A modulated fingerprint assisted machine learning method for retrieving elastic moduli from resonant ultrasound spectroscopy.

ABSTRACT: We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium-aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximu

SUBMITTER: Liu J 

PROVIDER: S-EPMC10090122 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature