Hebei Food Inspection and Research Institute

Quantitative evaluation of defect parameters in magnetic flux leakage testing based on an improved magnetic dipole model and convolutional neural networks

Acquiring low-cost magnetic flux leakage (MFL) signals while accurately inverting defect geometric parameters remains a significant challenge in magnetic flux leakage non-destructive testing. To address this issue, this paper proposes a simulation-driven defect inversion framewor...