The features traditionally extracted from hysteresis loops are highly sensitive to both the variation of case depth and uncontrollable factors in repeated testing cycles, thus increasing the difficulty in predicting the tensile force applied on surface-hardened steel rods with different case depths. In this study, in order to eliminate the influence of such high sensitivity, a case depth-insensitive feature (CDIF) was proposed to characterize the tensile force, and a particle swarm optimization (PSO)-optimized neural network was used to establish the correlation between the CDIF and tensile force in order to predict the tensile force applied on steel rods with different case depths. Five classical features (including remanent magnetic induction intensities, coercive force, hysteresis loss, maximum magnetic induction, and distortion factor) and the CDIF were successfully used to characterize the tensile force. Then, the linear regression model and PSO-optimized neural network model were used in turn to establish the relationship between each feature and tensile force to predict the tensile force applied on steel rods with different case depths. The CDIF was insensitive to the variation of case depth and linearly correlated with the tensile force. Even though the CDIF is affected by the unknown and uncontrollable factors in repeated testing cycles, the PSO-optimized neural network model based on it can be used to accurately predict the tensile force applied on steel rods with different case depths with a prediction error of 0.67%.

No abstract

GH4169 is primarily strengthened through precipitation, with heat treatment serving as a crucial method for regulating the precipitates of the alloy. However, the impact of aging temperature on the microstructure and properties of GH4169 has not been thoroughly studied, hindering effective regulation of its microstructure and properties. This study systematically investigated the effects of aging temperature on the evolution of precipitates and mechanical properties of GH4169 alloy using various techniques such as OM, SEM, XRD and TEM. The results indicate that raising the aging temperature leads to an increase in the sizes of both the γ″ and γ′ phases in the alloy, as well as promoting the precipitation of δ phase at grain boundaries. Notably, the increase in γ″ phase size enhances the strength of the alloy, while the presence of δ phase is detrimental to its strength but greatly enhances its elongation. The yield strength of the alloy aged at 750 ℃ exhibits the highest yield strength, with values of 1135 MPa and 1050 MPa at room temperature and elevated temperature, respectively. As the aging temperature increases, the Portevin-Le Châtelier (PLC) effect during elevated temperature tensile tests at 650 ℃ gradually weakens. The PLC effect disappears almost completely when the aging temperature reaches 780 ℃.