基于剩余物的铁磁材料表面裂纹检测研究

Jiarui Feng, Entao Yao, Ping Wang, Yuxia Shi
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摘要

剩磁检测是一种电磁无损检测技术。研究了一种基于剩余物的铁磁材料表面裂纹定量检测方法。利用有限元分析软件COMSOL Multiphysics建立剩磁检测模型,利用“移动网格”函数实现剩磁信号的仿真。铁磁材料磁化后,由于表面裂纹附近的磁感应线发生畸变而产生漏磁场。剩磁检测是利用泄漏磁场来检测裂纹。利用磁偶极子模型分析了泄漏磁场与裂纹深度和宽度的关系。通过对不同尺寸裂纹表面残余信号的测量,验证了裂纹尺寸与残余信号之间的关系。提取与裂纹尺寸相关的特征参数,建立特征参数与裂纹尺寸之间的回归模型。对于剩余物检测,宽度预测的最大误差为16.25%,深度预测的最大误差为18.48%。对于漏磁检测,宽度预测的最大误差为12.1%,深度预测的最大误差为12.32%。在相同的实验条件下,残余检测的裂纹宽度测量值的最大误差比MFL检测值大4.15%,深度测量值的最大误差比MFL检测值大6.16%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on surface crack detection of ferromagnetic materials based on remanence
Remanence detection is a technique of electromagnetic non-destructive testing (NDT). This paper studies a quantitative detection method for surface cracks of ferromagnetic materials based on remanence. The finite element analysis software COMSOL Multiphysics was used to establish the remanence detection model and the 'moving grid' function was used to realise the simulation of the remanence signal. The leakage magnetic field occurs due to the distortion of the magnetic induction lines near the surface cracks after ferromagnetic materials are magnetised. Remanence detection uses the leakage magnetic field to detect cracks. The relationship of the leakage magnetic field versus the crack depth and width was analysed using the magnetic dipole model. The relationship between the crack size and the remanence signal was verified by measuring the surface remanence signal of cracks of different sizes. The characteristic parameters related to the crack size were extracted and the regression model between the characteristic parameters and the crack size was established. For the remanence detection, the maximum error of width prediction was 16.25% and the maximum error of depth prediction was 18.48%. For the magnetic flux leakage (MFL) detection, the maximum error of width prediction was 12.1% and the maximum error of depth prediction was 12.32%. Under the same experimental conditions, the maximum error of crack width measurement of remanence detection was 4.15% larger than that of MFL detection and the maximum error of depth was 6.16% larger than that of MFL detection.
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