An intelligent method for prediction of surface-hardness in surface-hardened steel rod

Surface-hardened steels are widely used in many industrial components, such as cam and gears. Surface-hardness (SH) prediction plays an important role in assessing the quality of the surface-hardened steel. In this paper, an intelligent method for automatic SH prediction model establishment is proposed. The proposed intelligent approach involves three key steps. Firstly, hysteresis loops (HLs) are measured from the prepared surface-hardened steel rods with different SHs by a sensor. Secondly, a binary particle swarm optimization (BPSO) feature selection is applied to automatically select optimal feature subset from the measured HLs signal. Finally, a single-layer feedforward neural network (FNN) model is utilized to bridge the relationship between the selected features and SH. The experimental results show that the intelligent method can automatically establish SH prediction model. The optimal feature subset based single-layer FNN model can accurately predict SHs in surface-hardened steel rods with a prediction error of 0.298 %, indicating that the intelligent method can be integrated in an industrial robot for SH prediction in surface-hardened steel rod.