Adaptive neuro-fuzzy inference system trained for sizing semi-elliptical notches scanned by eddy currents

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Mohseni, Ehsan and Viens, Martin and Xie, Wen-Fang (2020) Adaptive neuro-fuzzy inference system trained for sizing semi-elliptical notches scanned by eddy currents. Journal of Nondestructive Evaluation, 39 (5). ISSN 0195-9298 (https://doi.org/10.1007/s10921-019-0648-8)

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Abstract

The present study explores the capability of COMSOL Multiphysics, as a finite element modelling (FEM) tool, to model the interaction between a split-D differential surface eddy current (ECT) probe and semi-elliptical surface electrical discharge machined (EDM) notches. The effect of the small probe’s lift-off and tilt on its signal is investigated through modelling and subsequently, the simulation outcomes are validated using the probe’s impedance measurements. In the next stage, an adaptive neuro-fuzzy inference system (ANFIS) is designed to take the signal features as inputs and consequently, provide the length of the scanned notch as the system’s output. The system is trained by extracted features of thirty model-generated signals obtained from scanning of the same number of semi-elliptical notches by means of the split-D probe. The trained ANFIS is tested afterwards using the measured signals of 3 calibration EDM notches together with 5 model-based ones. A very low average estimation error is observed with regard to the length estimation of the test notches and the accuracy of the length estimation is found to be quite reasonable.

ORCID iDs

Mohseni, Ehsan ORCID logoORCID: https://orcid.org/0000-0002-0819-6592, Viens, Martin and Xie, Wen-Fang;
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