In order to reduce the complexity and compensate for the poor reliability of the resistivity measurement instrument for high-temperature liquid metal, a set of the non-contact electrical resistivity measuring system is designed based on the electromagnetic induction principle and BP neural network. It consists of four components: the eddy current probe coil, the impedance analyzer, the temperature acquisition device, and the related software. A finite element simulation model of probe coil is established in COMSOL. BP neural network model is implemented to approximate the implicit mapping between the reactance variation of the coil and the resistivity of the metal since the relationship is difficult to be explicitly expressed by the combination of elementary functions. Depending on BP neural network, resistivity can be predicted from the data of coil collected by the system. The qualitative measurement of liquid zinc’s resistivity during the cooling process is realized. The results clearly reflect the properties of resistivity in the liquid state as well as the solid state. The resistivity mutation phenomenon during the phase change period is observed. The experimental results indicate that the system can be applied to the resistivity measurement of metal in liquid and solid states with considerable reliability and detection capability.
Sensor
| 氏名 | 専攻 | 研究室 | 役職/学年 |
|---|---|---|---|
| LIU CHUNTING | 知能情報学専攻 | 生命システム情報学講座 バイオ情報ネットワーク分野 | 博士1回生 |