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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: 9/3/2022 | 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