Roberto Miorelli is Research Engineer in electromagnetic modeling and machine learning (ML) within the nondestructive testing and evaluation department, University Paris-Saclay, CEA List Institute, Palaiseau, France. In the ADVISE project, he is involved in Work Package 4 in developing model-driven ML strategies applied to ADVISE inspection problems based on ultrasound testing methods.
Model based defect characterization
Accurate defect characterisation is desirable in ultrasonic non-destructive evaluation as it can provide quantitative information about the defect type and geometry. For defect characterisation using ultrasonic arrays, high resolution images can provide the size and type information if a defect is relatively large. However, the performance of image-based characterisation becomes poor for small defects that are comparable to the wavelength. An alternative approach is to extract the far-field scattering coefficient matrix from the array data and use it for characterisation. Defect characterisation can be performed based on a scattering matrix database that consists of the scattering matrices of idealised defects with varying parameters. In this presentation, the methodology and application of two different approaches are described.
The first method is based on the statistical distribution of the defect data in polycrystalline materials, and it performs characterisation within the Bayesian framework. The second approach relies on a supervised machine learning (ML) schema based on a scattering matrix database, which is used as the training set to fit the ML model exploited for the characterisation task. The performance of both approaches is compared and discussed.