Method of measuring the mechanical properties of ferromagnetic materials based on magnetostrictive EMAT characteristic parameters
| Autor: | Yuan Zhang, Yang Zheng, Yan Mi, Ping Wang, Entao Yao, Chenglong Tang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Applied Mathematics 020208 electrical & electronic engineering 010401 analytical chemistry Magnetostriction 02 engineering and technology Condensed Matter Physics Microstructure Magnetostatics 01 natural sciences Signal 0104 chemical sciences Electromagnetic induction Condensed Matter::Materials Science Ferromagnetism 0202 electrical engineering electronic engineering information engineering Condensed Matter::Strongly Correlated Electrons Electrical and Electronic Engineering Composite material Instrumentation Electromagnetic acoustic transducer Intensity (heat transfer) |
| Zdroj: | Measurement. 168:108187 |
| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2020.108187 |
| Popis: | The mechanical and micromagnetc properties of ferromagnetic materials are overall associated with their microstructure parameters. The magnetostriction refers to one of the micromagnetic properties. The magnetostrictive properties of ferromagnetic materials are characterized by the relationships between the intensity of the SH (Shear Horizontal) waves with magnetostrictive effect under the identical excitation and the static bias magnetic induction intensity. In this study, the characteristic parameters exhibiting sensitivity to the magnetostrictive properties of ferromagnetic materials in the relationships between the intensity of EMAT (electromagnetic acoustic transducer) signal and static magnetic field strength were extracted. Besides, with BP (back propagation) neural network, the mapping association between the magnetostrictive parameters and the mechanical properties of the materials was developed, and the mechanical properties of the materials were non-destructively determined. By verifying the testing samples of cold-rolled steel specimens synthesized by Bao-steel Inc., the method was found to be capable of achieving high prediction accuracy; the passing rate of relative error less than 10% can reach 90%. |
| Databáze: | OpenAIRE |
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