Estimation Method of Mechanical Properties of Ferromagnetic Materials Based on Pulsed Eddy Current

• Published in: Russian journal of nondestructive testing Vol. 57; no. 2; pp. 170 - 179
• Main Authors: Kaiyu, Li, Pengcheng, Qiu, Ping, Wang, Zixiang, Lu, Zhengda, Zhang
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.02.2021; Springer Nature B.V
• Subjects: Algorithms; Back propagation networks; Characterization and Evaluation of Materials; Chemistry and Materials Science; Destructive testing; Eddy currents; Electromagnetic Methods; Feature extraction; Ferromagnetic materials; Hilbert transformation; Iron and steel industry; Magnetism; Materials Science; Mechanical properties; Parameter estimation; Spectra; Structural Materials; pulsed eddy current; yield strength; BP neural network; multi-feature
• ISSN: 1061-8309; 1608-3385
• DOI: 10.1134/S1061830921020066

In the current steel industry production, the detection of the mechanical properties of ferromagnetic materials relies on destructive testing, which greatly increases the cost of production. In this paper, a method is proposed to estimate the mechanical properties of ferromagnetic materials based on the pulsed eddy current (PEC) techniques. Firstly, the traditional features of the PEC signal, such as differential signal peaks and spectral amplitudes, are applied to the quantitative estimation of mechanical properties. Secondly, a method based on the empirical mode decomposition and Hilbert-Huang transform is used to extract the marginal spectral peak and marginal spectral energy as new features. Finally, BP neural network algorithm is introduced to quantitatively estimate the mechanical property parameters. The results show that the combination of both traditional and new features is suitable for mechanical properties estimation. The method has high accuracy for the quantitative estimation of mechanical performance parameters.