Non-destructive testing (NDT) is the application of physical measurement
technology based on energy interaction with the material and its nonconformities.
The material’s response is sensed by transducers and sensors which—in most
cases—scan the component and document the results in inspection images.
However, NDT measures a physically defined quantity or even an intrinsic
property.
The difference between non-destructive evaluation (NDE) and NDT is in the
interpretation of the inspection data. NDE has to discuss the inspection results in
terms of quality elements and characteristics which are relevant to describe the
fitness of the material for use. In the case of macroscopic defects these are the
kind of defect (cracklike, globular) and its size and orientation to the main stress
directions; in the case of material property determination the parameters are
mainly mechanical properties. Therefore, in NDE one has to solve inverse
problems.
The solution of inverse problems based on mathematical procedures such as
integral equations is a strong developing discipline and most of the articles
prepared for this special issue of the journal have the objective of discussing the
latest state of the art in that field. However, practical NDE needs robust and
quick solutions which are to be applied mainly online. Therefore, we present here
inversion procedures based on multiple linear regression algorithms applied to
inspection data. We describe the calibration procedure to fit the free parameters
of the model functions and give examples of practical applications in industry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.