Investigating the Effects of Material Inhomogeneity on the Robustness of Magnetic Barkhausen Noise-Based Stress Characterization

The Magnetic Barkhausen Noise (MBN) measurement method is widely used to evaluate stress, microstructure, plasticity and other material states of ferromagnetic materials. However, stress assessment is generally affected by material inhomogeneity or anisotropy caused by microstructure properties such as grain size, leading to low signal-to-noise ratio and poor repeatability when characterizing stress using MBN signals. In this paper, a high spatial resolution MBN sensor based on a recording magnetic head is designed. A statistical method based on grain statistics is proposed to determine the minimum number of grains required for stable MBN signals. The method is experimentally validated on fine-grained Q235 carbon steel and 27QG110 oriented silicon steel with large grains. Different feature extraction methods, including RMS, amplitude histogram and pulse height distribution of MBN signals are applied and compared for robustness of material inhomogeneity. The results demonstrate that the average effect achieved by a sufficiently large measurement area reduces the impact of material heterogeneity on MBN signals, which is particularly relevant for large-grain materials. Additionally, the normalized threshold pulse count (ThPC) feature of amplitude histogram, defined as the number of upward threshold crossings, enhances robustness to material inhomogeneity and provides good repeatability in stress characterization across materials with different microstructurual scales. Therefore, this study provides practical guidance for quantitative stress evaluation using MBN measurement.