Sensitivity analysis of simulations for magnetic particle inspection using the finite-element method

Lee, J.Y.; Lee, S.J.; Jiles, David; Garton, M.; Lopez, R.; Brasche, L.

doi:10.1109/tmag.2003.816152

Cited by 26 publications

(11 citation statements)

References 4 publications

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“…The distribution of the magnetic field over the defect is in three independent axes, namely X direction component B x , Y direction component B y and Z direction component B z . By analysing the leakage field [17,18] , it can be known that the MFL signal's B y peakpeak value is strongly related to the defect depth and width, and the interval of B y peak-peak value is strongly related to the defect length. Because of this relationship, B y is chosen as the input data, and the defect length, width and depth are chosen as the output data.…”

Section: Experimental Schemementioning

confidence: 99%

Reconstruction of 3D defect profiles from the MFLT signals by using a radial wavelet basis function neural network iterative model

Xu¹,

Wang²,

Zuo³

et al. 2012

insight

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To achieve multi-resolution approximation of 3D defect profile reconstruction from magnetic flux leakage (MFL) signals, a radial wavelet basis function neural network iterative model, which contains a forward model and an inverse model based on a parallel radial wavelet basis function neural network (PRWBFNN), is proposed. The forward model in the loop is to determine the MFL signals for a given set of flaw parameters, and the inverse model is used to predict the profile given the measured value of the MFL signals and acts to constrain the solution space. This approach iteratively adjusts the weights of the inverse network to minimise the error between the measured and predicted values of the MFL signals. The reconstruction results of different defects indicate that significant advantages over other neural network-based defect characterisation schemes could be obtained.

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Section: Experimental Schemementioning

confidence: 99%

Reconstruction of 3D defect profiles from the MFLT signals by using a radial wavelet basis function neural network iterative model

Xu¹,

Wang²,

Zuo³

et al. 2012

insight

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“…Both types of elements will be used in this chapter to solve 3-D BVP in electro magnetics. Furthermore, h igher order elements will be used to illustrate the improvement in accuracy of the numerical solution without necessarily increasing the number of elements in the finite element mesh [3] [4].…”

Section: A the Finite Element Analysis Theorymentioning

confidence: 99%

A Study on Magnetic Force of the Large Displacement Hybrid Suspension System Based on Finite Element Method

Wen

Ming

Liu

et al. 2012

2012 Eighth International Conference on Computational Intelligence and Security

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A method for the computation of local magnetic force in suspension system is proposed. Precise calculation of magnetic force in electromagnet is significant to structure design. Therefore, on the basis of finite element method (FEM) and local virtual work method, a mathematical model is built according to the principle of virtual work equivalent, so as to calculate the magnetic force. Application shows it has good accuracy and extensive applicability.

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“…The length and wall thickness of the sample were chosen to be 16 cm and 1 cm, respectively. The magnetic force per unit volume of magnetic particles to retain them is proportional to the product of, the susceptibility of magnetic particles, external magnetic field and the field gradient [3][4][5]. The defect size was varied during the batch of simulation tests.…”

Section: Numerical Simulation Design For Mpi Testsmentioning

confidence: 99%

“…The analysis of MPI properties that affect sensitivity and reliability contributes not only reductions in inspection design cost and time but also improvement of analysis of experimental data. Computational advances enabled the numerical simulations of MPI problems for a more complicated geometry [4][5][6]. Computational advances enabled the numerical simulations of MPI problems for a more complicated geometry [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Generic Algorithm of Magnetic Particle Inspection Using Finite Element Modeling

Lee¹,

Lee²,

Melikhov³

et al. 2004

AIP Conference Proceedings

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Magnetic particle inspection (MPI) is a widely used nondestructive inspection method for aerospace applications essentially limited to experiment-based approaches. The analysis of MPI characteristics that affect sensitivity and reliability contributes not only reductions in inspection design cost and time but also improvement of analysis of experimental data. The material is assumed to have isotropic ferromagnetic properties in this research. The objective of this research is to develop a software package on the use of the finite element method (FEM) for magnetic particle inspection simulations with reversible nonlinear permeability of materials in order to give numerical simulations of MPI for defects with various sizes. We employed an FEM for numerical calculations because this is known to be suitable for complicated geometric objects such as the part shapes encountered in aviation components and defects of concern. The magnetic field gradient and magnetic force at the sites of defects having different widths and depths have been calculated. The simulated results can be used to assist in understanding the behavior of magnetic particles around a defect.

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Sensitivity analysis of simulations for magnetic particle inspection using the finite-element method

Cited by 26 publications

References 4 publications

Reconstruction of 3D defect profiles from the MFLT signals by using a radial wavelet basis function neural network iterative model

Reconstruction of 3D defect profiles from the MFLT signals by using a radial wavelet basis function neural network iterative model

A Study on Magnetic Force of the Large Displacement Hybrid Suspension System Based on Finite Element Method

Generic Algorithm of Magnetic Particle Inspection Using Finite Element Modeling

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