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AI method-apparatus for extracting crack-length from high-frequency AE signals
Reference #: 01536
The University of South Carolina is offering licensing opportunities for AI method-apparatus for extracting crack-length from high-frequency AE signals
Background:
In metallic structures, fatigue cracks are inevitable and need to be detected and quantified. However, the crack length information is hard to be estimated. It is imperative...
Published: 7/10/2026
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Updated: 6/1/2021
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Inventor(s): Victor Giurgiutiu, Hanfei Mei, Joseph Garrett, Kimberly Cardillo
Keywords(s): acoustic emission, artificial intelligence, crack length, fatigue crack growth, finite element simulation, machine learning, standing wave, structural health monitoring
Category(s): Engineering and Physical Sciences
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