Abstract
In the process of eddy current testing (ECT) of surface and subsurface defects of aviation aluminum alloy plates, the setting of parameters is important to the test results. Inappropriate test parameters can cause false detection or even missed detection of defects. To address this problem, the effects of probe type, coil size, and excitation frequency on the accurate identification and quantitative evaluation of surface and subsurface defect detection were studied and analyzed in this study to determine the best testing parameters. The experimental results show that the absolute probe with an outer radius of 3.3 mm has better detection performance for aviation aluminum alloy plate defects. There are different optimal excitation frequency ranges for the surface and subsurface defects. An excitation frequency of 80 kHz to 90 kHz can be used for the detection of unknown defects.
Highlights
Aluminum alloy materials are widely used in the manufacture of civil aircraft flaps, skins, and other structural parts owing to their advantages, such as low density, high strength, good processability, and strong corrosion resistance
Among various nondestructive testing (NDT) technologies, eddy current testing (ECT) is the most suitable for the detection of corrosion and fatigue cracks in aircraft aluminum alloy plates owing to its fast detection speed, wide detection range, easy automation, and higher detection performance for surface and subsurface defects of detected objects [1–5]
Four surface defects and subsurface defects with different depths were constructed in an aviation aluminum alloy plate, and the defects were detected by frequency sweeping
Summary
Aluminum alloy materials are widely used in the manufacture of civil aircraft flaps, skins, and other structural parts owing to their advantages, such as low density, high strength, good processability, and strong corrosion resistance. Based on the time-frequency analysis of the pulsed eddy current defect detection signal, combined with k-means clustering and expectation maximization, Hosseini and Lakis realized the automatic detection of the distribution of subsurface defects in each layer of a multilayer aluminum alloy plate structure [7]. He et al realized the automatic detection of layered defects of an aircraft two-layer aluminum alloy plate structure based on the constructed defect feature and support vector machine classification algorithm and further studied the influence of different lift-off distances on the detection results [8].
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