Multi-Parameter Electromagnetic Measurement and Neural Network-Based Fatigue Degree Assessment in Ferromagnetic Materials

Fatigue failure is a common mode of failure in ferromagnetic materials. Nondestructive electromagnetic testing technology can detect microstructural changes in ferromagnetic materials, but it is challenging to accurately estimate fatigue levels using a single parameter. To address this, a method for evaluating the fatigue degree of ferromagnetic materials is proposed, combining multi-parameter electromagnetic measurements with neural networks. First, a multi-electromagnetic testing system is established to collect four types of electromagnetic parameters, which include magnetic Barkhausen noise, incremental permeability, tangential magnetic field and hysteresis loop. Then, correlation analysis and principal component analysis are performed to reduce the dimensionality of the multi-parameter data, constructing an effective feature parameter set. Finally, an improved neural network model is used to predict fatigue levels. Experimental results on Q345 steel samples demonstrate that this method can accurately assess the fatigue degree of ferromagnetic materials, achieving an accuracy of 91%, enabling early fatigue detection and life prediction for materials.