Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

Multiple micro-magnetic non-destructive testing (NDT) technologies are suitable candidates for predicting the mechanical properties of cold-rolled steel strips. In this work, based on magnetic domain dynamics behavior and magnetization theory, the correlation between electromagnetic characteristics extracted by multiple micro-magnetic NDT technologies and the influence factors was investigated. It was found that temperature and tension can subsequently affect the electromagnetic parameters by altering the domain structure and domain walls' motion properties. Pearson's correlation coefficients were employed to reflect the dependence of micromagnetic characteristics on influencing factors. The lift-off was determined as the largest influence factor among influence factors. A pseudo-static detection was reached by polynomial fitting, which could eliminate the influence of lift-off on the detection results. The number of training models was optimized, and the detection accuracy was improved via the improved Generalized Regression Neural Network (GRNN) model, based on the Gaussian Mixture Clustering (GMC) algorithm.

Automated summary

This study investigates the correlation between electromagnetic characteristics obtained by multiple micro-magnetic NDT technologies and influencing factors based on magnetic domain dynamics behavior and magnetization theory. The study finds that temperature and tension can influence electromagnetic parameters by altering the domain structure and domain walls' motion properties. The study also proposes a polynomial fitting method to eliminate the influence of lift-off on detection results and improves the detection accuracy using an improved GRNN model based on the GMC algorithm.