Artificial intelligence Defect Detection Robustness inReal-time Non-Destructive Testing of Metal Surfaces
Artificial intelligence (AI) is revolutionizing defect detection by employing advanced computational techniques to enhance accuracy and efficiency. Through machine learning methods and deep neural networks, it is possible for AI systems to learn from diverse datasets and accurately identify de...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-03-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30808 |
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| Summary: | Artificial intelligence (AI) is revolutionizing defect
detection by employing advanced computational techniques to
enhance accuracy and efficiency. Through machine learning
methods and deep neural networks, it is possible for AI
systems to learn from diverse datasets and accurately
identify defects across various applications. In addition
to minimizing human intervention, AI-driven defect
detection provides real-time analysis, offering an
important assistance for industries.
Metal surfaces play a critical role in a wide range
of industries. Their structural integrity, durability, and
functionality depend heavily on their quality, making
surface inspection a vital process. Characteristics such as
texture, finish, corrosion resistance, and the presence of
defects like scratches, dents or cracks, can significantly
impact performance and longevity. Advanced inspection
techniques, such as non-destructive testing (NDT) and
surface characterization methods, are often employed to
evaluate metal surfaces for flaws.
Real-time NDT of metal surfaces is essential for
maintaining the integrity and reliability of metal
components without causing any damage. This technique
enables the immediate detection of defects that could
compromise the performance and safety of metal structures.
By incorporating advanced AI technologies, real-time NDT
enhances accuracy, shortens inspection times, and delivers
instant feedback, making it a vital tool for metal surface
inspection.
However, the robustness of AI-based defect detection
presents uncertainties in the NDT process of metal
surfaces, particularly in real-time scenarios under varied
and challenging conditions. Factors such as surface
variability, environmental influences, and defect types can
significantly impact the performance of an AI real-time NDT
system. In this study, we identify and summarize the key
factors that potentially affect metal surface NDT. Given
the diversity of defects, we propose a multi-model fused AI
approach for surface detection. Additionally, we establish
a real-time, cost-effective NDT platform to quantitatively
assess the robustness of the AI model in practical, online
settings. The primary robustness-affecting factors and AI
model uncertainties are verified, providing insights for
robust NDT system design.
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| ISSN: | 1435-4934 |