Magnetic Barkhausen noise (MBN) is measured in low carbon steels, and the relationship between microstructural properties and parameters extracted from MBN signal has been characterized. In the present study, the relationship between the number of turns of pick-up coils and MBN signals in both time-domain and frequency-domain is studied for the sensor coil optimization. With optimized pick-up coil, the characteristics of MBN are investigated for various mild steels with different grain sizes, carbon contents and hardness. To investigate the relationship between profiles of MBN signals and carbon contents of samples, the parameter has been extracted experimentally by fitting the original profiles with two Gaussian curves. The gap between two peaks (∆G) of fitted Gaussian curves shows a better linear relationship with carbon contents of samples in the experiment. The peak positions of MBN signal’s frequency response profiles have been observed decreasing with the increase of grain sizes. The fact is related to the length of two pinning sets reduces with the increase in the grain size. As a result, the frequency increases for the decrease of the time interval between two impulses. Due to the mechanical properties, such as hardness, are closely related to the grain size of the mild steels, the relationship can be described by Hall-Petch relation. The hardness can be predicted with the parameter MBN frequency peak position. To ensure the sensitivity of measurement, advanced multi-objective optimization algorithm Non-dominant sorting generic algorithm III (NSGA III) has been used to fulfill the optimization of the magnetic core of sensor. The relationship between the properties of samples and Magneto-Acoustic Emission (MAE) signals has also been investigated. Multi-features have been extracted and selected to fit a linear regression model to predict the hardness and grain size of the samples.
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