Eddy current testing, as one of the promising techniques in non-destructive testing, is widely applied in various industrial applications achieving high accuracy and contactless to the target. In the testing process, electromagnetic calculations are crucial to evaluate the performance of sensor probes and inversion algorithms. Electromagnetic calculation can be summarised mainly into two categories: analytical methods and numerical methods. For the analytical methods, Dodd and Deeds analytical solutions have served to calculate the eddy current problems for several decades, but it can only be applied to infinite plates. In this research, based on the finding that the sample radius is related to the integration range, the modified analytical method is proposed which is capable of calculating the problems for the case where the radius of the sample plate does not satisfy the assumption of infinity. Further, for the measurement of ferrous plate magnetic permeability, it suffers from the lift-off effect. With increased lift-off, the phase of the measured impedance for steel plates reduces. Meanwhile, the magnitude of the impedance signal decreases. Based on these facts, a novel algorithm is developed to reduce the error of impedance phase for ferrous steels due to sensor lift-offs. By utilising the compensated phase, the prediction for the permeability can be more precise. The finite element method, as a numerical method, is a versatile tool for eddy currents simulations. However, the computation speed of eddy current three-dimensional modelling is rather slow. Therefore, two methods to accelerate the customised solver for crack detection are proposed. Numerical tests and experiments have been carried out to verify the proposed methods. From the flow patterns of eddy currents and the calculated inductance change, the effectiveness and robustness of the accelerated solver are proved. Numerical tests show that the computation time can be reduced significantly by utilising the accelerated approaches.
|Date of Award||1 Aug 2022|
- The University of Manchester
|Supervisor||Hujun Yin (Supervisor) & Wuliang Yin (Supervisor)|