Magnetic flux leakage testing (MFLT) is an important nondestructive testing method for the detection and evaluation of defects in magnetic materials. Magnetic field distribution in an MFLT system is usually simulated by the finite element method (FEM), which required large memory, high computation, and complication of the meshing process. In this paper, an alternative simulation method will be proposed using a deep neural network (DNN). The DNN method provides an easy way of simulation by feeding only the distribution of supplied current and the physical properties such as magnetic permeability without the need for the meshing process. Defects with arbitrary sizes were simulated under different configurations of the MFLT systems. The DNN was trained on the simulation results of the FEM and provided an accurate prediction of the magnetic field distribution of the unseen data. This study paves the way for designing optimized MFLT systems in a bigdata-driven method.

磁通量泄漏测试（MFLT）是一种重要的无损检测方法，用于检测和评估磁性材料中的缺陷。MFLT系统中的磁场分布通常是通过有限元方法（FEM）模拟的，这需要大内存，高计算量和复杂的啮合过程。在本文中，将提出一种使用深度神经网络（DNN）的替代仿真方法。DNN方法提供了一种简单的模拟方法，无需网格划分过程，只需馈入所分配的电流分布和物理特性（例如磁导率）即可。在MFLT系统的不同配置下模拟了任意大小的缺陷。对DNN进行了FEM仿真结果的训练，并为看不见数据的磁场分布提供了准确的预测。这项研究为以大数据驱动的方法设计优化的MFLT系统铺平了道路。