For the detection of defects in aluminum laser welds an anisotropic magnetoresistive (AMR) sensor array was implemented in an eddy current testing system. The reliability of weld testing is strongly influenced by the texture of the laser weld whose field response significantly superimposes the defect’s magnetic signature. A finite-element model was used to determine the influence of the weld’s topology on the detection of defects such as porosities and inclusions hosted in the aluminum matrix. When using an AMR sensor array with field sensitivity of better than 1nT∕√Hz defects with a radius smaller than 100μm could be detected and classified.
Abstruc+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 signalto-noise ratio is required. Our system provides a large analog dynamic range of more than 140 dBIdHz 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.
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