Magnetic Barkhausen Noise Signal Research Articles

Evaluation of the wear mechanism of ferromagnetic materials based on magnetic barkhausen noise

ABSTRACT As a typical form of damage, wear greatly affects the service properties of products, and non-destructive evaluation of the degree of wear is urgently needed. In this paper, the wear degree of AISI 1045 steel was evaluated by magnetic Barkhausen noise (MBN) testing. The root mean square (RMS) and full width at half maximum (FWHM) of MBN were extracted to establish the quantitative relationships between the tribological indicators and the stress state. The results indicate that the RMS decreases linearly and the FWHM increases quadratically with increasing tribological indicators, which can distinguish among different wear stages, including abrasive, adhesive and fatigue wear. However, the RMS shows a complicated nonlinear relation with the residual stress during the wear process. In addition, the MBN energy parameter is also calculated to reveal the features of the hysteresis loop at the wear scar. Both the coercivity H c and the maximum magnetic permeability μ max are linearly related to the tribological indicators and quadratically related to the stress, which reflects the effect of tribo-magnetisation on the MBN signals. The variation mechanism of MBN under different wear stages is attributed to the combined effect of plastic deformation and the stress state by microstructure observations.

An improved hidden Markov model with magnetic Barkhausen noise and optimized Gaussian mixture feature for fatigue prediction

Evaluating fatigue states of metallic materials is essential for predicting their failures and ensuring structural safety. Magnetic Barkhausen noise (MBN) analysis, a non-destructive testing method, provides efficient and reliable methods for identifying and categorising material parameters such as hardness and residual stresses. To establish a quantitative relationship between MBN signals and fatigue states, an improved hidden Markov model (HMM) is proposed based on optimised Gaussian mixture features (GMFs) and the Kullback–Leibler (KL) divergence measure for fatigue prediction. The MBN-GMFs replicate the probability characteristics of MBN signals and track the fatigue degradation trend throughout the fatigue life; thus, they are superior to some widely used statistical features. A Gaussian component optimisation algorithm is proposed to automatically adjust the appropriate number of components in the Gaussian mixture model and enhance the representation of MBN-GMFs. Then, the KL divergence is introduced to quantify the similarity and accurately classify the degree of MBN-GMF migration. The HMM is constructed to obtain the probability transfer relationship between the observations and states and obtain accurate fatigue predictions. Experiments on two 20R metallic materials at three excitation frequencies are conducted to collect the MBN signals. The experimental results and comparisons indicate that the proposed HMM can accurately predict fatigue states and provide a practical and robust analysis tool for MBN-based fatigue predictions.

Barkhausen Noise as A Magnetic Nondestructive Testing Technique

Magnetic Barkhausen Noise (MBN) is a magnetic-based non-destructive electromagnetic testing method. Due to the electromagnetic working principle of MBN, it can be used for ferromagnetic materials, which consist of small magnetic fields discredited by domain walls and oriented in various directions. In an external magnetic field application, the fields turn to the magnetic direction, and the domain walls move and cause magnetic flux jumps. The jumps are named Barkhausen Noise (BN). The domain wall movements are sometimes pushed down by microstructure, composition, and defects. As the magnetic domain walls break away from the pinning sites produce MBN signal. MBN can be used for different material properties such as microstructure, composition, residual stress, and hardness. The paper's purpose is to analyze MBN as an improved NDT, clarify the relationship between material properties and MBN profile, and introduce MBN's applications and test equipment of MBN.

A three-dimensional numerical model for computing the actual voltage induced in a Barkhausen surface coil

This investigation presents a novel three-dimensional simulation model designed for the analysis of Magnetic Barkhausen Noise (MBN). The model’s formulation is directly deduced from Maxwell’s equations, establishing a Boundary Value Problem (BVP) with boundary conditions tailored to a specific configuration involving a lengthy steel rod encased within a solenoid, subjected to sinusoidal current excitation, all contained within an air enclosure. This chosen configuration holds physical equivalence to the majority of setups commonly utilized in Barkhausen experiments. Utilizing the proposed three-dimensional model, it became feasible, for the first time, to computationally assess the voltage induced in the coil by the flux emanating from all spatial directions. The findings indicate that the MBN signal is primarily generated by the flux rate aligned with the direction of the applied field. Nevertheless, simulations confirm additional contributions to the signal originating from magnetic flux in directions significantly deviating from the applied field direction. It is demonstrated that these contributions from lateral flux significantly influence the angular dependence of the Barkhausen signal energy, showcasing a notable alignment between the proposed model and experimental results. The results underscore the capability of the proposed model to facilitate the simulation of Barkhausen Noise.

Comprehensive surface integrity evaluation during early stages of gear contact fatigue

The study aims to evaluate the microstructural sensitive aspects of contact fatigue crack initiation and its evolution during the gear lifetime. Tests were performed to evaluate different stages in the evolution curve of gear contact fatigue. The magnetic Barkhausen noise (MBN) technique was used to characterize variations in the magnetic response in gears during the incipient gear contact fatigue mechanisms initiation. For a complete comprehension of the surface degradation, residual stresses, microstructure, and microhardness were explored. A substantial increase in the MBN signal is identified before the fatigue failure occurs, indicating the occurrence of microstructural alterations that change the magnetic properties. The early stages of contact fatigue are accompanied by a surface softening in the near-surface region, up to approximately 40 µm depth. These phenomena were also followed by a less compressive residual stress region at 20 µm depth. A lower influence of microstrains on the diffractogram can be observed by the full width at half maximum parameter (FWHM), indicating a higher amount of dislocation annihilation during the appearance of the initial stages of the contact fatigue mechanism. The study concludes by proposing a comprehensive approach to understand the mechanisms behind the early stages of gear contact fatigue and how the MBN signal can be used to detect fatigue damage.

Influence of the Transverse Stress Component of Ferromagnetic Materials on MBN Signals Under Bidirectional Stress Conditions

This paper studies the effect of the transverse stress component of magnetically isotropic ferromagnetic materials on the magnetic Barkhausen noise (MBN) testing signals under planar bidirectional stress states. Based on the logistic distribution function, a model that can be used to describe the effect of the planar bidirectional stress level on MBN characteristic signals is proposed; the formulated model works within the elastic limit of the magnetically isotropic ferromagnetic materials. Then, the characteristics of the proposed regression model are analyzed by computer simulation. Finally, to validate the proposed regression model, the bidirectional stress experimental data extracted from the published paper is used. The results show that there is a high linear correlation between the actual data and the predicted data of the formulated model. Therefore, it can be proved that the proposed planar bidirectional stress regression model based on the logistic distribution function is feasible, applicable, and valuable.

Feature extraction and evaluation for quantitative prediction of hardness in bearing steel based on magnetic Barkhausen noise

In this paper, magnetic Barkhausen noise (MBN) is employed to quantitatively predict the hardness of GCr15 bearing steel. Firstly, to thoroughly investigate the relationship between MBN signal features and material hardness, a comprehensive study is conducted on multi-feature extraction methods for MBN signals based on time domain, frequency domain and time–frequency domain. Secondly, a novel feature evaluation algorithm is proposed that considers the correlation, stability and discriminability (CSD) of MBN features. This algorithm selects MBN features that are relevant to material hardness, remain stable under the same hardness level, and can distinguish between different hardness levels. Finally, linear regression models and multilayer perceptron models are established for the relationship between MBN features and material hardness. The models built using the features selected by the CSD feature evaluation algorithm demonstrate superior accuracy, with the root mean square error of 1.04 HRC for predicting unknown hardness values.

Tension-induced magnetic Barkhausen noise morphology transition caused by pre-introduced strain

Magnetic Barkhausen noise (MBN) technology is widely used to reveal the microscopic mechanism of magnetomechanical phenomena, but the morphological transition induced by tension has not yet been confirmed by experimental measurements. Here, we report a novel effect on the morphology transition of MBN signals due to tensile stress with pre-introduced strain. We present a power-law relation between the critical stress for the morphology transition and the degree of pre-introduced strain, highlighting the strong correlation between plastic deformation and MBN response. Through comprehensive microscopic simulations, we reveal that dislocations localized in regions with distinct densities trigger different MBN avalanches, thereby leading to a bimodal morphology of the MBN signal. Upon reloading, an effective field introduced by stress promotes the realignment of the magnetic domains, thereby resulting in the formation of a unimodal morphology of the MBN signal. Our unprecedented findings provide valuable insights into the correlation between deformation and MBN signal response, thereby opening a new avenue for designing and optimizing ferromagnetic materials with tailored magnetic behavior.

Stress measurement based on magnetic Barkhausen noise for thin films

Non-destructive testing methods based on magnetic Barkhausen noise (MBN) are expected to be applied to thin films. Recently, MBN analysis using machine learning has been performed on bulk materials and used as an in-situ stress and material evaluation method. However, for thin films, it is difficult to measure due to the weak MBN signal intensity, and few examples of its use as a stress and material evaluation method have been reported. This study acquired and analyzed MBN of FeCo polycrystalline thin films under bending stress. After pre-processing the acquired MBNs, two representative machine learning algorithms were used to learn the relationship between MBN and stress. By quantitatively comparing the prediction accuracy of each machine learning method, the characteristics of the MBN-based stress evaluation method were discussed from two perspectives: the need for domain knowledge and its applicability to unknown data. This study provides insight into machine learning-assisted MBN analysis as a stress evaluation method for thin films. The extension of MBN-based stress evaluation methods to thin films could be applied to the non-destructive stress evaluation of micro- and nanostructures, where stress state is critical, and could improve process development efficiency.

On the Combined Effect of Elastic and Plastic Strain on Magnetic Barkhausen Noise Signals

On the Combined Effect of Elastic and Plastic Strain on Magnetic Barkhausen Noise Signals

Study on non-destructive testing of rail rolling contact fatigue crack based on magnetic barkhausen noise

Rail rolling contact fatigue (RCF) crack and other rail damage have great impact on the normal operation of the railway system. The detection and evaluation of RCF crack can effectively ensure the running safety of the train. Compared with the traditional non-destructive testing methods, the characteristics of Magnetic Barkhausen Noise (MBN) detection technology such as easier implementation, higher efficiency and faster inspection process etc., make it an ideal choice for non-destructive detection technology of rail RCF crack. Therefore, this paper studied the influence of plastic deformation layer and crack of rail roller on MBN signal. The detection equipment based on MBN signal was built and used to detect and characterize the rail PDL and cracks. The wheel-rail rolling-sliding tests were conducted on a twin-disc tribometer to prefabricate the rail RCF crack and PDL. Experimental results proved that MBN signal decreased with the increase in plastic deformation layer (PDL) thickness. Crack initiation would release the residual compressive stress (RCS) of material surface, resulting in the abnormal increase in MBN signal under the general trend of gradually weakening. The influence mechanism of cracks on MBN signals was analyzed and discussed.

Barkhausen noise hysteresis cycle: Theoretical and experimental understanding

Magnetic Barkhausen Noise (MBN) reflects magnetic domains’ motions during a ferromagnetic part’s magnetization process. For industrial specimens, the raw MBN signal is stochastic and not reproducible, leading to complex analyses. This issue is solved using time-average variables like the Magnetic Barkhausen Noise energy (MBNenergy).Plotting MBNenergy as a function of the magnetic excitation gives rise to a hysteresis cycle. Recent studies have highlighted some exciting properties from this cycle, such as a way to observe the magnetic loss contribution associated with the domain wall motions. Still, questions remain, including in the basic description of MBNenergy.This paper describes a theoretical development to understand MBNenergy further. We demonstrate that in standard characterization conditions, the magnetization variations associated with the domain wall motions are proportional to the square of the envelope of the MBN signal instead of its absolute value.Then, this theoretical conclusion is confirmed experimentally. Finally, the absence of privileged orientation and direction in the MBN effect is verified in the case of unidirectional magnetization.

TRIP effect detection of medium manganese steel based on the MBN signal

In recent years, m-Mn (medium manganese) steel has gained increasing attention as a promising material for improving the crashworthiness and lightweight of vehicles, owing to its enhanced strength and ductility characteristics. However, the Transformation Induced Plasticity (TRIP) effect makes it challenging to accurately predict the microstructure and mechanical properties of m-Mn steel parts after forming. To overcome this issue, the Magnetic Barkhausen noise (MBN) detection technique is being employed to investigate the mechanism of TRIP effect on m-Mn steel, which could pave the way for the development of a non-destructive testing method for assessing the microstructure and mechanical properties of m-Mn steel parts. By analyzing the variation of MBN peak values of m-Mn steel and dual-phase steel DP1180 during the tensile process, it was found that the peak value of MBN signals increases continuously after the m-Mn steel is yielded due to the TRIP effect. Furthermore, a new strain-transformation model based on the O-C model was proposed to calculate the martensite content of m-Mn steel at all stages of elasticity and plasticity. By combining the detection data of MBN signals during the tensile process of m-Mn steel with the proposed strain-phase transformation model, a non-destructive testing method for the martensite content of m-Mn steel was established and compared with the traditional XRD test method. The results obtained from the testing of the martensite content of the corner of m-Mn steel U-shaped parts after stamping using the MBN testing method (6.56%, 9.94%) and the XRD testing method (7.42%, 9.26%) showed that the error between the two methods is less than 12%.

Grinding Burn Detection via Magnetic Barkhausen Noise Analysis Independently of Induction Hardened Depth

The electromagnetic technique based on magnetic Barkhausen noise (MBN) can be used to control the quality of ball screw shafts non-destructively, although identifying any slight grinding burns independently of induction-hardened depth remains a challenge. The capacity to detect slight grinding burns was studied using a set of ball screw shafts manufactured by means of different induction hardening treatments and different grinding conditions (some of them under abnormal conditions for the purpose of generating grinding burns), and MBN measurements were taken in the whole group of ball screw shafts. Additionally, some of them were tested using two different MBN systems in order to better understand the effect of the slight grinding burns, while Vickers microhardness and nanohardness measurements were taken in selected samples. To detect the grinding burns (both slight anddata intense) with varying depths of the hardened layer, a multiparametric analysis of the MBN signal is proposed using the main parameters of the MBN two-peak envelope. At first, the samples are classified into groups depending on their hardened layer depth, estimated using the intensity of the magnetic field measured on the first peak (H1) parameter, and the threshold functions of two parameters (the minimum amplitude between the peaks of the MBN envelope (MIN) and the amplitude of the second peak (P2)) are then determined to detect the slight grinding burns for the different groups.

Multifunctional Induction Coil Sensor for Evaluation of Carbon Content in Carbon Steel

Carbon steel has proven to be an important structural and functional material that plays an irreplaceable role in the worldwide economy. The influence of carbon on the mechanical and magnetic properties of the steel is well understood. Thus, the precise knowledge of the amount of carbon content in steel is crucial. Magnetic Barkhausen noise (MBN), magnetic hysteresis loop (MHL), and impedance measurements are reliable tools to assess carbon content. In this work, a multifunctional induction coil sensor used for MBN, MHL, and impedance measurements is designed and optimized. A multifunctional measurement system using the optimized induction coil is employed to measure MBN, MHL, and impedance signals. The parabolic dependence of the maximum value of MBN envelope on carbon content in steel is theoretically analyzed and experimentally verified. Coercive field and remanence from MHL measurements as well as the maximum impedance value are found to be proportional to carbon content and their dependence is explained with analytical simulations.

Martensite morphology and volume fraction evaluation of dual-phase X70 steel using magnetic Barkhausen noise technique

• The magnetic Barkhausen noise method is highly sensitive to changes in microstructure morphologies and martensite volume fraction. • The IQ treatment formed fine and fibrous martensite morphology uniformly distributed in the ferrite matrix. • The DQ treatment revealed hard martensite distributed in a ductile ferrite matrix. • The SQ treatment showed a banded morphology with a non-uniform distribution of the phases. • There are direct correlation between magnetic hardness (Hc) and mechanical hardness (HV) for each heat treatment. This paper aims to evaluate the martensite morphologies and volume fractions of dual-phase (DP) X70 microstructure (ferrite + martensite) using the Magnetic Barkhausen Noise (MBN) non-destructive technique. In order to develop different morphologies in X70 steel, three different heat treatments (intermediate quenching (IQ), direct quenching (DQ), and step quenching (SQ)) are done for half-hour. The inter-critical annealing temperature (IAT) ranges applied to the samples are from 740 °C to 820 °C. Measurements of MBN were carried out using the MikroMach instrument. Hardness measurements and microstructure examinations were conducted using a durometer and optical microscopy. Experimental results show that IQ treatment formed fine and fibrous martensite morphology uniformly distributed in the ferrite matrix. DQ treatment revealed hard martensite distributed in a ductile ferrite matrix. SQ treatment showed a banded morphology with a non-uniform distribution of the phases. Martensite Volume Fraction (MVF) and hardness get larger with IAT increases. These treatments permit obtaining the highest hardness in DQ treatment, and the lowest hardness has been identified in SQ treatment. Indeed, the MBN method has been proved to be highly sensitive to changes in microstructure morphologies and MVF. The highest MBN signal amplitude (MBNSA) for the SQ treatment has been identified at the lowest magnetic excitation field; in contrast, the lowest MBNSA for DQ treatment was located at the highest magnetic excitation field. The maximum MBNSA decreases whenever IAT increases. Finally, the direct correlation between magnetic hardness (Hc) and mechanical hardness (HV) for each heat treatment is established in this paper.

Magnetic Barkhausen Noise Measurements on Rocks

Context: The magnetic properties of rocks may reflect the modal abundance, composition, and microstructure of the magnetic grains contained within them, usually in a very small proportion. Magnetic Barkhausen Noise (MBN) is a non-destructive technique applied to magnetic materials, and it is very sensitive to microstructure and residual stresses. In this work, measurements of MBN in samples of rocks extracted from nature and containing varying proportions of magnetite were studied and analyzed. Method: Measurements were taken by magnetically exciting the cylindrical samples, using a magnetic yoke and measuring the induced field inside the rock as a consequence of the excitation. For the detection, a sensor coil placed on the excited surface on one of the flat faces of each cylinder was used. The signals obtained from the MBN were digitized, and a digital 5-200 kHz Butterworth filter was applied, calculating the root mean square (RMS) values. Results: The linear fit of the MBN RMS values with the increasing percentage of ferromagnetic minerals showed an increasing trend with a moderate correlation. A correlation between coercive force and the MBN RMS values was observed only for samples with abundant magnetite (> 25 vol%). Conclusions: An increasing variation of the RMS values of the MBN signals was observed in relation with coercive force for abundant magnetite samples. This may be related to the geological processes involved in magnetite genesis. Method: The measurements were made exciting magnetically the samples (cylindrical shapes), using a magnetic yoke and measuring the induced field inside the rock as a result of the excitation. For the detection, a sensor coil placed on the excited surface on one of the flat faces of each cylinder was used. The signals obtained from MBN were digitized. A digital 5-200 kHz Butterworth filter was applied and the RMS (Root Mean Square) values were calculated. Results: The linear fit of the MBN RMS values with the increasing percentage of ferromagnetic minerals showed an increasing trend with moderate correlation. A correlation between coercive force and MBN RMS was observed only for samples with abundant magnetite (> 25 vol%). Conclusions: A variation of the RMS values of the MBN signals was observed in relation with coercive force for massive magnetite samples. This can be related to geological processes involved in magnetite genesis. Keywords: Rocks, Magnetization, Barkhausen Magnetic Noise, Non-Destructive Test.

Stress Evaluation of SS275 Steel Using Magnetic Barkhausen Noise Characteristics

Magnetic Barkhausen noise (MBN) can be used to potentially detect fatigue, creep, stress, and hardness in ferromagnetic materials. Recently, non-destructive measurement equipment using MBN has been developed to measure the residual stress of railway rails in Korea. MBN has different characteristics depending on the type of ferromagnetic steel and residual stress. In this study, MBN signal characteristics based on the stress change of SS275 steel are experimentally analyzed using the developed MBN equipment. The results of this study can be used as basic data for estimating the residual stress of SS275 steel in the future.

Characterization and Analysis of Plastic Instability in an Ultrafine‐Grained Medium Mn TRIP Steel

Herein, the mechanical and magnetic behavior of an ultrafine‐grained (UFG) medium manganese (Mn) transformation‐induced plasticity (TRIP) steel is focused on in its plastic instability. The in situ methods of digital image correlation (DIC) and magnetic Barkhausen noise (MBN) are used to macroscopically characterize the propagation of the Lüders band (stretcher–strain marks) and the evolution of MBN activities during quasistatic tensile deformation. The evolution of microstructure during the plastic instability is investigated ex situ using X‐Ray diffraction (XRD) and transmission electron microscopy (TEM) for selected plastic strain states. It is showed in the results that the plastic instability of this steel is associated with an increase of hardness and enrichment of dislocation density, which can also amplify the MBN signal, while the derived coercivity behaves reversely on an overall trend due to work hardening. The different stress response of the medium Mn steel is closely related to the kinetic martensite microstructure, which in turn modifies the domain–structure response. Thus, the MBN can be used as a potential means for nondestructive evaluation (NDE) for the strengthening of the UFG medium Mn TRIP steel.

FilterNet: A deep convolutional neural network for measuring plastic deformation from raw Barkhausen noise waveform

In this paper, a novel convolutional neural network named FilterNet is proposed to learn feature representation directly from raw magnetic Barkhausen noise (MBN) for quantitatively evaluating plastic deformation. In the proposed model, three modified filter-type convolutional kernels are adopted to extract appropriate feature expressions from raw MBN. In the experimental part, a special experimental device, which could change the angle between the electromagnetic sensor and specimens, was designed to simulate the case that the performance of a trained model declined due to the change in the angle. MBN signals at 11 angles from left to right were measured. The performances of multiple linear regression (MLR), BP neural network (BP), and the FilterNet were analyzed with mean absolute error (MAE) and root-mean-square error (RMSE). MAE and RMSE of the FilterNet were smaller than the other two models at different angles and reveal the outperformance of the FilterNet.