High-Precision Electrical Impedance Tomography for Electrical Conductivity of Metallic Materials

• Published in: Advances in materials science and engineering 2022-01, Vol.2022, p.1-16
• Main Authors: Zhao, Shuanfeng, Miao, Yao, Chai, Rongxia, Zhao, Jiaojiao, Bai, Yunrui, Wei, Zheng, Ren, Simin
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Crystal structure; Electrical impedance; Electrical resistivity; Excitation; Flaw detection; Image quality; Image reconstruction; Imaging techniques; Inverse problems; Material properties; Measurement methods; Noise reduction; Residual stress; Stability analysis; Stress distribution; Tomography; Wavelet analysis
• ISSN: 1687-8434; 1687-8442
• DOI: 10.1155/2022/3611691

Metal materials are subject to deformation, internal stress distribution, and cracking during processing, all of which affect the distribution of electrical conductivity of the metal. Suppose we can detect the conductivity distribution of metal materials in real time. In that case, we can complete the inverse imaging of metal material properties, structures, cracks, etc. and realize nondestructive flaw detection. However, metal materials' small resistance, high electrical conductivity, and susceptibility of voltage signals to noise signal interference make an accurate measurement of metal conductivity challenging. Therefore, this paper addresses the problem of detecting the conductivity distribution of metals by investigating a high-precision four-electrode AC measurement method. This technical approach combines laminar imaging techniques with high-precision weak signal extraction methods. On this basis, a method and equipment for high-precision electrical impedance tomography of metallic materials’ electrical conductivity were established. The way specifies a new number of electrodes and adopts a model of spaced excitation reference measurements. Single-frequency sinusoidal AC signal is used for excitation, and Shannon wavelet analysis is used for signal extraction and noise reduction. Super-resolution reconstruction algorithms are used for resistivity distribution image reconstruction to improve image quality. Based on the results of various comparative experiments, it is clear that this new functional technique method has good imaging stability and operability and can perform tasks such as analyzing the internal conductivity distribution of metals. This research provides an effective way of new ideas for the safe detection of metal structures, the changes in crystal tissue structure, and the study of metal properties. In particular, it expands the scope of research in the development and application of resistance tomography, which has tremendous commercial potential research significance.