Inversion of Physically Recorded Ultrasonic Waveforms Using Adaptive Learning Network Models Trained on Theoretical Data

Abstract

The objective of this work has been to demonstrate the feasibility of estimating automatically the size and orientation of subsurface defects in metals. The approach has been to (1) obtain computer-generated spectra from various elastic scattering theories, (2) use these spectra to train empirical nonlinear Adaptive Learning Network (ALN) models, and (3) evaluate the theoretically trained ALN\u27s on eight physically recorded defect specimens via a blind test. The results demonstrate that very good defect characterization is possible and that a fully automatic and general purpose NDE system can be implemented. An average orientation error of 10.2 degrees has been achieved and the defect average volume error is 17.5 percent