基于微磁检测技术的钢杆淬硬层深度定量预测

为将微磁检测原理应用于钢杆淬硬层深度的定量检测,设计了可同步检测切向磁场强度时变信号、磁滞回线和巴克豪森噪声信号的多功能传感器,从3类微磁信号中提取出共8项特征参数用于淬硬层深度表征.基于逐步回归方法,筛选出显著水平小于0.07的4项微磁特征参数(即矫顽力Hc、切向磁场强度时变信号的3次谐波幅值A3和谐波畸变因子K、巴克豪森噪声信号蝶形曲线的参数H_(cm)),建立了四元线性回归预测模型.该模型对淬硬层深度的预测平均误差仅为3.87%.上述基于多功能传感器的微磁检测方法,可以推广应用于铁磁性杆类构件表面硬化层深度的定量检测.

Quantitative Prediction of Induction Hardened Case Depth in Steel Rods Based on Micro-magnetic Testing Technique

To apply the micro-magnetic testing method to the induction hardened case depth evaluation, an integrated sensor was designed to synchronously detect the tangential magnetic field strength signal, the hysteresis curves and the Barkhausen noise. A total of eight features or parameters were extracted from the above mentioned three kinds of signals for induction hardened case depth characterization in steel rods. Stepwise regression method was employed to help select the parameters with a significant level lower than 0.07. Finally, the coercive force Hc, the amplitude of the three harmonics (A3), the distortion factor (K) of tangential magnetic field strength signal, and the parameter Hcmof the butterfly-like Barkhausen noise envelop were selected as the input of a multivariable linear regression model. The established linear regression model had an averaged prediction error of 3.87%. The developed experimental set-up together with the signal analysis method is expected to be applied to quantitative measurement of the induction hardened case depth in rod-like ferromagnetic components.