Intelligent Non-destructive Quantitative Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-reflection Infrared Thermography

YANG Yang; CHEN Hongen; LIU Bofan; TONG Zongfei; PEI Cuixiang; CHEN Zhenmao

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Intelligent Non-destructive Quantitative Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-reflection Infrared Thermography

更新时间：2025-11-18

- Intelligent Non-destructive Quantitative Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-reflection Infrared Thermography
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2025)
- 作者机构：
  
  1.西安交通大学航天航空学院,陕西省西安市710049
  2.西安交通大学复杂服役环境重大装备结构强度与寿命全国重点实验室,陕西省西安市710049
  3.西安交通大学陕西省无损检测与结构完整性评价工程技术研究中心,陕西省西安市710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP274
- Received：03 September 2025，
  
  Revised：2025-10-22，
  
  Accepted：28 October 2025，
- 稿件说明：
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YANG Yang, CHEN Hongen, LIU Bofan, et al. Intelligent Non-destructive Quantitative Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-reflection Infrared Thermography[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2025.
DOI：

YANG Yang, CHEN Hongen, LIU Bofan, et al. Intelligent Non-destructive Quantitative Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-reflection Infrared Thermography[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2025. DOI：

摘要

针对大型可展开天线碳纤维复合材料管壁缺陷传统检测方法检测距离近、效率低、智能化不足的问题，提出了一种基于图像处理和深度学习算法的碳纤维管脱粘缺陷镜面激光红外检测智能化定量评估方法。首先，基于Otsu算法进行检测区域自动识别提取，并对提取后的图像序列进行主成分分析处理，有效抑制噪声干扰并提升缺陷特征对比度；其次，提出了一种基于深度学习算法的缺陷智能化识别和分割算法，通过决策级融合策略实现缺陷区域的精准快速识别，并基于区域约束分割策略和逻辑或聚合算法实现缺陷形状的准确分割；最后，通过缺陷矫正与三维还原算法，实现了缺陷的三维可视化重构。对含内部脱粘缺陷的碳纤维管试件进行了检测，结果表明：所提方法对缺陷的定量评估结果与CT扫描结果高度一致，缺陷形状识别误差在工程误差内，验证了所提方法的准确性与有效性。该研究为可展开天线碳纤维管的远距离、高效和智能化无损定量评估提供了一定的指导意义。

Abstract

To address the challenge of short detection distance

low efficiency and insufficient intelligence in traditional detection methods for carbon fiber composite tube delamination defects in large deployable antennas

based on image processing and deep learning algorithms

an intelligent quantitative evaluation method for carbon fiber tube delamination defects with mirror-reflection laser infrared thermography is proposed. First

the detection area is automatically identified and extracted based on the Otsu algorithm

and the principal component analysis is conducted on the extracted image sequence to suppress noise interference and enhance the contrast of defect features. Next

a defect intelligent identification and segmentation algorithm based on deep learning algorithms is proposed. Through the decision-level fusion strategy

precise and rapid identification of defect is achieved

and based on the region-constrained segmentation strategy and logical OR aggregation

accurate segmentation of defect shape can be realized. Finally

the three-dimensional visualization reconstruction of the defects is accomplished through defect correction and the three-dimensional characterization algorithm. Experimental results of carbon fiber composite tubes with internal delamination defects show that the quantitative evaluation results through the proposed method are highly consistent with computed tomography (CT) within acceptable engineering tolerances

which validates the accuracy and effectiveness of the proposed method. This study provides guidance for remote

efficient

and intelligent quantitative nondestructive evaluation of carbon fiber tubes used in deployable space antenna structures.

关键词

Keywords

references

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