Prediction of the tensile force applied on surface-hardened steel rods based on a CDIF and PSO-optimized neural network

• Published in: Measurement science & technology Vol. 29; no. 11; pp. 115602 - 115622
• Main Authors: Zhu, Zhongyang, Sun, Guangmin, He, Cunfu, Liu, Anqi
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.11.2018
• Subjects: case depth insensitive feature; hysteresis loop; PSO-optimized neural network; unknown and uncontrollable factors; variation of case depth
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/aadebf

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%.