PurposeThe principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the measurements of the probe admittance (Y(f) = G(f)+ jB(f )). The purpose of this paper is to develop an alternative inversion tool able to predict the evolution of the complex permittivity (ε = ε′ – jε″) on a broad band frequency (f from 1 MHz to 1.8 GHz).
Design/methodology/approachThe inverse problem is solved using adaptive network based fuzzy inference system (ANFIS) which needs the creation of a database for its learning. Unfortunately, train ANFIS using f, G and B as inputs has given unsatisfying results. Therefore, an inputs selection procedure is used to select the three optimal inputs from new inputs, created mathematically from original ones, using the Jang method.
FindingsInversion results of measurements give, after training, in real time the complex permittivity of solid and liquid samples with a very good accuracy which prove the applicability of ANFIS to solve inverse problems in microwave characterization.
Originality/valueThe originality of this paper consists on the use of ANFIS with input selection procedure based on the Jang method to solve the inverse problem where the three optimal inputs are selected from 26 new inputs created mathematically from original ones (f, G and B).
Industrial equipment can be exposed to various types of damage during their long exploitation in harsh environments which might lead to corrosion cracking. Typically, a hidden crack is very dangerous since it is difficult to be detected by Non-Destructive Testing (NDT) techniques. Moreover, the integrity requirements of metallic structures cannot be satisfied by simply detecting the existence of cracks. However, the quantitative description of the cracks is still an issue that should be solved. This paper introduces a new approach to estimate the dimensions of hidden cracks inside a stainless steel plate based on Shear Vertical (SV) waves generated by an Electromagnetic Acoustic Transducer (EMAT). The Finite Elements Method (FEM) has been used to model the acquired signal by EMAT. Then, the characterization of hidden cracks is carried out by combining two methods; Time-of-Flight (ToF) technique that has been used to evaluate the crack height, and the Partial Least Square Regression (PLSR) method that has been used to estimate the crack width. The obtained results demonstrate the efficiency and the reliability of the adopted approach concerning the quantitative evaluation of the crack dimensions.
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