利用声发射对碳纤维增强聚合物夹层复合材料中的损伤模式进行聚类的集合特征选择

IF 3.4 3区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Advanced Engineering Materials Pub Date : 2024-11-20 DOI:10.1002/adem.202470063

Abdulkadir Gulsen, Burak Kolukisa, Umut Caliskan, Burcu Bakir-Gungor, Vehbi Cagri Gungor

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引用次数: 0

摘要

声发射在编号为 2400317 的文章中，Abdulkadir Gulsen 及其合作者介绍了一种新颖的集合特征选择方法，用于对 CFRP 夹层复合材料 AE 信号中与损伤模式相关的特征进行排序。随后，在无监督聚类模型中利用排序的特征来识别损伤模式。比较结果表明，除常用特征外，其他特征（如部分幂）与损伤模式也有很强的相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission

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Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission

Acoustic Emission

In article number 2400317, Abdulkadir Gulsen and co-workers present a novel ensemble feature selection methodology to rank features relevant to damage modes on AE signals in CFRP sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, in addition to the commonly used features, other features, like partial powers, have a robust correlation with damage modes.

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来源期刊

Advanced Engineering Materials 工程技术-材料科学：综合

CiteScore

5.70

自引率

5.60%

发文量

544

审稿时长

1.7 months

期刊介绍： Advanced Engineering Materials is the membership journal of three leading European Materials Societies - German Materials Society/DGM, - French Materials Society/SF2M, - Swiss Materials Federation/SVMT.