Monitoring Surface Defects Deformations and Displacements in Hot Steel Using Magnetic Induction Tomography

Abstract

Magnetic Induction Tomography (MIT) is a non-invasive imaging technique that has been widely applied for imaging materials with high electrical conductivity contrasts. Steel production is among an increasing number of applications that require a contactless method for monitoring the casting process due to the high temperature of hot steel. In this paper, an MIT technique is proposed for detecting defects and deformations in the external surfaces of metal, which has the potential to be used to monitor the external surface of hot steel during the continuous casting process. The Total Variation (TV) reconstruction algorithm was developed to image the conductivity distributions. Nonetheless, the reconstructed image of the deformed square metal obtained using the TV algorithm directly does not yield resonable images of the surface deformation. However, differential images obtained by subtracting the image of a perfect square metal with no deformations from the image obtained for a deformed square metal does provide accurate and repeatable deformation information. It is possible to obtain a more precise image of surface deformation by thresholding the differential image. This TV-based threshold-differencing method has been analysed and verified from both simulation and experimental tests. The simulation results reported that 0.92 % of the image region can be detected, and the experimental results indicated a 0.57 % detectability. Use of the proposed method was demonstareted in a MIT device which was used in continuous casting set up. The paper shows results from computer simulation, lab based cold tests, and real life data from continoeus cating demonstating the effectiveness of the proposed method.

In situ steel solidification imaging in continuous casting using magnetic induction tomography

“…where α is the regularization parameter, ∥∇∆σ∥ 1 is the total variation functional, ∇ is the gradient and ∥ • ∥ 1 is the l 1 norm. The theory of different types of TV inverse solvers has been explained and investigated in detail in [14,15]. The TV algorithm is to solve the constrained optimization problem: ∆σ = argmin ∆σ ∥∇∆σ∥ 1 such that ∥J∆σ − ∆v∥ 2 < ρ (8) where ρ accounts for the noise data.…”

“…In reality, the billet can move, so to enable the comparison of the MIT images with the thermal model we need to account for positional movement of the billet. This can be done using MIT images and the method proposed in [15]. Figures 9(c) and (d) show the relative movement between the MIT coil array and the billet (mainly due to the billet's mechanical movement).…”

Multi-Frequency Magnetic Induction Tomography System and Algorithm for Imaging Metallic Objects

“…The described unit is designed for simplicity and compactness so that it is easily relocatable to industrial environments. Li et al have used a similar system for studying defect measurements in hot steel [ 29 ]. In this first development, the system operates from 1 to 100 kHz and collects the magnitude of the perturbed excitation field only.…”

Inverse Transformation in Eddy Current Tomography with Continuous Optimization of Reference Defect Parameters

“…It is based on the standard eddy current non-destructive evaluation method [2][3][4][5], where the tested object influences the coupling of at least two coils. ECT (eddy current tomography) setups either collect data from multiple sets of coils [6][7][8][9] or conduct measurements for different positions of the sample [10,11].…”