Magnetic Memory Signal Research Articles

Research on influence of stress concentration on metal magnetic memory signal for stress evaluation

Abstract Based on the magneto-mechanical effect and stress concentration effect, this paper takes the failure of ferromagnetic components caused by stress concentration as the research background, artificially creates stress concentration by prefabricating U-shaped defects, and discusses the Hp(y) signal distribution law of prefabricated U-shaped defect samples and the difference in Hp(y) signal between specimens with different tip SCFs. Results show that the Hp(y) signal has an apparent mutation in the stress concentration area, and when the stress concentration factor was the same, the magnetic field intensity gradient K becomes higher as tensile load increasing. As stress concentration factor increasing, the K value of the stress concentration area also increases gradually. Based on theoretical model of Hp(y) signal, it can be known that the K value can be used to judge the sample stress concentration degree with the results in this study.

Research on defect image inversion method based on multiple lift-off values magnetic memory signals

Metal magnetic memory (MMM) technique can detect macroscopic defects and stress concentration zones of ferromagnets, and it can identify the approximate positions of these flaws, while a little further information about defects characteristics can be provided. To promote study in this area, defects were modeled as located magnetic dipoles whose strength and locations should be determined, and the technique of truncated generalized inverse and reduced space inversion were used to image the dipole strength. Through the simulation experiment, we found that the inversion image quality was influenced by the lift-off value. When the magnetic field data for inversion was measured with a small lift-off value, the inner dipoles contributions were easily buried and the inversion image can only shown the surface dipole well. In contrast, if the magnetic field data was measured with a greater lift-off value, it was possible to inverse the deep dipoles while the image was somewhat out of focus. To overcome this limitation, we composed the magnetic field data measured under different lift-off values together for inversion. We used the same model to test this approach and applied it on real crack defects magnetic field data. It demonstrates that the new inversion approach shows better accuracy than the single lift-off value and it was possible to infer the location and size of defect by the evaluation of preset dipoles.

Overview of welding defect detection utilising metal magnetic memory technology

Welded joints frequently endure the composite stress of steel components. The presence of defects within these welded joints can significantly jeopardise the safety and performance of the welded structure. Magnetic memory testing technology has garnered substantial attention due to its ability to evaluate welding defects. However, the conventional zero-point pole theory, which serves as the foundation for defect assessment in practical detection, may lead to defect location and omission errors. In response to this challenge, scholars have conducted extensive research to accurately pinpoint the location and identify the types of defect within welds. This paper systematically reviews the mechanisms of magnetic memory welding defect detection, the factors that influence it, signal characteristic parameters, noise reduction in magnetic memory signals and the application of machine learning for quantitative assessment. By summarising these research advancements, this paper aims to address the current issues and provide guidance for the precise quantitative evaluation of welding defects in the future using metal magnetic memory technology.

Stress corrosion phenomenon of casing in CO2 medium and magnetic memory detection

Due to being in an acidic CO2 environment and high-pressure underground, the stress corrosion phenomenon on the inner wall of the CCUS wellbore is more pronounced, with the possibility of wall thickness reduction and crack propagation, which affects the integrity and storage reliability of the wellbore. This article conducted indoor experiments on stress corrosion of oil casing, and characterized the stress corrosion through magnetic memory detection technology. Through experiments, the stress corrosion morphology of specimens under different concentrations, stresses, and defects was obtained. The microstructure of the stress corrosion site was observed using SEM electron microscopy, and the stress corrosion situation of the material in CO2 was observed, laying a foundation for magnetic memory detection; A stress corrosion detection method under CO2 environment was proposed, and the magnetic memory detection method was used to measure that the more severe the stress corrosion, the more obvious the magnetic signal mutation; The shape and morphology of different defects vary, but the main factors affecting magnetic memory signals are concentration and loading force. This article verifies the feasibility of magnetic memory in stress corrosion detection through experiments, and characterizes the stress corrosion situation using magnetic memory signals, which is of great significance for non-destructive testing of wellbore stress corrosion in CO2 storage.

Evaluation of residual load-bearing capacity for corroded steel strands via MMM technique

The quantitative evaluation of the residual load-carrying capacity of corroded cables is critical to the safe service of bridge structures. In this study, the locally corroded steel strands with different corrosion degrees were obtained through electrochemical experiments and the metal magnetic memory (MMM) signals were collected by the TSC-9 M-12 tester. The tensile failure tests of corroded steel strands were conducted by the WAW-1000 tensile testing machine. The distribution laws of the normal component Hz signals for steel strands in different corrosion cases were detailed and a three-dimensional (3D) magnetic dipole model of corroded steel strands was developed considering the end effects. The linear relationships between the magnetic characteristic quantity Hn and the corrosion cross-section loss rate As were revealed and verified by the theoretical and finite element simulation results. Based on the above relationships, a quantitative linear relationship between the residual load-carrying capacity F and the corrosion cross-section loss rate As of the corroded steel strand was proposed. The relative errors of the residual load-carrying capacity F calculated by the proposed quantitative method were overall within 20%. This study provides a basis for evaluating the residual load-carrying capacity of corroded cables using the MMM technique.

Magnetic memory testing towards fatigue crack propagation of Q235 steel for remanufacturing

A crack propagation trial was performed on a Q235 steel M(T) sample to investigate the modifications in magnetic memory signals throughout the crack propagation procedure of ferromagnetic substances. Various detection lines were employed to gather and scrutinize magnetic memory signals under two-stage fatigue loading. The interrelation between the gradient value Kmax, the peak-to-peak value SP−Py, the stress intensity factor Ka, the extension of the sample 𝜀, crack length a, and the cyclic N was established. The findings indicate that the Hp(y) curves present a varied peak at the crack tip and in the notch, whereas the Hp(x) curve is linear. The magnetic signals display similar changes during two-stage fatigue loading, but the post-break state deviates. The fatigue process’s SP−Py shows three phases of fluctuation, escalation, and decline. Throughout the period of fatigue crack growth, Kmax and 𝜀 rise exponentially, Ka ascends linearly, and a shows linear changes. The characteristics of magnetic memory signals can measure harm after varying service periods and offer robust foundations for remanufacturing.

Magnetic Dipole Model and Experimental Analysis of Magnetic Signal Detection Based on Magnetic Memory

By assuming that the magnetic charge is evenly distributed in the defect groove, the linear magnetic dipole is integrated in the depth direction of the defect, and the numerical calculation is carried out by using MATLAB to establish a two-dimensional magnetic dipole theoretical analysis model of double-correlated defects, which is suitable for analyzing the influence of stress on magnetic signal in the magnetic memory detection of rectangular and V-shaped combined macroscopic crack defects. To explore the influence factors and change rules of magnetic memory signal. In order to verify the correctness of the model analysis results, tensile tests were carried out on Q235 steel plate with double associated defects with different morphological characteristics, and the Hp(y) value of the normal component of the leakage magnetic field on the surface of the member was measured. The results show that the model based on magnetic dipole theory can explain some experimental phenomena and rules in magnetic memory detection.

Research on magnetic memory inspection signal characteristics of multi-parameter coupling pipeline welds

Magnetic memory inspection technology is an effective method for detecting early damage in ferromagnetic materials. However, the commonly used force-magnetic coupling models are insufficient for analyzing the distribution characteristics of magnetic signals in ferromagnetic material welds. This study first analyzes the influence of metal phase transformation and residual stress on the magnetic hysteresis characteristics of materials. Based on the principle of magnetic hysteresis characteristics, the magnetic charge density under the combined action of stress and phase transformation is determined, and a magnetic memory analysis model is established for multi-parameter coupling. The study quantitatively calculates the effects of phase transformation volume fraction, residual stress, the lift-off effect, and cracks on the magnetic signal features at welds. The research results show that the magnetic memory signals at welds exhibit extreme values in the tangential component and sinusoidal fluctuations with zero-crossings in the normal component. The peak-to-peak values of both the normal and tangential components of the magnetic signal increase with an increase in metal phase transformation and residual stress, while they decrease with the increase of the lift-off effect. When cracks appear in the weld, the normal component exhibits a double-peak and valley structure, with the inner side representing crack features and the outer side representing weld features. The tangential component shows a maximum value at the center of the crack. Based on the peak and valley values of the magnetic signal, a damage grade index F is determined. The damage index F can characterize the level of weld damage, decreasing approximately linearly with increasing stress.

Experimental Study on Fatigue Damage of Drilling Tool Materials Based on Magnetic Memory Detection

In drilling engineering, the cost of drilling tool fracture is enormous, and studying the fatigue failure process of drilling tools has practical significance. This paper uses metal magnetic memory detection technology to design and conduct fatigue damage tests on typical drilling tool material 42CrMo specimens under tensile, torsional, compressive, tensile, compressive, and torsional dynamic loading conditions. By analyzing the changes in the tangential component Hp(x) and gradient value K of the magnetic memory signal under different load conditions with the number of loading times, the process of fatigue failure of the specimens and the trend of changes in the magnetic memory signal in local stress concentration areas are explored. The characteristic parameters of fatigue damage based on magnetic memory detection were extracted and the critical point at which fatigue damage leads to crack initiation was inferred. This confirms that metal magnetic memory testing technology is an effective means of analyzing the fatigue damage process of drilling tools and provides a certain reference for formulating judgment standards for drilling tool maintenance on site.

Theoretical and experimental analysis of the correlation between magnetic memory signals and cumulative ductile damage of butt weld under low cycle fatigue

Welded structures experiencing superimposed cyclic loads with large plastic strain during a strong earthquake can easily lead to ductile or fatigue fracture. A newly developed self-magnetic flux leakage (SMFL) nondestructive detection technology named metal magnetic memory (MMM) is a valid approach to monitoring the development of fatigue damage in ferromagnetic materials. To investigate the correlation between MMM signals and cumulative ductile damage for butt weld under low cycle fatigue (LCF), combining strain-based Jiles-Atherton hysteresis model, magnetic charge model, and cumulative damage mechanics model, the theoretical relations between normal component HSF(z) of MMM signals and cumulative ductile damage D for single V groove butt weld were established in this paper. Relevant monotonic tensile tests and strain-controlled LCF tests were conducted, and the HSF(z) signals on the surface of welding specimens under different cyclic loads were measured. Comparing with experimental results verified the feasibility and accuracy of the theoretical relations. The results indicated that the peak-valley gradient Kp-v on the HSF(z) signals curve was decreased monotonously as the cumulative ductile damage D increased and the rate of decrease also diminished gradually. A possible exponential descending tendency of Kp-v with increasing D was demonstrated. Besides, the influences of weld width and weld reinforcement on the correlation between Kp-v and D were discussed theoretically in detail. This research provided a potential possibility for the assessment of the cumulative ductile damage at the welding joints under LCF based on MMM technology.

Non-destructive testing of steel wire ropes incorporating magnetic memory information

In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.

Propagation Characteristics of Magnetic Tomography Method Detection Signals of Oil and Gas Pipelines Based on Boundary Conditions.

The magnetic tomography method (MTM) is a non-contact external inspection method for detecting metal magnetic memory signals. It has great potential for application in long-distance oil pipeline and subsea pipeline inspection. However, the spatial distribution characteristics and propagation laws of magnetic signals are not yet clear, which makes the MTM passive detection. In this study, a three-dimensional mathematical model of the magnetic field distribution of the stress concentration zone outside the pipe was established based on the boundary conditions. For the two cases in which the stress concentration zone was located at the top and bottom of the inner wall of the pipe, the model was solved by finite element analysis. The variation law of the magnetic signal outside the pipe was analyzed, and experiments were designed to verify the model. The results show that the shape of the magnetic memory signal remained unchanged after passing through the pipe wall. As the magnetic permeability of the pipe medium is much larger than that of air, the magnetic memory signal is significantly attenuated after penetrating the pipe wall. As the detection height increases, the magnetic induction outside the tube decays exponentially. The results also prove that the magnetic tomography method can detect the stress concentration zone at any position of the pipeline, and the detection accuracy is higher when it is located at the top of the pipeline.

Magnetic-Acoustic Feature Extraction and Damage Fusion Evaluation of 45 Steel Specimens during Fatigue Process for Remanufacturing

To clarify the changes of the magnetic-acoustic features of 45 steel specimens during fatigue damage, an experimental platform was built to carry out magnetic memory and acoustic emission detection. The magnetic memory and acoustic emission signals of specimens in different damage states were collected, and the multi-scale entropy characteristics of magnetic memory signals, as well as the wavelet packet energy spectrum and singularity index characteristics of acoustic emission signals, were further extracted. A magnetic-acoustic feature fusion and damage assessment model was constructed by using Naive Bayes method. Results show that the average value of multi-scale entropy of normal magnetic field intensity Hp (y) increases gradually with the increase of fatigue cycles, and the average value of multi-scale entropy of magnetic field intensity gradient K gradually decreases. The cumulative ringing count and energy spectrum (proportion of frequency band 1) of acoustic emission signals decrease with the increase of fatigue cycles, while the amplitude singularity index gradually increases. The established model has high evaluation accuracy, and the conclusions of this paper can provide basic methods and data support for fatigue damage evaluation of remanufactured components.

Modification and experimental study of magnetization model of ferromagnetic materials based on J-A theory

In this paper, a new magnetization model is proposed, aiming at model correction based on the J-A model: a magnetization correction model is established in the elastic and plastic stages, respectively, and the relationship between the magnetization and the external magnetic field, stress and plastic strain is analyzed,and explain the asymmetric phenomena. The experimental results show that when the Q235 steel plate is in the elastic stage, the magnetic memory signal increases with the increase of the stress; when the Q235 steel plate is in the plastic stage, the magnetic memory signal first decreases and then increases with the increase of the stress,and finally tends to be stable; when the Q235 steel plate transitions from the elastic stage to the plastic stage, the magnetic memory signal produces a “cliff” decline, which corresponds to 0.5% plastic strain. This study shows that the novel magnetization model provides a new method for detecting ferromagnetic materials from the elastic stage to the plastic stage.

Modeling, simulation and experimental exploration of metal magnetic memory under weak magnetic excitation

This study proposes to use the improved Jiles–Atherton (J–A) magneto-mechanical effect model combined with simulation analysis to detect the damage of wire rope. By establishing the theoretical basis of the J–A model under weak magnetic excitation, the simulation analysis is carried out by using ANSYS software, and finally the correctness of the simulation and theoretical results is verified by experiments. The results show that the weak magnetic excitation magnetic field has a strengthening effect on the magnetic memory signal under the action of the force-magnetic coupling. By processing the extracted magnetic memory signal, it is verified that the weak magnetic excitation magnetic field alone has a certain inhibitory effect on the environmental interference of the geomagnetic field. Compared with the geomagnetic field environment, the weak magnetic excitation magnetic field can better identify the different defect types of simulated 60 steel damage and wire rope defects. Studies have shown that weak magnetic excitation plays an important role in the damage detection of ferromagnetic materials.

Experimental and theoretical research on relation between magnetic and mechanical property of structural I-steel column

As an ignored physical property, the metal magnetic memory signal has the potential for non-destructive testing in the future. To get the variation characteristic of magnetic signals under different loads, the normal components of surface spontaneous magnetic signals Hp(y) of the Q235 I-steel column were measured online. In this paper, the variation law of Hp(y) with stress was investigated in the position of the point of contraflexure and stress concentration. The physical mechanism between its magnetic property and mechanical property was established at the elastic and plastic stages. Experimental results show that Hp(y) reaches the extreme value in the position of stress concentration, where the magnetic field gradient K presents the zero-crossing phenomenon along with the peak–peak value phenomenon on the left and right sides. In the position of the point of contraflexure, when the Hp(y) curve reaches the extreme value, the corresponding equivalent uniaxial stress σeq is 54 MPa, which is in good agreement with the Jiles–Atherton (J–A) model. Characteristic parameters Kmax, ζ, and η can reflect the extent of damage of the specimen. In the position of stress concentration, Hp(y) values decrease sharply at the early plastic stage. The analysis shows that the distribution regularity of Hp(y) at the elastic stage is in good agreement with the J–A model. The modified J–A model is consistent with the experimental results at the plastic stage.

Forward model of metal magnetic memory testing based on equivalent surface magnetic charge theory

The magnetic dipole theory has been widely and successfully used to qualitatively analyze the testing signals of metal magnetic memory (MMM) testing. However, the magnetic charge density of the existing models is always an assumed value distributed uniformly or linearly along the defect section or in a stress concentration area, as a result the existing models are unsuitable for quantitatively analyzing the metal magnetic memory signal. In this work, a new forward model of MMM testing is established by considering the influence of uneven stress on magnetic charge density distribution, discretizing the specimen into numbers of micro elements firstly, assuming that the magnetic characteristic parameters of each element are evenly distributed and the magnetic charge density changes with stress in each element which can be determined by combining the modified magneto-mechanical model and the classical theory of magnetic charges. Compared with the experimental results of hole defect and crack defect specimen, the theoretical results calculated by the proposed model prove to be in good agreement with the testing results both qualitatively and quantitatively. Consequently, the proposed model is a new theoretical and quantitative model for analyzing the experimental change rule of metal magnetic memory testing. Then, the effects of stress concentration and macroscopic defects on the distribution of magnetic field are analyzed, showing that when there is only a stress concentration in the specimen, the horizontal component is negative valley, and the normal component changes from negative to positive valley peak in the stress concentration area; when there is a crack defect in the specimen, the distribution of magnetic field is just opposite to that when there is only a stress concentration. The distribution characteristics of the magnetic field can be used to judge the damage type in the specimen. Moreover, taking crack defect for example, the horizontal and normal component of magnetic field and their characteristic parameters changing with the size parameters of crack defect, such as width, length, depth and buried depth of crack defect, are analyzed in detail. The results show that the <inline-formula><tex-math id="M1">\begin{document}${W_{\Delta {H_x}}}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M1.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M1.png"/></alternatives></inline-formula> and <inline-formula><tex-math id="M2">\begin{document}${W_{\Delta {H_z}}}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M2.png"/></alternatives></inline-formula> increase lineally with the increase of the width of crack, <inline-formula><tex-math id="M3">\begin{document}$\Delta {H_x}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M3.png"/></alternatives></inline-formula> and <inline-formula><tex-math id="M4">\begin{document}$\Delta {H_z}$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20220176_M4.png"/></alternatives></inline-formula>increase with the increase of the length and depth of crack, but gradually decrease with the increase of defect buried depth.

Spatial Propagation Law of Magnetic Memory Signals Detected by Using Magnetic Tomography Method

Spatial Propagation Law of Magnetic Memory Signals Detected by Using Magnetic Tomography Method

Effect of applied load and thermal treatment on the magnetic memory signal of defect-bearing Q345R steel samples

Herein, the effects of external loading and heat treatment on magnetic memory field parameters were studied for Q345R steel samples with defects of different width and depth, and a quantitative relationship between the magnetic memory signal and defect size was established. On this basis, we characterized the changes in defect magnetic memory signals at different loading stages and studied the variation in defect magnetic field parameters before and after heat treatment. The obtained results showed that load application increased the magnetic memory field parameters within a certain range, while these parameters significantly decreased upon heat treatment. Therefore, in addition to determining magnetic field parameters, other important factors such as the quantitative relationships between magnetic field parameters and defect size, applied load, and heat treatment state must be considered for accurate defect characterization.

Pipeline Damage Detection Based on Metal Magnetic Memory

The metal magnetic memory detection technology can detect early stress concentration and invisible damage of the pipeline. It can be detected under the action of the geomagnetic field, without the need to magnetize the pipeline in advance, which has the advantage of non-destructive testing. Since the magnetic memory signal is relatively weak, the actual detected signal will be affected by environmental noise, sensor jitter, and pipeline surface deposits. Therefore, the magnetic memory signal needs to be denoised. In this article, the translation invariant wavelet denoising method, which is improved based on the wavelet threshold denoising method, is used to denoise the collected pipeline magnetic memory signals. The experimental results show that the signal-to-noise ratio (SNR) obtained by this method is 1.15% higher than the unmodified wavelet threshold denoising, and the signal obtained is more stable. To solve the problem of fewer data samples for magnetic memory detection, three methods including support vector regression machine, back propagation (BP) neural network, and particle swarm optimization multiple output least-squares support vector regression (MLS-SVR) are compared and analyzed to inverse the overall defect size of pipeline defects. The experiments show that the MLS-SVR inversion result based on particle swarm optimization is the best, and the overall mean square error reaches 0.27 mm. In the end, a pipeline damage detection system is built to detect the magnetic memory signals of pipelines with different sizes of defects, and the defect depth and radius are inverted.