基于脉冲涡流的铁磁性材料屈服强度检测方法

摘要/Abstract

摘要： 钢铁工业中的铁磁性材料屈服强度的检测依赖拉伸检测，增加了检测成本，为此提出了一种多特征融合的铁磁性材料屈服强度脉冲涡流检测方法。提取脉冲涡流响应信号的时域特征、频域特征，然后建立各个信号特征与材料屈服强度的神经网络模型，最后用神经网络模型对材料的屈服强度进行估计。该方法是一种无损检测方法，检测误差不超过5%。

关键词: 铁磁性材料, 涡流检测, 屈服强度, 神经网络, 特征融合

Abstract: Yield strength detection of ferromagnetic materials in iron and steel industries relied on loss detection, which greatly increased the cost of testing. So, a multi-feature fusion method for yield strength pulsed eddy current testing of ferromagnetic materials was proposed herein. Firstly, the time domain characteristics and frequency domain characteristics of pulsed eddy current response signals were extracted. And then, a neural network model for establishing the yield strength of each signal characteristic and material was established. Finally, the neural network model was used to predict the yield strength of the materials. As a non-destructive testing method, the method may achieve prediction errors less than 5%.

Key words: ferromagnetic materials, eddy current testing, yield strength, neural network, feature fusion

中图分类号:

TG115.28

李开宇;高雯娟;王平;张艳艳;杭成. 基于脉冲涡流的铁磁性材料屈服强度检测方法[J]. 中国机械工程.

LI Kaiyu;GAO Wenjuan;WANG Ping;ZHANG Yanyan;HANG Cheng. Yield Strength Testing Method of Ferromagnetic Materials Based on Pulsed Eddy Current[J]. China Mechanical Engineering.

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