Search Results (60)

This study investigates the evolution of strain-induced martensite (SIM) and its effect on magnetic Barkhausen noise (MBN) in AISI 321 austenitic stainless steel subjected to uniaxial tensile testing. Using X-ray diffraction and the Barkhausen noise technique, the formation and distribution of SIM were analysed as functions of plastic strain and strain rate. The results show that MBN is primarily governed by plastic deformation and strain rate rather than residual stress. The martensite fraction increases from 10% at low strains to 42.5% at high strains; however, accelerated strain rates significantly reduce martensite formation to approximately 25%. The increase in martensite density enhances the magnetic exchange interactions among neighbouring islands, resulting in stronger and more numerous MBN pulses. The anisotropy of MBN is also influenced by the initial crystallographic texture of the austenite. These findings highlight the strong correlation between MBN and SIM evolution, establishing MBN as a sensitive, non-destructive tool for assessing martensitic transformation and optimising deformation parameters in austenitic steels. Full article

Magnetic Barkhausen noise (MBN) analysis is a non-destructive evaluation technique that offers significant advantages in assessing the magnetic properties of electrical steels. It is particularly useful for quality control in electrical steel production and for evaluating magnetic quality during core manufacturing and assembly. Despite its potential, MBN has not been widely used in electrical steel characterization. One obstacle is that the effects of silicon content in the electrical steel and the residual stress generated during its processing on MBN have not been thoroughly understood, limiting the practical application of the MBN technique in the electrical steel and electric motor industries. To address this knowledge gap, this paper investigates the MBN responses from four non-oriented electrical steel (NOES) sheets with varying silicon contents (0.88, 1.8, 2.8, and 3.2 wt%) but similar other elements. The measurements were performed both with and without applied tensile stress. It is observed that increasing the Si content increases the pinning density, which, together with the microstructure and texture, largely impacts the MBN response. In addition, the MBN energy increases with the applied stress, which can be attributed to the increase in the number of 180° domain walls (DWs) in the direction of stress. The rate of this MBN increase, however, differs among steels with different silicon concentrations. This difference is due to the combined effect of the DWs and pinning density. When the DW spacing becomes less than the jump distance between the pinning sites, no further increase in the MBN energy is observed with additional stress. The reported results provide a basis for the interpretation of MBN signals for varying wt% Si in NOES when residual stresses are present. Full article

In this study, the methods of tangential magnetic field (TMF), magnetic Barkhausen noise (MBN), and incremental permeability (IP) were employed for in the simultaneous, quantitative prediction of target properties (bidirectional residual stress, surface hardness, and case depth) in the 45 steel plate. The bidirectional magnetic signals and target properties were measured experimentally. The results of Pearson correlation analyses revealed that most parameters of the MBN and IP signals are strongly correlated with both residual stress and surface hardness under the influence of multiple target properties. The multiple linear regression (MLR) model demonstrated highly accurate quantitative prediction of residual stress and hardness in the y-direction. However, the simultaneous prediction of residual stress and case depth in the x-direction proved less effective than expected. To address this limitation, an inversion method was developed based on the regression model with the single parameter as the dependent variable and the target properties as the independent variable. By incorporating known magnetic parameters and target properties, the model effectively determined the unknown target properties. After applying the method, the coefficient of determination (R²) for x-direction residual stress increased from 0.89 to 0.96 and the absolute error (AE) of case depth decreased from 0.10 mm to 0.04 mm for case depths below 0.15. Full article

This study proposes a novel modelling approach that integrates mutual information (MI)-based parameter screening with random forest (RF) modelling to achieve an accurate quantitative prediction of surface hardness and impact energy in two martensitic stainless steels (1Cr13 and 2Cr13). Preliminary analyses indicated that the magnetic parameters derived from Barkhausen noise (MBN), and the incremental permeability (IP) measurements showed limited linear correlations with the target properties (surface hardness and impact energy). To address this challenge, an MI feature screening method has been developed to identify both the linear and non-linear parameter dependencies that are critical for predicting target mechanical properties. The selected features were then fed into an RF model, which outperformed traditional multiple linear regression in handling the complex, non-monotonic relationships between magnetic signatures and mechanical performance. A key advantage of the proposed MI-RF framework lies in its robustness to small sample sizes, where it achieved high prediction accuracy (e.g., R² > 0.97 for hardness, and R² > 0.86 for impact energy) using limited experimental data. By leveraging MI’s ability to capture multivariate dependencies and RF’s ensemble learning power, it effectively mitigates overfitting and improves generalisation. In addition to demonstrating a promising tool for the non-destructive evaluation of martensitic steels, this study also provides a transferable paradigm for the quantitative assessment of other mechanical properties by magnetic feature fusion. Full article

Despite their excellent mechanical properties, martensitic stainless steels present significant welding challenges due to their susceptibility to cracking and forming brittle microstructures during thermal cycles. While electron beam welding offers advantages through its high energy density and precise control over conventional welding methods, the induced residual stresses remain a critical concern. This study aims to determine surface residual stresses in electron beam welded AISI 410 martensitic stainless steel using a self-developed C-scan mode Magnetic Barkhausen Noise (MBN) measurement system. A novel calibration and measurement methodology was developed to establish a quantitative relationship between MBN signals and residual stress state. The residual stresses in the welded specimens were analyzed systematically using MBN and X-ray diffraction (XRD) measurements and microstructural characterization. The results revealed a strong correlation between MBN parameters and residual stress states, showing notable variations across the weld zones, i.e., approximately +350 MPa in the heat-affected zone and −50 MPa in the base metal. The experimental findings were also validated through finite element simulations. The correlation between experimental and numerical results confirms the reliability of the proposed MBN-based methodology and system. These findings provide valuable insights for industrial applications, offering a rapid and reliable non-destructive method for residual stress assessment in critical welded components. Full article

In this paper, magnetic permeability sensors able to perform structural health monitoring of magnetic steels, by means of determining residual strain and stress amplitude and gradient distribution, responsible for crack initiation, are presented. The good agreement between magnetic properties and residual strains and stresses is illustrated first, resulting in the determination of the magnetic stress calibration (MASC) curves and the Universal MASC curve. Having determined differential magnetic permeability as a key magnetic property, able to measure and monitor residual strain and stress distribution in magnetic steels, the paper is devoted to the presentation of the permeability instruments and sensors developed in our lab. The classic single sheet testers and the electromagnetic yokes, are compared with new, low-power-consumption permeability sensors using the Hall effect and the anisotropic magnetoresistive (AMR) effect, discussing their advantages and disadvantages in magnetic steel structural health monitoring. Full article

Aeromagnetic surveying technology detects minute variations in Earth’s magnetic field and is essential for geological studies, environmental monitoring, and resource exploration. Compared to conventional methods, residence time difference (RTD) fluxgate sensors deployed on unmanned aerial vehicles (UAVs) offer increased flexibility in complex terrains. However, measurement accuracy and reliability are adversely affected by environmental and sensor noise, including Barkhausen noise. Therefore, we proposed a novel denoising method that integrates Particle Swarm Optimization (PSO) with Wavelet Neural Networks, enhanced by a dynamic compression factor and an adaptive adjustment strategy. This approach leverages PSO to fine-tune the Wavelet Neural Network parameters in real time, significantly improving denoising performance and computational efficiency. Experimental results indicate that, compared to conventional wavelet transform methods, this approach reduces time difference fluctuation by 23.26%, enhances the signal-to-noise ratio (SNR) by 0.46%, and improves sensor precision and stability. This novel approach to processing RTD fluxgate sensor signals not only strengthens noise suppression and measurement accuracy but also holds significant potential for improving UAV-based geological surveying and environmental monitoring in challenging terrains. Full article

The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time–frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training. Full article

Magnetic Barkhausen noise (MBN) is one of the most effective methods for determining the easy axis of ferromagnetic materials and for evaluating texture and residual stress in a nondestructive manner. MBN signals from multiple angles and different magnetization sections can be used to characterize magnetic anisotropy caused by various magnetization mechanisms. This paper reviews the development and application of magnetic anisotropy detection technology, and the MBN anisotropy models that take into account domain wall motion and magnetic domain rotation are analyzed thoroughly. Subsequently, the MBN anisotropy detection devices and detection methods are discussed, and the application of magnetic anisotropy detection technology in stress measurement and texture evaluation is reviewed. From the perspective of improving detection accuracy, the influence of composite mechanisms on magnetic anisotropy is analyzed. Finally, the opportunities and challenges faced by current magnetic anisotropy detection technology are summarized. The relevant conclusions obtained in this paper can be used to guide the MBN evaluation of magnetic anisotropy in ferromagnetic materials. Full article

Among non-destructive testing methods, a group dedicated to the assessment of the state of residual stresses can be distinguished. The method of measuring residual stresses using the Barkhausen noise method has many advantages, as evidenced by the number of publications. The residual stresses in metal products are important for the further processing of such metal, such as laser cutting or bending. The results presented in this work are of an experimental nature, and the presented method of calibration of measuring heads shows how various research techniques can be used to correlate results. The research was carried out for structural steel due to the market share of this type of steel. The method can be used to measure the residual stresses in ferromagnetic metal products in order to assess their directions and quantify them. A prerequisite for the use of this measurement method is that the amplitude and geometry of the Barkhausen noise are adequately correlated to the specific values of the state of stress depending on the tested steel grade or other metals. In this study, a method for calibrating measuring sensors for the residual stress measurements is presented, as developed by the authors. The method involved conducting bending tests in both numerical modeling and experimental tests. During the bending tests, changes in the magnetic field (Barkhausen noise waveform) were recorded, taking into account the state of elastic stresses. Correlating the results of the numerical calculations and Barkhausen noise measurements made it possible to determine the quantitative values of the residual stresses in the steel sheets. Thanks to the method used, very accurate measurement is possible, and the obtained results are repeatable. Full article

The reactor pressure vessel (RPV) is a critical barrier in nuclear power plants, but its embrittlement during service poses a significant safety challenge. This study investigated the effects of Cu-enriched clusters on the mechanical and magnetic properties of Fe-0.9 wt.%Cu model alloys through thermal aging. Using Vickers hardness tests, Magnetic Barkhausen Noise (MBN) detection, and Atom Probe Tomography (APT), the study aimed to establish a quantitative correlation between MBN signals, Vickers hardness, and Cu-enriched clusters, facilitating the non-destructive testing of RPV embrittlement. Experimental results showed that the hardness and MBN parameters (RMS and V_pp values) changed significantly with aging time. The hardness increased rapidly in the early stage (under-aged), followed by a plateau and then a decreasing trend (over-aged). In contrast, MBN parameters decreased initially and then increased. APT analysis revealed that Cu-enriched clusters increase in size to 4.60 nm and coalesced during aging, with their number density peaking to 3.76 × 10²³ m⁻³ before declining. An inverse linear correlation was found between MBN signals and the combined factor N_d²R_g (product of the number density squared and the mean radius of Cu-enriched clusters). This correlation was consistent across both under-aged and over-aged states, suggesting that MBN signals can serve as applicable indicators for the non-destructive evaluation of RPV steel embrittlement. Full article

Both microstructure and stress affect the structure and kinematic properties of magnetic domains. In fact, microstructural and stress variations often coexist. However, the coupling of microstructure and stress on magnetic domains is seldom considered in the evaluation of microstructural characteristics. In this investigation, Magnetic incremental permeability (MIP) and magnetic Barkhausen noise (MBN) techniques are used to study the coupling effect of characteristic microstructure and stress on the reversible and irreversible motions of magnetic domains, and the quantitative relationship between microstructure and magnetic domain characteristics is established. Considering the coupling effect of microstructure and stress on magnetic domains, a patterned characterization method of microstructure and stress is innovatively proposed. Pattern recognition based on the Multi-layer Perceptron (MLP) model is realized for microstructure and stress with an accuracy rate higher than 97%. The results show that the pattern recognition accuracy of magnetic domain features and micro-magnetic features simultaneously as input parameters is higher than that of micro-magnetic features alone as input parameters. Full article

This paper presents the effect of the complex strain state resulting from the asymmetric rolling of TRB products on the changes and distribution of the stress state in the material. The evaluation of the stress state in the material was based on measurements of the magnetoelastic parameter (MP) using the Barkhausen magnetic noise method. The key characteristics of the material under study that enabled the use of changes in the MP parameter to assess the stress state were ferromagnetism and a lack of texture. The first of these enabled the detection of the magnetic signals produced when a magnetic field is applied to the material, causing magnetic domains to align and sudden changes in magnetization. On the other hand, the absence of texture in the material precluded the occurrence of magnetocrystalline anisotropy, which could disturb the results of measurements of the magnetoelastic parameter in the material. In order to determine these features in the material under study, its chemical composition was determined, and a phase analysis was carried out using the X-ray diffraction method. The results of these tests showed the possibility of determining the stress state of the material by means of changes in the values of the MP parameter. On this basis, it was shown that in the TRB strips studied, there is a complex state of stress, the values of which and the nature of the changes depending on the direction of the measurements carried out, as well as on the amount of rolling reduction in the studied area of the strip. Full article

The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction. Full article

The nondestructive determination of the neutron-irradiation-induced embrittlement of nuclear reactor pressure-vessel steel is a very important and recent problem. Within the scope of the so-called NOMAD project funded by the Euratom research and training program, novel nondestructive electromagnetic testing and evaluation (NDE) methods were applied to the inspection of irradiated reactor pressure-vessel steel. In this review, the most important results of this project are summarized. Different methods were used and compared with each other. The measurement results were compared with the destructively determined ductile-to-brittle transition temperature (DBTT) values. Three magnetic methods, 3MA (micromagnetic, multiparameter, microstructure and stress analysis), MAT (magnetic adaptive testing), and Barkhausen noise technique (MBN), were found to be the most promising techniques. The results of these methods were in good agreement with each other. A good correlation was found between the magnetic parameters and the DBTT values. The basic idea of the NOMAD project is to use a multi-method/multi-parameter approach and to focus on the synergies that allow us to recognize the side effects, therefore suppressing them at the same time. Different types of machine-learning (ML) algorithms were tested in a competitive manner, and their performances were evaluated. The important outcome of the ML technique is that not only one but several different ML techniques could reach the required precision and reliability, i.e., keeping the DBTT prediction error lower than a ±25 °C threshold, which was previously not possible for any of the NDE methods as single entities. A calibration/training procedure was carried out on the merged outcome of the testing methods with excellent results to predict the transition temperature, yield strength, and mechanical hardness for all investigated materials. Our results, achieved within the NOMAD project, can be useful for the future potential introduction of this (and, in general, any) nondestructive evolution method. Full article