NEUP CINR-23-29106: Physics-Informed Artificial Intelligence for Non-Destructive Evaluation of Ceramic Composite Cladding by Creating Digital Fingerprints

The objective is to spatially map the material composition, structure, and defect distribution of SiCf-SiCm composite tubes from ultrasonic wavefields measured of the materials the and defects within them. Specifically, we learn the unique ultrasonic fingerprints (i.e., dispersion relations and mode shapes) of the SiCf-SiCm composites using physics-informed machine learning to assess the quality of the manufactured tubes based on their spatial-spectral ultrasonic characteristics.

Additional Info

Field	Value
Awarded Institution	University of Florida
PI	Joel Harley