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DP590带钢屈服和抗拉强度的微磁定量预测

王贤贤, 何存富, 刘秀成

王贤贤, 何存富, 刘秀成. DP590带钢屈服和抗拉强度的微磁定量预测[J]. 无损检测, 2020, 42(11): 10-15. DOI: 10.11973/wsjc202011003
引用本文: 王贤贤, 何存富, 刘秀成. DP590带钢屈服和抗拉强度的微磁定量预测[J]. 无损检测, 2020, 42(11): 10-15. DOI: 10.11973/wsjc202011003
WANG Xianxian, HE Cunfu, LIU Xiucheng. Micromagnetic quantitative prediction of yield and tensile strength of DP590 steel strip[J]. Nondestructive Testing, 2020, 42(11): 10-15. DOI: 10.11973/wsjc202011003
Citation: WANG Xianxian, HE Cunfu, LIU Xiucheng. Micromagnetic quantitative prediction of yield and tensile strength of DP590 steel strip[J]. Nondestructive Testing, 2020, 42(11): 10-15. DOI: 10.11973/wsjc202011003

DP590带钢屈服和抗拉强度的微磁定量预测

基金项目: 

国家重点研发计划资助项目(2018YFF01012300)

详细信息
    作者简介:

    王贤贤(1993-),女,博士研究生,主要研究方向为力学性能的微磁无损检测

    通讯作者:

    刘秀成, E-mail:xiuchliu@bjut.edu.cn

  • 中图分类号: TH879;O348.9;TG115.28

Micromagnetic quantitative prediction of yield and tensile strength of DP590 steel strip

  • 摘要: 基于微磁检测方法对DP590带钢的屈服强度和抗拉强度进行无损定量预测。采用两级退火工艺对10个试件进行处理,得到强度不同的拉伸试样。利用多功能传感器同步检测试件表面的切向磁场强度和磁巴克豪森噪声信号,提取得到多项特征磁参量。通过变异系数分析方法,评价检测装置对磁参量的重复检测性能。对比分析了多元一次和多元二次模型对屈服强度和抗拉强度的定量预测精度,结果表明,多元二次方程对带钢强度指标的预测精度更高,其方程拟合确定系数大于0.97,预测平均误差小于3%。
    Abstract: The yield strength and the tensile strength of DP590 steel strip were nondestructively and quantitatively predicted based on the micromagnetic testing method. Ten specimens were prepared and two-stage annealing method was employed to change the yield and tensile strength of the specimens. A multi-functional sensor was used to simultaneously detect the tangential magnetic field and magnetic Barkhausen noise in the surface of the tested specimens. Multiple feature parameters representing the magnetic properties of the tested material were extracted from the obtained signals. The coefficient of variation analysis method was applied to evaluate the repeatability performance of the experimental set-up with respect to the measured magnetic parameters. Comparative studies were conducted to investigate the accuracy of multivariate linear and quadratic models in predicting the yield and tensile strength of the specimens. The results show that the multivariate quadratic model has better performances than the multivariate linear model. The coefficient of determination of the multivariate quadratic model is higher than 0.97. The prediction errors of the multivariate quadratic model to both the yield and tensile strength are less than 3%.
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出版历程
  • 收稿日期:  2020-04-19
  • 刊出日期:  2020-11-09

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