A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

The safety testing of ferromagnetic materials, which are the main materials for various machines and equipment, is particularly important. Stress concentration zones (stress defects) cause stress corrosion of ferromagnetic materials, and also have the potential to cause direct damage to ferromagnetic materials. Estimation of stress sources state using electromagnetic nondestructive measurement methods is a critical and difficult problem. In this paper, a visual and intelligent identification method of stress defects in ferromagnetic materials by low frequency AC magnetic flux leakage (ACMFL) technique is proposed. A new three-point compression experiment was designed in this paper. Time-difference vision is established to analyze the ACMFL signal caused by stress defects. A visual transformed convolutional neural network deep learning algorithm has been proposed to identify grayscale patterns pre-processed by the time-difference vision. The results show that the method proposed in this paper elucidates the relationship between the time-difference vision of a stress defect and the stress source state of the mechanical stress. Our proposed method allows to analyze the pressure indenter size of the pressure source of stress defects.