Sliding Mean Value Subtraction-Based DC Drift Correction of B-H Curve for 3D-Printed Magnetic Materials

Abstract

This paper presents an algorithm to remove the DC drift from the B-H curve of an additively manufactured soft ferromagnetic material. The removal of DC drift from the magnetization curve is crucial for the accurate estimation of iron losses. The algorithm is based on the sliding mean value subtraction from each cycle of calculated magnetic flux density (B) signal. The sliding mean values (SMVs) are calculated using the convolution theorem, where a DC kernel with a length equal to the size of one cycle is convolved with B to recover the drifting signal. The results are based on the toroid measurements made by selective laser melting (SLM)-based 3D printing mechanism. The measurements taken at different flux density values show the effectiveness of the method.

Magnetic Material Modelling of Electrical Machines

“…An algorithm to remove the DC drift from the B-H curve of an additively manufactured soft ferromagnetic material, based on the sliding mean value subtraction from each cycle of calculated magnetic flux density (B) signal, is presented by Bilal Asad et al in [8]. This is crucial for the accurate estimation of iron losses, and the measurements taken at different flux density values show the effectiveness of the proposed method, whose benefits are presented in the paper.…”