Abstract
A partial-contact stress corrosion crack (SCC) is electrically modeled as a crack region with non-zero conductivity in eddy current testing (ECT). This partial-contact effect is excluded by an optimally designed crack-conductivity-insensitive depth characterization signal function (DCSF), and consequently the master curves obtained from electric-discharge machining (EDM) notches can be utilized directly in the depth sizing of SCCs. Furthermore, a crack conductivity independent artificial neural network (ANN) is constructed so that the entire depth profile can be reconstructed regardless of the crack conductivity. These two approaches are numerically validated and applied to the characterization of SCCs in SUS304 from measurement ECT signals. The average depth of each SCC is fast estimated from the DCSF, and the detailed depth profile is reconstructed from ANN. The ECT depth-sizing results show reasonable agreement with UT-TOFD measurement.
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