journal article·2026-06-11

Solid-state non-array vector-bias-field EMAT for arbitrary-direction ultrasonic guided-wave measurement and imaging

Zhengqing Zuo, Ning Pei, Weimin Zhang, Fangxing Li, Xinyan Tan, Xiaohai He, Shicheng Fan, Chandan Sheikder, Mahmoud Mabrouk

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Abstract

Electromagnetic acoustic transducers (EMATs) have been widely used for ultrasonic guided-wave non-destructive testing (NDT) and structural health monitoring (SHM) of plate-like structures. However, practical azimuthal imaging often relies on mechanical scanning of the EMAT or on multi-element arrays with digital beam steering, and coherent focusing methods such as the total focusing method (TFM) and the synthetic aperture focusing technique (SAFT) generally require full-matrix or synthetic-aperture measurements acquired from multiple elements and multiple spatial locations. This paper presents a fixed-position, solid-state, non-array vector-bias-field EMAT (VBF-EMAT) for arbitrary-direction Lamb wave measurement and circumferential imaging. In the proposed transducer, a multi-pole electromagnet synthesizes a continuously steerable vector bias magnetic field, and direction selectivity is physically achieved at the transducer level. Under the synthesised vector bias field, guided waves propagating along a prescribed azimuth are generated through magnetostriction. This mechanically stationary solid-state architecture enables directional control without requiring mechanical rotation. The geometries of the multi-pole electromagnet and transceiver coil were optimized to improve the uniformity of the bias field and the directional response of the transducer. Experiments on steel plate specimens demonstrate 360° circumferential S0-mode Lamb wave imaging and accurate defect localisation without array deployment.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Thermography and Photoacoustic Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Beijing Institute of Technology

University of Science and Technology Beijing

Alexandria University

Funding

National Natural Science Foundation of China

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