Volume 10,Issue 9
Artificial‑Intelligence‑Empowered Optimization and Intelligent Recognition of Non‑Destructive Testing Technology
As high‑end manufacturing imposes increasingly stringent requirements on component quality, conventional non‑destructive testing (NDT) encounters difficulties in massive‑data interpretation, micro‑defect identification, and quantitative evaluation. The penetration of artificial intelligence into non‑destructive testing technologies creates a new paradigm for technical improvement and intelligent recognition in NDT. This paper discusses the subject from two perspectives: optimization paths and recognition strategies. It improves the end‑to‑end efficiency of the whole testing chain through four approaches: data augmentation, lightweight models together with edge acceleration, physics‑informed fusion imaging, and end‑to‑end workflow reconstruction. On the recognition dimension, it puts forward strategies including transfer‑learning‑based few‑shot defect recognition, multi‑modal attention‑fusion‑enabled grading, explainability‑assisted quantitative assessment, and online continual‑learning‑driven predictive health management.
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