Metal Magnetic Memory Research Articles

Variations of the residual magnetic fields of X70 pipeline steels with double defects

The aim of this research is to correlate residual magnetic fields (RMFs) with double defects in ferromagnetic steels. Specimens of X70 pipeline steels were machined into smooth plates with two types of defect (single and double defects) for comparison. Tensile tests were carried out to detect the RMF signals on the surface of the defective specimens. The variation of the tangential component of the RMF signals was presented and magnetic parameters were defined to investigate the abnormal magnetic changes of the two types of defect. It was found that the peak value of the tangential component was not influenced by defect types and the valley value was capable of evaluating the interaction effect between defects. Correlation between the RMF signals and the location of double defects was also investigated. This research will promote the investigation of defect clusters in ferromagnetic steels based on the metal magnetic memory (MMM) technique.

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.

The Metal Magnetic Memory Method and Its Application for Early Detection of Stress Zones in NPP Piping

The policy of each NPP is to ensure safe production of electrical energy as well as security of supply. It is known that stress concentration (SC) zones, in which corrosion, fatigue and brittle processes develop most intensively, are the main sources for the occurrence of failures in operational structures. In this paper the method of magnetic memory of metal (MMM method) is discussed. This method was developed in order to conduct an early diagnosis of the condition of the metal of the technological equipment. The method is new and has not yet been studied in depth. The article presents the essence of the method, equipment and scheme of scanning the object. The method was applied to perform a pipeline test of NPP equipment. The results are given and verified. The aim of the article is to present the application of the Metal magnetic memory method in practical terms for performing technical diagnostics of the equipment in NPPs.

Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning

Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability.

Statistical quantitative assessment of stress in steel strands via MMM technique

The accurate monitoring and assessment of the stress state for steel strands is essential to ensure the safe service of engineering structures. In this study, the metal magnetic memory (MMM) technique was innovatively employed to assess the stress in steel strands. The MMM measurement experiments under tensile loads of the 8 steel strands were conducted. The variation laws of the Hz signals (the normal component of MMM signals) with tensile loads were analyzed from theoretical analysis and experimental results. The quantitative exponential relationship between the evaluation index Iσ and the tensile load F was proposed, and the mean value of the goodness-of-fit was 0.943. Finally, based on the Bayesian method, the stress in steel strands was quantitatively assessed via the normalized index G from the perspective of probabilistic statistics. The mean value of the posterior probability reached 67.9%. This study provides a new method for stress assessment of steel strands.

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.

Inspection of Semi-Elliptical Defects in a Steel Pipe Using the Metal Magnetic Memory Method

Ferromagnetic pipes are widely used for fluid transportation in various industries. The failure of these ferromagnetic pipes due to surface defects can generate industrial accidents, economic losses, and environmental pollution. Non-destructive testing techniques are required to detect these surface defects. An alternative is the metal magnetic memory (MMM) method, which can be employed to detect surface flaws in ferromagnetic structures. Based on this method, we present an analysis of experimental results of the magnetic field variations around five different surface semi-elliptical defects of an ASTM A36 steel pipe. A measurement system of MMM signals is implemented with a rotatory mechanism, a magnetoresistive sensor, a data processing unit, and a control digital unit. The MMM method does not require expensive equipment or special treatment of the ferromagnetic structures. In order to research a potential relationship between the defect sample size and the measured MMM signals, variable defect dimensions are experimentally considered. According to these results, the shape and magnitude of the normal and tangential MMM signals are altered by the superficial semi-elliptical defects. In particular, the maximum and mean tangential components and the maximum and minimum normal components are related to the defect dimensions. The proposed measurement system can be used to study the behavior of magnetic field variations around surface defects of ferromagnetic pipes. This system can be adapted to measure the position and damage level of small defects on the surface of ferromagnetic pipes.

Non-Contact Magnetometric Diagnostics of Pipelines Using the Metal Magnetic Memory Method

The non-contact magnetometric diagnostics (NCMD) of buried pipelines (oil and gas pipelines, heat lines, water supply lines, etc.) has been developed by Energodiagnostika Co. Ltd. since 2000 based on the 25-year experience of the metal magnetic memory (MMM) method application (ISO 24497-1:2007(E)). NCMD is based on measurement along the pipeline route of the Earth’s magnetic field intensity (HEarth) distortions caused by variation of the pipe metal magnetization in stress concentration zones (SCZs) and in zones of developing corrosion-fatigue damages. The paper describes the experience of complex diagnostics of buried pipelines based on NCMD, the MMM method and conventional methods of non-destructive testing. Based on NCMD results, the most strained pipeline sections are determined for their opening and additional control by the MMM method, ultrasonic testing and other NDT methods. On opened pipeline sections the MMM method is used to determine the presence of defects and the mechanical properties of the metal by hardness parameters. Inadmissible defects are removed, and the actual mechanical properties (yield strength and ultimate strength) are taken into account in check strength calculations. The inspection should provide the answer to the following question: “Where and when should a damage or an accident be expected?”. If this task is solved, then the possibility of timely replacement or repair of a potentially hazardous section is provided. Application of NCMD in combination with additional inspection of pipelines (UT, eddy current, etc.) in prospect holes determined by NCMD is aimed exactly at solution of this problem. The paper presents the experience of NCMD of buried polyurethane foam insulated pipelines in order to detect girth welded joints with developing damage.

Identification of structural defects and their impact on the magnetic memory, static and cyclic strength of VNS9-SH thin sheet trip-steel

Identification of microstructure defects, which are stress concentrators (SC) during the operation of mechanical engineering products, is an important scientific and practical task relevant for manufacturing enterprises. This problem becomes especially urgent and difficult for critical helicopter parts made of sheet TRIP steel VNS9-Sh (23Kh15N5AM3-Sh) and operating under cyclic loads due to the complex microstructure of the steel and small thickness of strips and sheets. To assess the impact of structural defects on the cyclic strength of products made of the steel under study, the specimens were preliminary sorted proceeding from the results of their testing using the method of metal magnetic memory (MMM) and metallographic studies. The MMM method is a structure-sensitive procedure which provides information about the presence of structural defects that arise during the manufacture. Magnetic anomalies in the form of sharp local changes in the intrinsic stray magnetic field (SSMF) (H) and its gradient |ΔH| along the length of the controlled section Δx, were identified on the surface of sheets cut from five different batches. A conventional classification of the identified anomalies was made according to the magnitude of the magnetic field gradient. The specimens of two types were cut in zones of magnetic anomalies and outside them: type 1 — for cyclic tests and type 2 — for metallographic studies. The geometric parameters and field gradient values of magnetic anomalies on specimens of type 1 and type 2 were the same. Metallographic studies in zones of maximum magnetic field gradient on type 2 specimens revealed defects in the form of a strip at the boundary of different structures, which is a structural stress concentrator (SSC) and a source of the inhomogeneity and changes in the magnetic properties. Type 1 specimens with similar magnetic anomalies and Type 1 specimens cut from sheets outside zones of magnetic anomalies were then selected for cyclic testing. Comparative tests for cyclic strength of the specimens with and without specified SSC were carried out. It is shown that the presence of SSC zones in the specimens reduces the number of cycles to failure during cyclic tests by 1 – 2 orders of magnitude compared to the specimens free of SSC. Based on cyclic tensile tests of specimens, a limiting value of the magnetic field gradient was determined that corresponds to the acceptable level of stress concentration on structural defects. This value is recommended for use as a rejection criterion when examining a new tape by the MMM method.

Research on the defect depth detection for pipeline steel with double defects using metal magnetic memory method

Metal magnetic memory testing is an effective method to detect the stress state of ferromagnetic materials, and shows great application potential in the field of defect detection of oil and gas pipelines recently. In this paper, the correlation between the normal component of residual magnetic field (RMF) and the defect depth, as well as the applied loading were investigated by tensile tests. The X70 pipeline steels were processed into specimens with double defects of six types of depths and three types of spacings. Some magnetic parameters are defined to characterize the distortion of the RMF in the defect area. The effect of defect depth and applied loading on the magnetic parameters is discussed. The RMF signals measured on the smooth side of the specimens were compared with those on the defective side. Results show that the normal parameters, obtained from the front and back of the specimens, are capable of detecting the defect depth. The slope of the linear relationship between the two increases with the increase of defect spacing. The expressions of normal magnetic parameters were derived based on the Jiles model and magnetic dipole model. The simulation results of defective side and smooth side were compared, as well as the simulation results of different defect spacing. The experimental and numerical simulation results show that the defect depth of pipeline steel can be evaluated by the characteristic parameters of the normal RMF signal under a certain stress level.

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.

Research on the force-magnetic relationship of bridge steel under tensile stress based on magnetic memory effect

It’s of great significance to conduct the nondestructive evaluation of steel bridges to extend their service life. The metal magnetic memory testing (MMMT) method can effectively evaluate the stress states of ferromagnetic components. To improve the accuracy and reliability of the stress evaluation on bridge steel structures based on MMMT technology, an improved force-magnetic coupling model considering loading history is proposed, which can reflect the impact of material mechanical properties on magnetization behavior. The MMMT is carried out on the Q345qD bridge steel plates under load-unload tensile stress considering the relative location between the specimen and the geomagnetic field, according to the rotation angle θr. It comprehensively analyzes the variation laws of representative tangential and normal components of the magnetic signal, i.e. Hm(y) and Hm(z), corresponding to different inspection parameters during the loading process. The mean value Ay of the Hm(y) curves and the linear fitting slope Kz of Hm(z) curves are applied to characterize the variation laws of magnetic signal with respect to the applied load Fa. According to the fitting curves of Ay and Kz with Fa, it quantitatively demonstrates the applied loads. It further puts forward the normalized magnetic characteristic parameters Ayn and Kzn to characterize the force-magnetic relationship of the specimen, which are less affected by the position of the specimen in geomagnetic field. Finally, the proposed force-magnetic coupling model is applied to calculate the variation of normalized magnetization ΔMn. The theoretical results are compared with the synthetic magnetic characteristic parameter AKn obtained by experiment. It shows an average error of 8.53% between ΔMn and AKn, which proves the feasibility and effectiveness of the force-magnetic coupling model proposed in this paper.

Performance evaluation of a magnetic NDE technique to assess residual fatigue life of the retired hydraulic cylinders

Metal magnetic memory (MMM) method is a novel and potential nondestructive evaluation (NDE) technique to assess residual fatigue life of the ferromagnetic structure, such as hydraulic cylinder. In this paper, an evaluation framework was proposed. Firstly, the MMM signals under different fatigue cycles were recorded in the tensile-tensile experiment. A representative signal characteristic Hpp was defined as an evaluation index. The monotonic change trend and abrupt change point was investigated. Statistical results show that Hpp can be used to predict residual fatigue life. Except for a few specimens, almost all specimens produced an abrupt change at the normalized fatigue life coefficient k = 0.91. This can be used to distinguish different stages of fatigue life. The sensitivity, specificity, and diagnostic performance were analyzed using Receiver Operating Characteristic (ROC) curves. The statistical results show that when the coefficient k = 0.91, the diagnostic performance is relatively best with an area under the curve (AUC) is about 0.85, and Jorden's index is about 0.75.

Metal magnetic memory characterization of fatigue crack propagation of Q345qD bridge steel under the influence of stress ratio

Fatigue fracture failure has been one of the major causes of damage in steel bridges. To better use fracture mechanics to study this failure behavior, the fatigue crack growth behavior of bridge steel plates under different stress ratios was investigated by metal magnetic memory nondestructive testing technique. The changes of magnetic signals during fatigue crack growth were recorded and analyzed, and then the magnetic signal characterization method of fatigue crack growth length was discussed both theoretically and experimentally. The stress ratio was incorporated into the model to establish a quantitative relationship equation between the magnetic signal characteristic and the crack growth length. Subsequently, the proposed relationship was applied to the fatigue crack growth remaining life prediction study and verified by experimental results. The results showed that the magnetic signal gradient is suitable as a characteristic parameter to quantitatively evaluate the fatigue crack growth length, and the relative errors between the prediction results and the test results were all within 15 %.

High Lift Value Metal Pipeline Detection Model Based on Metal Magnetic Memory 3-D Differential Method

High Lift Value Metal Pipeline Detection Model Based on Metal Magnetic Memory 3-D Differential Method

Influence of microstructure and geometric dimension on metal magnetic memory testing

Metal magnetic memory testing (MMMT) is a prominent NDT method for early damage diagnosis of ferromagnetic structures due to its significant advantages in stress concentration state evaluation. While it is still challengeable to apply the existing criteria from the laboratory to practical application, and some of the main reasons are the microstructure transformation and geometric dimensions for the ferromagnetic material itself. To explore the influence of microstructure and geometric dimension on MMMT, static tensile tests of specimens made of Optim 900QC steel with different microstructures and geometric dimensions have been carried out, and the MMMT signals of the specimens during the tests were collected and analyzed. It is found that the microstructure not only change the strength of the steel, but also affect the MMMT signal of the steel, and the bigger the grain is, the stronger the magnetic field is. Geometric dimension would also affect the MMMT signal, and thickness affects the MMMT signal the most, width less, and length the least. The results of the present work indicate that the microstructure and the geometric dimensions should be the same to fix the gap of damage determination criteria between the laboratory and the engineering application. When it is impossible to fabricate specimens with the same dimension with the engineering structure, set the same thickness would improve the accuracy of the criteria.

Visualization evaluation of damage degree on remanufacturing cores based on residual magnetic scanning measurement

A novel residual magnetic scanning measurement (RMSM) method with easy operation, low cost and good universality based on metal magnetic memory (MMM) is proposed to evaluate the damage degree of remanufacturing cores. The two-dimensional (2-D) grayscale maps of residual magnetic field (RMF) and residual stress can be visualized, respectively, by constructing the relationship among magnetic field, stress and pixel gray value. The contour of high residual stress zone which enters the plastic stage can be extracted from the 2-D grayscale map based on the gray thresholds representing the yield strength. The defect size can also be inverted with the evaluation error below 10 % and the resolution reaching 0.2 mm. To prove the feasibility of RMSM applying into the damage evaluation on remanufacturing cores, the RMF distribution and the high residual stress zone of a damaged gear are also visualized. The 2-D grayscale maps of RMF and residual stress can clearly show the profile of gear and the plastic deformation damage zone. The RMSM method can facilitate the accurate damage evaluation on remanufacturing cores and provide the support for the subsequent repairing process design.

Research on characteristic quantity and intelligent classification prediction of metal magnetic memory detection signal

<abstract> <p>Metal magnetic memory (MMM) is an innovative, nondestructive testing method. It can detect both stress concentrations and macroscopic defects. The three-dimensional force-magnetic coupling model was established by the ANSYS simulation software, the evolution process of different defect depths was studied in detail, and the change of the signal characteristic was analyzed. The results showed that the variation trend and amplitude characteristic of MMM signals resulted in obvious differences among different defect types. Meanwhile, the impacts caused by the defect parameters and the type are complex, which cannot be decoupled or calculated by a certain formula. The accuracy of the simulation data was verified by experiments. To solve the classification prediction problem in MMM detection, the signal peak and valley <italic>H</italic><sub><italic>p-v</italic></sub>, the signal width <italic>W</italic>, the gradient <italic>K</italic><sub><italic>y</italic></sub>, and the peak energy <italic>H</italic><sub><italic>y</italic></sub> were selected as characteristic parameters to evaluate different defect types according to the change in the signal waveform. Finally, using these vectors as the input variables, the radial basis function neural network (RBFNN) pre-classification test model was established to realize the classification recognition of pit defects, crack defects, and porosity defects. The results show that the accuracy of the training and test sets, and it is feasible to use this model to complete the intelligent classification of defects.</p> </abstract>

A Three-Dimensional Forward Model of Metal Magnetic Memory Method for Quantitative Defect Detection

A Three-Dimensional Forward Model of Metal Magnetic Memory Method for Quantitative Defect Detection