Fast defect parameter estimation based on magnetic flux leakage measurements with GMR sensors
(2011)
We present a fast inverse scheme that is capable of simultaneously estimating the parameters depth, opening and length for rectangular 3D geometries of surface-breaking defects. The parameter estimation is realized by an iterative least-squares minimization using the trust-region reflective algorithm. A semi-analytic magnetic dipole model that allows the sensor characteristics to be incorporated is used for predicting the stray magnetic fields. Giant magneto-resistance (GMR) measurements were carried out on a test specimen that includes a series of artificial defects. For the estimation of the defect depths relative errors between 0.6% and 15.9% have been obtained. Due to its very low computational costs, the inverse scheme can suitably be employed in automated production environments.
The determination of magnetic distortion fields caused by inclusions hidden in a
conductive matrix using homogeneous current flow needs to be addressed in multiple tasks of
electromagnetic non-destructive testing and materials science. This includes a series of testing
problems such as the detection of tantalum inclusions hidden in niobium plates, metal inclusion in
a nonmetallic base material or porosity in aluminum laser welds. Unfortunately, straightforward
tools for an estimation of the defect response fields above the sample using pertinent detection
concepts are still missing. In this study the Finite Element Method (FEM) was used for modeling
spherically shaped defects and an analytical expression developed for the strength of the response
field including the conductivity of the defect and matrix, the sensor-to-inclusion separation and the
defect size. Finally, the results also can be useful for Eddy Current Testing problems, by taking the
skin effect into consideration.