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<p>This paper introduces a deep learning (DL)-enhanced X-ray computed tomography (CT) approach for detection of defects in composite structures. While X-ray CT offers high-fidelity defect detection, test specimen size limitations restrict its application to large aerospace components. Inclined CT (ICT) addresses these size constraints by keeping X-ray source and detector on the different sides of a stationary test specimen. This system geometry results in a limited angular data 3D reconstructions that produce significant artifacts that may represent defects incorrectly. This research demonstrates that DL techniques, particularly the fine-tuned Segment Anything Model (SAM), can improve defect detection from ICT data. Methodology employs fine-tuning of SAM with a dataset of 1,800 images across ten synthetic phantoms with varying defect sizes and locations. The fine-tuned model was validated on an as-built aluminum test specimen, achieving over 70% accuracy in defect detection and 98% accuracy in overall shape detection. Validation with carbon fiber reinforced polymer specimens containing Teflon inserts yielded improved results compared to ICT reconstruction methods, indicating practical applicability. The findings suggest that DL-enhanced ICT can offer detection capabilities comparable to full CT while preserving the large-structure compatibility of ICT, making it a viable non-destructive inspection method for aerospace industry applications.</p>

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Deep Learning-Enhanced X-Ray Computed Tomography for Defect Detection in Composite Structures

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Abdullah Metiner,
  • Yuri Nikishkov,
  • Andrew Makeev,
  • Mustafa T. Koçyiğit

摘要

This paper introduces a deep learning (DL)-enhanced X-ray computed tomography (CT) approach for detection of defects in composite structures. While X-ray CT offers high-fidelity defect detection, test specimen size limitations restrict its application to large aerospace components. Inclined CT (ICT) addresses these size constraints by keeping X-ray source and detector on the different sides of a stationary test specimen. This system geometry results in a limited angular data 3D reconstructions that produce significant artifacts that may represent defects incorrectly. This research demonstrates that DL techniques, particularly the fine-tuned Segment Anything Model (SAM), can improve defect detection from ICT data. Methodology employs fine-tuning of SAM with a dataset of 1,800 images across ten synthetic phantoms with varying defect sizes and locations. The fine-tuned model was validated on an as-built aluminum test specimen, achieving over 70% accuracy in defect detection and 98% accuracy in overall shape detection. Validation with carbon fiber reinforced polymer specimens containing Teflon inserts yielded improved results compared to ICT reconstruction methods, indicating practical applicability. The findings suggest that DL-enhanced ICT can offer detection capabilities comparable to full CT while preserving the large-structure compatibility of ICT, making it a viable non-destructive inspection method for aerospace industry applications.