1. 武汉大学,武汉,430072
2. 北京强度环境研究所可靠性与环境工程技术重点实验室,北京,100076
纸质出版:2025
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黄鑫, 屈文忠, 蒋琪, 等. 基于域自适应迁移学习的隔热瓦导波脱粘检测方法研究[J]. 航空制造技术, 2025,(21).
HUANG Xin, QU Wenzhong, JIANG Qi, et al. Domain-Adaptive Transfer Learning-Based Guided Wave Method for Debonding Detection in Thermal Protection System[J]. Aeronautical Manufacturing Technology, 2025, (21).
黄鑫, 屈文忠, 蒋琪, 等. 基于域自适应迁移学习的隔热瓦导波脱粘检测方法研究[J]. 航空制造技术, 2025,(21). DOI: 10.16080/j.issn1671-833x.2025.21.076.
HUANG Xin, QU Wenzhong, JIANG Qi, et al. Domain-Adaptive Transfer Learning-Based Guided Wave Method for Debonding Detection in Thermal Protection System[J]. Aeronautical Manufacturing Technology, 2025, (21). DOI: 10.16080/j.issn1671-833x.2025.21.076.
针对可重复使用飞行器热防护结构在复杂多场耦合环境下易产生层间脱粘损伤的关键问题,提出基于超声导波与域自适应迁移学习的无损检测方法。通过设计4 类典型粘接缺陷的隔热瓦试件,结合双向正交扫描策略与超声激励– 接收机制,实现粘接区域的高效覆盖检测。针对试件个体差异引起的信号漂移问题,采用基于峰值比例阈值的相位对齐方法,通过优化窗口长度同步保留损伤敏感特征并抑制噪声干扰。进一步构建域自适应迁移学习网络(Domain-adaptive transfer learning,DATL),实现跨试件损伤特征的分布对齐。试验表明,在跨试件测试场景下,DATL 模型准确率仅下降3.9%,域间分布差异指数从0.31 降至0.10 ;在目标域数据量不足40% 时,其准确率仍达85%,较卷积神经网络(Convolutional neural network,CNN)提升19.4%。该方法缓解了对损伤类型和试件一致性的依赖,可降低在役热防护结构脱粘检测的误报率与漏检率,为可重复使用飞行器的快速无损检测与健康评估提供了一种可行的解决参考方案。
Aiming at the critical issue of interlayer debonding damage susceptibility in reusable launch vehicle thermal protection structures under complex multi-physics coupling environments
a non-destructive testing method integrating ultrasonic guided waves with domain-adaptive transfer learning was proposed. Four typical bonding types were designed in thermal protection tile specimens
enabling efficient full-coverage inspection of bonded areas through a bidirectional orthogonal scanning strategy coupled with an ultrasonic excitation-reception mechanism. To solve the problem of signal drift caused by individual differences of specimens
an adaptive phase alignment method based on peak proportion threshold is proposed
and an appropriate window length is selected to realize the retention of key features of debonding damage while suppressing the interference of redundant data. A Domain-Adaptive Transfer Learning (DATL) was further proposed to align cross-specimen damage feature distributions. Experimental results demonstrate that in cross-specimen testing scenarios
the DATL model exhibits only a 3.9% accuracy decline
with inter-domain distribution discrepancy reduced from 0.31 to 0.10. With target domain data below 40%
DATL achieves 85% accuracy
outperforming CNN by 19.4%. The methodology mitigates reliance on damage patterns and specimen consistency
effectively reducing false alarms and missed detections in debonding testing for in-service thermal protection systems
which provides a practical solution for rapid non-destructive evaluation and structural health monitoring of reusable launch vehicle.
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