Magnetic characterization of steel strips using transient field measurements: global sensitivity analysis and regression from a machine-learning perspective

In this contribution, the magnetic characterization of steel strips is studied using synthetic data of field-gradient transients, which have been produced via the finite integration technique. The material law is described and parameterized using the Jiles-Atherton model. The sensitivity of relevant magnetic indicators with respect to the material parameters is then analyzed using two global methods: Sobol' indices and delta-sensitivity indices. In order to accelerate the evaluation of these quantities, a fast metamodel is built using machine learning techniques from a simulated dataset. The solution of the inverse problem based on a tailored learning framework is tested for the different proposed identifiers, and their suitability for the magnetic characterization of the material in question is finally discussed.