Quantitative Prediction of Surface Hardness in 12CrMoV Steel Plate Based on Magnetic Barkhausen Noise and Tangential Magnetic Field Measurements

Both magnetic Barkhausen noise (MBN) and tangential magnetic field (TMF) strength can be applied in the quantitative prediction of surface hardness of ferromagnetic specimens. The prediction accuracy depends on the selected model and the input parameters of the model. In this study, the relationship between the surface hardness of 12CrMoV steel plate and the measured MBN and TMF signals is investigated with multivariable linear regression (MLR) model and BP neural network technique. A comparative study between the MLR and BP model is conducted. The external validation results show that the BP model utilizing four MBN features as the input nodes has a smaller average prediction error (3.7%) than that of the MLR model (13.2%). Features extracted from the MBN and TMF signals are combined together as the input parameters of the BP model in order to achieve high accuracy. After adding two more TMF features into the input nodes of the BP network, the external validation results suggest that the average prediction error is decreased from 3.7 to 3.5%.