High deformation/damage localization accuracy of fibrous composites through deep-learning of single channel data from carbon nanotube sensors

IF 8.1 2区材料科学 Q1 ENGINEERING, MANUFACTURING

Composites Part A: Applied Science and Manufacturing Pub Date : 2024-10-09 DOI:10.1016/j.compositesa.2024.108512

Xiaowei Jiang, Wenjin Zhang, Xiaodong Wang, Ling Liu

引用次数: 0

Abstract

A convolutional neural network (CNN) model by deep-learning single channel data from a serpentine carbon nanotube sensor (S-CNT) with gradient distributed CNTs is proposed for locating deformation/damage in carbon fiber reinforced plastic (CFRP). The real-time resistance-time data caused by bending deformation of CFRP embedded with S-CNT are encoded into more discriminative 2D images for training the CNN. The results show that an accurate deformation localization within 1.5 mm for the trained positions can be obtained. Moreover, static-indentation loading reveals that the CNN model also has high localization accuracy for new deformation/damage locations in CFRP, with an error of less than 5.5 mm.

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通过深度学习碳纳米管传感器的单通道数据，实现纤维复合材料的高变形/损伤定位精度

通过深度学习带有梯度分布碳纳米管的蛇形碳纳米管传感器（S-CNT）的单通道数据，提出了一种卷积神经网络（CNN）模型，用于定位碳纤维增强塑料（CFRP）的变形/损伤。将嵌入 S-CNT 的碳纤维增强塑料弯曲变形引起的实时电阻时间数据编码成更具区分度的二维图像，用于训练 CNN。结果表明，经过训练的位置可以获得 1.5 毫米以内的精确变形定位。此外，静态压痕加载显示，CNN 模型对 CFRP 中新变形/损伤位置的定位精度也很高，误差小于 5.5 毫米。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Composites Part A: Applied Science and Manufacturing 工程技术-材料科学：复合

CiteScore

15.20

自引率

5.70%

发文量

492

审稿时长

30 days

期刊介绍： Composites Part A: Applied Science and Manufacturing is a comprehensive journal that publishes original research papers, review articles, case studies, short communications, and letters covering various aspects of composite materials science and technology. This includes fibrous and particulate reinforcements in polymeric, metallic, and ceramic matrices, as well as 'natural' composites like wood and biological materials. The journal addresses topics such as properties, design, and manufacture of reinforcing fibers and particles, novel architectures and concepts, multifunctional composites, advancements in fabrication and processing, manufacturing science, process modeling, experimental mechanics, microstructural characterization, interfaces, prediction and measurement of mechanical, physical, and chemical behavior, and performance in service. Additionally, articles on economic and commercial aspects, design, and case studies are welcomed. All submissions undergo rigorous peer review to ensure they contribute significantly and innovatively, maintaining high standards for content and presentation. The editorial team aims to expedite the review process for prompt publication.