示材料特性与吸波性能的关系，加速新材料研发，支持吸波材料知识图谱和知识库的建设。
- 吸波材料 /
- 模式识别 /
- 神经网络 /
- 数据挖掘 /
- 人工智能
Abstract: The performance of electromagnetic radiation shielding and absorbing materials is mainly determined by thickness, maximum reflection loss, and effective absorption bandwidth. Research focuses on metal-organic frameworks, carbon-based, and ceramic absorbing materials, and weak artificial intelligence is used to analyze the WAM (Wave Absorption Materials) dataset. After dividing the dataset into training and testing sets, data augmentation and correlation and principal component analysis are conducted. The decision tree algorithm is used to establish classification indicators, and it is found that the reflection loss of MOFs (Metal Organic Frameworks) materials is better than that of carbon-based materials, and MOFs materials are more likely to meet the maximum reflection loss value of less than –45 dB. The generalization performance of the random forest algorithm is better than that of the decision tree algorithm, and the ROC-AUC value is higher. The neural network is used for classification research, and the results show that the self-organizing mapping neural network performs better in classification, while the probabilistic neural network has a poor effect. After extending the binary classification problem to a three-class classification problem, nonlinear classification, clustering, and Boosting algorithms are used, and it is found that the maximum reflection loss is a key indicator. Further analysis shows that the WAM dataset is nonlinearly separable, and the fuzzy clustering effect is better.Artificial intelligence helps to reveal the relationship between material properties and absorbing performance, accelerate the development of new materials, and support the construction of the knowledge graph and knowledge base of absorbing materials. Objective Computational materials science, high-throughput experimentation, and the Materials Genome Initiative (MGI) have become prominent research frontiers in materials science. The Materials Genome Initiative serves as a strategic framework and developmental roadmap aimed at advancing materials research through artificial intelligence. Similar to gene sequencing in bioinformatics, its primary goal is to facilitate the discovery of novel material compositions and structures. Extracting valuable insights from large-scale datasets contributes significantly to cost reduction, efficiency improvement, interdisciplinary integration, and leapfrog advancements in materials development. Big data analytics, high-performance computing, and advanced algorithms constitute the foundational pillars of this initiative, providing critical support for the research and development of new materials. However, a prerequisite for discovering new material compositions and structures lies in the effective screening of candidate materials to identify those with outstanding properties that satisfy engineering application requirements. This necessitates the construction of comprehensive datasets, the development of robust classification algorithms, further enhancement of model generalization capabilities, and the advancement of associated application software. Methods This study was conducted using pattern recognition methods. First, a self-developed Wave-Absorbing Materials (WAM) dataset was constructed, comprising a test set and a validation set. Data preprocessing was carried out initially, which included data augmentation, data merging, and principal component analysis. Decision trees and random forests were employed to establish classification indicators and define the basis for classification. Self-Organizing Maps (SOM) and Probabilistic Neural Networks (PNN) were utilized for the classification task. Finally, the accuracy rates of different clustering algorithms were compared, revealing that the fuzzy clustering algorithm demonstrated relatively superior performance and was capable of achieving satisfactory results. Results and Discussions It was found that the reflection loss of MOFs (Metal Organic Frameworks) materials is superior to that of carbon-based materials. Semantic segmentation algorithms are not applicable to the classification of the WAM dataset. The classification accuracy of SOM is better than that of PNN. The WAM dataset is not linearly separable, and the classification results depend on the data distribution characteristics of the dataset itself. The maximum reflection loss is the key indicator for classification. Conclusions For the construction of the dataset of absorbing materials, a self-created WAM dataset was first built, which solved the problem that there was no dataset for the study of absorbing materials using pattern recognition reported in the known literature. The performance of various algorithms was compared and studied, and the optimal algorithm was determined based on the characteristics of the dataset. The traditional binary classification problem was extended to three classifications, preparing for the next step of multi-classification problem research. The use of artificial intelligence algorithms is conducive to improving the credibility and reliability of the research, and is beneficial to saving time costs and human resources. This method can explore the relationship between material properties and absorbing performance. It is conducive to shortening the research and development cycle, providing assistance for the screening of new materials, and providing support for the construction of the knowledge base of absorbing materials. The useful knowledge extracted from WAM is troubled by the problem of data sparsity, so there are certain limitations to artificial intelligence.
- Electromagnetic wave absorbing materials /
- pattern recognition /
- neural networks /
- data mining /
- artificial intelligence.

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图 1 原始数据加入高斯白噪声、随机噪声的影响

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图 2 数据相关性分析

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图 3 (a)不剪枝决策树，(b)减枝决策树；(c) 随机森林第1棵决策树，共30棵，(d) 随机森林第2棵决策树，共30棵，(e) 随机森林第3棵决策树，共30棵

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图 4 随机森林算法与决策树算法的ROC-AUC对比图

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图 5 神经网络结构示意图

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图 6 PNN算法分类效果图

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图 7 不同训练次数的权值分布图

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表 1 MOFs类及其衍生材料类吸波性能数据集^[20–21]

名称	厚度/mm	损耗/dB	带宽/GHz
Co@C	2	–49.76	5.44
Co@C@NRGO	2	–73.4	5.3
MPC@Ni/C	2.2	–73.8	5.8
Ni@C-700	1.8	–73.2	4.8
Fe3O4@NPC	3	–65.5	4.5
C-MIL-88/GNP	0.12	–28.0	4.2
Fe7S8/C	1.45	–68.8	4.56
MCC/rGO	3.6	–62.5	11.68
Cu/C	2.3	–52.0	6.8
CuO/C	1.55	–57.5	4.7

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表 2 Carbon类材料吸波性能数据集^[22]

名称	厚度/mm	损耗/dB	带宽/GHz
Ni/纳米石墨	1.5	–17.5	6
NiFeCo/纳米石墨	0.3	–28	7.5
洋葱碳/水性丙烯酸	5	–17.2	3.2
洋葱碳/水性丙烯酸	3	–14.3	3.8
石墨烯	10	–26	13.9
石墨烯/碳管	10	–39.5	16
石墨烯/PPy	3	–27	5.9
石墨烯/PVA	3.5	–44.5	7.5
石墨烯/Fe3O4	3	–23	5.8
氮掺杂石墨烯	3.3	–53.2	8.1

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表 3 Ceramics类材料吸波性能数据集^[23]

名称	厚度/mm	损耗/dB	带宽/GHz
Graphene nanosheets/MgO	1.5	–36.5	2
Edge-Rich Graphene/Si3N4	3.75	–26.7	4.2
SiC-Si3N4	2.5	–27.1	2.7
SiC/SiO2-Si3N4	3.8	–30	3.5
Ti3SiC2/cordierte	1.5	–14.1	2.4
Ni/ZnO	2	–45.4	4.6
FeCo/ZnO	1.9	–53.81	3.8
CuFe2O4/MgO	2	–25.35	8.38
Fe3Si/CNTs/SiC	3	–40	4.8
C/AlN	2	–30	2

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表 4 WAM数据集的验证集^[24]

名称	厚度/mm	损耗/dB	带宽/GHz
CNT/CFs	2.5	–42	2.7
CNT/PANI/CFs	2	–45.7	5.6
CNTs/Fe3O4/CFs	1.5	–59.9	0
CNTs/Fe3O4/PANI/CFs	2	–22.4	6.9
CNT/CFs	3	–44.6	7.44
CNTs/SiCf	3.35	–37.6	1.5
CNT/SiCf	4	–62.5	8.8
SiOC/CFs	2.3	–47.9	4.6
WMCNT/PPy/CFs	3.5	–17	6.12
MWCNTs/PDEOT/CFs	2	–39	4.5

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表 5 特征变量的随机森林算法与决策树算法重要性评分对比

特征变量	决策树算法重要性得分	随机森林算法重要性得分
厚度/[mm]	0.298	0.475
有效吸收带宽/[dB]	0.702	0.524

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表 6 不同分类算法归纳总结

分类算法	分类结果及分类误差
近邻法	近邻法不论按照“厚度/损耗”、“厚度/带宽”还是“带宽/损耗”进行组合，均能将数据准确地分为三类。
二次判别法	分界线是曲线，二次判别法不论按照“碳基/陶瓷”、“有机/碳基”、“有机/陶瓷”进行组合，发生误报、漏报的概率很小，均有1-2个样品被误分类，会有样品出现在分界线上。
Adaboost类算法	产生两个弱分类错误样本或者产生两个组合分类错误样本。
聚类算法	不论是二维还是三维空间，均将数据准确地分为三类。
GMM模型	高斯成分个数k=3时最能刻画数据分布；根据标签确定初始值的方法最能刻画数据分布。
最优聚类数	最优聚类数的数量为3或者6。
排序算法	反射损耗是三个数据标签里最重要的标签。
备注：以上内容的代码引用自参考文献[26–27]。

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