基于能量重构的MBN材料硬度检测算法研究

Research on algorithms for material hardness detection basedon energy reconstruction MBN

传统硬度测试方法会损伤材料,采用一种无损检测方法即磁巴克豪森噪声(MBN)法测试不同硬度的低合金钢试件。根据MBN信号分布特点,采用基于小波处理的全局阈值部分重构算法,对信号降噪后,提取能量特征,得到能量归一化分布图。实验表明:该算法优于传统阈值去噪法;MBN信号与硬度有着很高的相关性;不同子频带的能量归一化比例反映了材料硬度变化的特征;不同硬度材料的近矫顽力场强分布呈现一定规律。

Traditional hardness testing method may damage the material.Magnetic Barkhausen noise(MBN),a non-destructive testing method,can be used to detect low alloy steel specimens with different hardness.According to the characteristics of noise signal distribution,a global threshold partial reconstruction algorithm based on wavelet processing is used to de-noise the signal and extract energy characteristics and obtain energy normalized distribution graph.The experimental results show that this algorithm is superior to the traditional threshold denoising method;the MBN signal has a high correlation with hardness;the energy normalization ratio of different sub-bands reflects the characteristics of material hardness change;the near coercive force field distribution of different hardness materials presents a certain rule.