A Comprehensive Review of Artificial Intelligence for Image- and Signal-Based Nondestructive Testing in Aerospace Structures

Rexcharles Enyinna Donatus

doi:10.57159/jcmm.4.6.25230

Authors

Rexcharles Enyinna Donatus Department of Aerospace Engineering, Air Force Institute of Technology, Kaduna, Nigeria 800283 https://orcid.org/0009-0007-1557-4161

DOI:

https://doi.org/10.57159/jcmm.4.6.25230

Keywords:

Artificial intelligence, Deep learning, Machine learning, Non-Destructive Testing, Digital Image Processing, aerospace structures

Abstract

Ensuring the structural integrity of aerospace components requires inspection techniques that can detect diverse surface and subsurface flaws in increasingly complex materials and geometries. Although conventional nondestructive testing (NDT) remains essential, its dependence on manual interpretation and limited automation has created demand for more objective, scalable solutions. This review presents a structured synthesis of artificial intelligence advancements in nondestructive testing, organized around the two dominant data paradigms: image-based and signal-based inspection. Image modalities, such as radiography, infrared thermography, and visual inspection, generate spatial information well-suited to convolutional networks, segmentation models, and vision transformers. Signal modalities, including ultrasonics, acoustic emission, eddy currents, and vibration analysis, produce temporal or spectral data that can be effectively modeled by recurrent neural networks (RNNs), hybrid CNN-LSTM architectures, and emerging transformers. The review compares these modalities, evaluates their diagnostic performance, and highlights challenges related to dataset scarcity, inconsistent annotation standards, domain shift, interpretability, and certification. Particular attention is given to multimodal fusion strategies that integrate spatial and temporal cues through attention-enabled hybrid models to improve robustness and decision reliability. Practical aerospace scenarios such as composite panel inspection, ultrasonic C-scan analysis, radiographic porosity detection, and structural health monitoring are examined to illustrate operational readiness. Despite significant progress, most models rely on controlled datasets, lack standardized evaluation protocols, and provide limited insight into uncertainty or failure modes. Advancements in open benchmarks, explainable and physics-informed architectures, and digital-twin-enabled deployment are essential for achieving trustworthy, certifiable AI-based NDT. Overall, the review provides a concise roadmap for developing intelligent and interpretable NDT systems for next-generation aerospace applications.

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