• CN：11-2187/TH
• ISSN：0577-6686

• 仪器科学与技术 •

### 基于磁巴克豪森噪声的S136钢表面硬度定量预测模型对比

1. 北京工业大学机械工程与应用电子技术学院 北京 100124
• 收稿日期:2018-11-09 修回日期:2019-05-19 发布日期:2020-01-07
• 通讯作者: 刘秀成(通信作者),男,1984年出生,教授。主要研究方向为力学性能的微磁无损检测方法、结构健康监测技术与仪器。E-mail:xiuchliu@bjut.edu.cn
• 作者简介:何存富,男,1958年出生,教授,博士研究生导师。主要研究方向为无损检测新技术。E-mail:hecunfu@bjut.edu.cn;蔡燕超,男,1993年出生,硕士研究生。主要研究方向为微磁无损检测技术。E-mail:chaoyanc@sina.com
• 基金资助:
国家自然科学基金资助项目（11527801，11402008）。

### Comparative of Models for Quantitative Prediction of Surface Hardness in S136 Steel Based on Magnetic Barkhausen Noise

HE Cunfu, CAI Yanchao, LIU Xiucheng, WU Bin

1. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124
• Received:2018-11-09 Revised:2019-05-19 Published:2020-01-07

Abstract: The surface hardness is important index for the manufacturing quality evaluation of ferromagnetic materials and the non-destructively quantitative testing of surface hardness is one of the hot topics in the field. Magnetic Barkhausen noise (MBN) measurement technique is applied for quantitative evaluation of surface hardness in S136 steel. A self-developed device is used to conduct MBN measurements in sixty specimens of different surface hardness. The coefficient of variation of the repeated measured magnetic parameters is analyzed to show that the device has good repeated accuracy for MBN measurements. To achieve quantitative characterization of the surface hardness using magnetic parameters, the dependency of the surface hardness on six parameters extracted from the measured MBN and tangential magnetic field signals is individually analyzed. More importantly, comparative study is performed among the simple linear regression model, multiple linear regression model and BP neural network model to evaluate their accuracy in surface hardness prediction. The results show that the BP neural network model employing six magnetic parameters as inputs has highest accuracy in surface hardness prediction. A total of 300 cases are investigated and it is found that the average prediction error is only around 2.14% and the maximum error is about 11.74%, and the prediction errors out of 274 of 300 cases are less than 5.00%. A reference is provided for realizing the nondestructive quantitative detection of steel plate surface hardness based on magnetic Buckhausen noise method.