Parameter estimation method of yield strength for ferromagnetic materials based on Pulsed Eddy Current

Abstract

In the current iron and steel industry production, the detection of the yield strength of ferromagnetic materials relies on loss detection. By determining the yield strength of the material through tensile tests, the material needs to be stretched to the yield limit, which greatly increases the cost of testing. This paper presents a method for estimating the yield strength parameters of ferromagnetic materials based on pulsed eddy currents. Firstly, the characteristics of time-domain and frequency-domain of pulsed eddy current response signals are extracted. Then, the back propagation neural network model of each signal characteristic and material yield strength is established, and finally the neural network is used to predict the yield strength of the material. Using the BP neural network model trained by features of pulsed eddy current, the yield strength of the specimens can be estimated. As a non-destructive testing method, the method has a prediction error of 5% or less, and has a certain practical value for reducing the detection cost of industrial production and improving the detection efficiency.