Defect Detection in Thick Aircraft Samples Based on HTS SQUID-Magnetometry and Pattern Recognition

SQUID technology has recently evolved to the point that it can be used for industrial applications in Non-Destructive Evaluation (NDE). We present the implementation of an HTS SQUID magnetometer in an eddy current testing system to measure very thick structures in large aircraft. We measured a 62 mm-thick, bolted aluminum sample from EADS-Airbus, similar to the three-layered outer wing splice that is being proposed for the Airbus A-380. The combination of field sensitivities of a few pT/rootHz and a large dynamic range of about 140 dB/rootHz enabled us to detect defects at a depth of up to 40 mm. However a problem was presented by the fact that deep-lying defects which caused small field variations-were superimposed on field changes, in their turn caused by current distortions in the vicinity of the titanium bolts.. Separation of these two contributions was achieved through parameter optimization based on FEM simulations and signal processing. We report on the possibilities for flaw detection using adapted eddy current excitation

Defect detection and classification using a SQUID based multiple frequency eddy current NDE system

The probability of detection (POD) of hidden fatigue defects in riveted multilayer joints, e.g. aircraft fuselage, can be improved by using sophisticated eddy-current systems which provide more information than conventional NDE equipment. In order to collect this information, sensor arrays or multi-frequency excitation schemes can be used. We have performed simulations and measurements with an eddy current NDE system based on a SQUID magnetometer. To distinguish between signals caused by material defects and those caused by structures in the sample, such as bolts or rivets, a high signal-to-noise ratio is required. Our system provides a large analog dynamic range of more than 140 dB/root Hz in unshielded environment, a digital dynamics of the ADC of more than 25 bit (>150 dB) and multiple frequency excitation. A large number of stacked aluminum samples resembling aircraft fuselage were measured, containing titanium rivets and hidden defects In different depths in order to obtain sufficient statistical information for classification of the defect geometry. We report on flaw reconstruction using adapted feature extraction and neural network techniques