Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method

Abstract

The correlation between multiple patterns of micromagnetic signatures and the mechanical properties (yield strength (Rp) and tensile strength (Rm) of high-strength steel (referred to as DP590 steel in Chinese standards) was investigated in this study. Feedforward neural network (FF-NN) models with carefully selected magnetic features as input nodes were established for quantitative prediction of yield strength and tensile strength. The accuracy FF-NN models highly relied on the quality of calibration specimens and the way of selecting magnetic features. The variations of the measured target properties were used to evaluate the quality of the calibration specimens. The specimens with similar yield strength (or tensile strength) were merged to share the same target properties in the model training process. The results demonstrated that merging proper target properties (label) could improve the performance of the models in quantitative prediction of yield strength and tensile strength in DP590 steels. In addition, the performances of FF-NN models combined with the algorithms of ReliefF and ReliefF + clustering were evaluated. The comparison results proved that the FF-NN models employing input nodes selection strategy of ReliefF + clustering realized the advantages of smaller dimensions of input nodes, less training time consumption at the cost of slight accuracy reduction.