基于电磁无损检测的车轮钢表面硬度检测方法

doi:10.13228/j.boyuan.issn10010777.20200015

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摘要介绍采用巴克豪森噪声、增量磁导率和切向磁场强度等多项电磁特征参量，通过挑选R2值较大的项作为输入样本，采用人工神经网络模型构建方法，对铁路车轮钢的表面硬度进行定量预测。研究实践表明，通过电磁参量测试，筛选出与硬度值线性拟合度相对较高的特征参量作为神经网络模型的输入，可以较好地预测车轮钢的表面硬度。

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	夏鹏
	魏志辉

关键词 ： , 电磁检测；表面硬度；人工神经网络；定量预测

Abstract：It introduces the quantitative prediction of the surface hardness of railway wheel steel by using Barkhausen noise, incremental permeability, tangential magnetic field strength and other electromagnetic characteristic parameters, selecting the item with larger R2value as the input sample, and using artificial neural network model construction method. The research practice shows that the characteristic parameters with relatively high linear fitting degree with the hardness value are selected as the input of the neural network model through the electromagnetic parameter test, which can better predict the surface hardness of wheel steel.

Key words： electromagnetic testing surface hardness artificial neural network quantitative prediction

收稿日期: 2021-01-25

引用本文:

夏鹏，魏志辉. 基于电磁无损检测的车轮钢表面硬度检测方法[J]. 物理测试, 2021, 39(2): 19-. XIA Peng, WEI Zhihui. Testing method for surface hardness of wheel steel based on electromagnetic nondestructive testing technology. PHYSICS EXAMINATION AND TESTING, 2021, 39(2): 19-.

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http://www.chinamet.cn/Jweb_wlcs/CN/10.13228/j.boyuan.issn10010777.20200015 或 http://www.chinamet.cn/Jweb_wlcs/CN/Y2021/V39/I2/19