Wang Xianxian, Cai Yanchao, Liu Xiucheng, He Cunfu
Department of information, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2024 Mar 23;24(7):2051. doi: 10.3390/s24072051.
The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction.
本研究探讨了两种模具钢（中国标准中的Cr12MoV钢和S136钢）的磁巴克豪森噪声（MBN）特征与表面硬度之间的相关性。以不同方法提取的MBN磁特征作为输入节点，建立了反向传播神经网络（BP-NN）模型，以实现表面硬度的定量预测。BP-NN模型的准确性在很大程度上取决于输入特征的质量。在磁特征提取过程中，简化参数设置并减少人工干预可显著提高磁特征的稳定性。在本研究中，我们提出了一种类似于磁巴克豪森噪声磁滞回线（MBNHL）的方法并进行特征提取。与传统的MBN特征提取方法相比，该方法简化了特征提取过程中的参数设置步骤，提高了特征的稳定性。最后，建立了表面硬度的BP-NN模型，并与传统的MBN特征提取方法进行比较。所提出的MBNHL方法以精度略有降低为代价，实现了参数设置简单、人工干预少以及提取参数稳定的优点。
