Defects In Metallic Materials Research Articles

Classification Method of Internal Defects in Metal Materials Based on Ultrasonic Point Cloud Data

Abstract The commonly used methods for analysing defects in metal materials include metallography and scanning electron microscopy(SEM). However, these methods require grinding and polishing of the material surface, which can only obtain the characteristics of material surface defects, making it difficult to effectively detect and characterize internal defects in metal materials. Therefore, a deep neural network model based on ultrasonic point cloud data was proposed in this paper, called the Global and Local Feature Fusion Network (GLFFN), to classify the internal defects in metal materials. Firstly, an ultrasonic microscope was used to perform X-layer scanning on the metal materials, obtaining ultrasonic images at different depths, from which ultrasonic point cloud data was extracted. Based on this, the GLFFN model enhances the classification accuracy of internal defects in metal materials by deep fusion of global and local features of the point cloud. With casting billets as the detection object, which contain three typical internal defects: inclusions, shrinkage cavities, and cracks. The accuracy of the new model for defect classification reached 88.24%, which is superior to the traditional methods, proving the effectiveness of the new model on the classification of internal defects in metal materials.

Enhancing precision of defect 3D reconstruction in metal ultrasonic testing through point cloud completion

During the ultrasound multi-layer focused scanning inspection process, the sequential images characterizing the defect morphology suffer from inter-layer contour information loss, which causes a reduction in the accuracy of defect 3D reconstruction, subsequently leading to errors in the characterization of the defect size and morphology. In order to address the above issues, a new method based on the Attention-based Residual Completion Network (ARC) is proposed for ultrasonic point cloud completion to characterize metal defects. Firstly, the ARC network extracts global contour morphological features and local edge detail features from the ultrasonic point cloud through consecutive residual convolutions. Subsequently, the two sets of features are concatenated and finally fed into a decoder based on self-attention, realizing the reconstruction of lost contour information and enhancing the 3D reconstruction accuracy of defects. In the experiment, an ultrasonic microscope was used to inspect actual steel plates. The internal defects were then completed using the ARC network, and the completion results were compared with the metallographic images of the defects. The experiment results indicated that, after completion, the characterization accuracy of defect morphology and sizes is enhanced by an average of 10.31 %. The ARC network provides a novel method for high-precision 3D characterization of internal defects in metal materials.

Advances in Understanding the Evolution Mechanism of Micropore Defects in Metal Materials under External Loads

Micropores are one of the critical factors affecting materials’ performance and service life. As the need for a deeper understanding of micropore evolution and damage mechanisms grows, assessing the mechanical properties of materials containing micropores and predicting the lifespan of related metal structural components becomes increasingly complex. This paper focuses on the evolution process, regularities, and research methods of micropores in metal materials. Based on recent research and practical applications, the key stages of micropore evolution are discussed, encompassing nucleation, growth, coalescence, collapse, interaction, and the influence of other microstructures. Firstly, the advantages and limitations of commonly used characterization methods such as scanning electron microscopy, transmission electron microscopy, and X-ray computed tomography are introduced in the study of micropore evolution. Subsequently, critical theoretical models for micropore evolution, such as the Gurson model and its extensions, are summarized. By using a multiscale approach combining the crystal plasticity finite element method, dislocation dynamics, and molecular dynamics, the factors influencing the micropore evolution, such as external stress conditions, internal microstructures, and micropore characteristics, are specifically elaborated, and the basic physical mechanisms of micropore evolution are analyzed. Finally, a comprehensive review and summary of current research trends and key findings are provided, and a forward-looking perspective on future research directions is presented.

Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum

Grain boundaries (GBs) are fundamental planar defects in metallic materials, exerting a profound influence on material behaviour and properties. Understanding the influence of GB structure on material performance presents challenges due to the intricate atomic arrangements involved. To overcome inconsistencies observed in previous simulations, experimental validation is necessary. However, research on [111] tilt GBs in metals other than copper, such as aluminium, remains limited. This study aims to address these gaps by investigating the characterization of [111] tilt GBs in Al, employing advanced experimental techniques and validating the findings through simulations. By exploring a range of misorientation angles and plane inclinations, two distinct misorientation groups are identified, each exhibiting unique structural configurations. However, the structural differences resulting from plane inclination are more pronounced than those stemming from misorientation angle. Additionally, it is found that deviations from precise misorientation angles and different plane inclinations significantly influence the excess properties of GBs, such as GB energy and excess volume, etc. Furthermore, comparisons between Al and Cu structures enhance our understanding of GB behaviour in different metallic materials. This study lays the foundation for robust structure-property correlations in GBs and emphasizes the importance of considering intricate GB structural details to better understand and predict their properties accurately.

An Enhanced YOLOv5-Based Algorithm for Metal Surface Defect Detection

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability.

Technique for Introducing Internal Defects with Arbitrary Sizes and Locations in Metals via Additive Manufacturing and Evaluation of Fatigue Properties

Mechanical component failure is usually caused by metal fatigue originating from small defects in metallic materials. Thus, it is important to precisely capture the fatigue properties of materials containing small defects. Fatigue tests of materials with artificial surface defects introduced by drilling have been conducted. Using the resulting data, an equation for predicting the material fatigue limit has been proposed on the basis of the √area parameter model, and its effectiveness has been confirmed for various materials. However, for additive manufactured (AM) materials that contain internal defects resulting in failure, controlling the size of the defect where the fracture originates is extremely difficult. Therefore, verification of the predictive ability of the √area parameter model for AM materials is impossible, in contrast with other materials that fail because of surface defects. In this context, developing a technique to intentionally introduce internal defects with arbitrary sizes at arbitrary locations can provide insights that help predict the fatigue limit of AM materials. This study aimed to establish a technology for quantitatively evaluating the effect of internal defects on the fatigue properties of AM materials by introducing internal defects with arbitrary sizes at arbitrary locations via AM. Specimens with different defect sizes and locations were prepared. Prior to the fatigue tests, the defect sizes and locations were measured non-destructively via X-ray computed tomography (CT). The fatigue tests were conducted in air at room temperature. All the specimens failed because of the intentionally introduced internal defects, and the fatigue lives became shorter with increasing defect sizes, except for the specimens with defects adjacent to the surface. In those cases, fatigue cracks easily reached the surface; therefore, the fatigue lives were speculated to be shorter than those of the specimens with the same defect sizes. Moreover, the defect sizes determined from the fracture surfaces by scanning electron microscopy were nearly consistent with those determined by X-ray CT.

Numerical studies of self-organization of shear bands in one and two dimensions

In the present manuscript we perform a numerical analysis of the self-organization processes of adiabatic shear bands formation in depleted uranium, aluminum alloy, and high-strength steel in one- and two-dimensional cases. Since the processes of shear bands formation are strongly nonlinear, three materials with significantly different parameters considered allow us to generalize obtained dependencies. For all materials, we investigate the evolution of stress, temperature and velocity fields from initial to final stage of localization. It is well known that localization processes occur during the high shear rate loads in the presence of initial microstructural defects in metallic materials. Starting with a random distribution of initial stress, which models microstructural defects, we obtained new dependencies for such significant parameters of the problem considered such as localization time and spacing between shear bands. The influence of initial plastic strain rate on these parameters was also studied. We obtain the linear dependence of localization time on initial strain rate and show that spatial dimension significantly influences on its value. Also, we introduce the method of shear bands selection during computations and present new statistical distributions for band spacing. We show that the interaction between shear bands significant influences on the spacing between them.

Detection of defects in metallic materials using an improved post-processing method of pulsed thermography

A low-cost and easy-to-implement method based on the first-order forward difference (FOFD) and infrared thermal signal reconstruction is proposed in this work. The proposed method first adopts the FOFD method to effectively improve the image signal-to-noise ratio (SNR) and detect the defects at deeper locations. After that, an improved thermal signal reconstruction (ITSR) method is proposed to be applied to the long pulse thermography. The numerical calculation and experiment were adopted to study the effectiveness of the method at the detection of defects of metallic materials. The results indicate that the proposed FOFD method improves the SNR by 80.92% over the raw image. The error of the proposed ITSR method is less than 0.4oC and the relative error of the experimental depth is only 9.65%. Comparing with existing methods, the proposed method holds the advantage of high efficiency and simple in implementation and is therefore highly recommended for the practice use of the pulsed thermography.

Elucidating the origin of electroplasticity in metallic materials

Electroplastic phenomenon has been demonstrated by that the elongation increases remarkably during deformation under electric current without a significant elevation of temperature due to Joule heating. Since the 1960s, the electroplasticity has been actively investigated; however, an exact explanation of the mechanism has been lacking. In this study, the origin of electroplasticity in metallic materials is elucidated based on first principle calculation, finite element simulation and experimental approaches. First principle calculations on a system that includes a grain boundary, which is the general defect in polycrystalline metallic materials, show that a charge imbalance near defects weakens drastically atomic bonding under electric current. The electroplastic behavior could be well reproduced with a small-scale, microstructure-based finite element simulation, which incorporates an effective temperature near defects under electric current. The effective temperature under electric current reflects the weakening of atomic bonding due to charge imbalance. In addition, the weakening of atomic bonding was confirmed by measuring the elastic modulus under electric current, which is inherently related to the atomic bonding strength. It can be said that the mechanical properties under electric current ultimately depend on the existing defects in metallic materials.

Quantitative Detection of Minor Defects in Metal Materials Based on Variation Coefficient of CT Image

In order to perform precise quantitative detection of internal minor defects in additive manufacturing of metal materials based on industrial CT, a quantitative detection method for minor defects in metal materials based on gray-scale variation coefficient of minor defects in CT image is presented. Firstly, based on the analysis of gray-scale features of minor defect regions in CT image, a minor defect image response model is constructed by combining the point spread function (PSF) and noise approximate Gaussian distribution, and the correspondence between the minor defects of different sizes and the local variation coefficient of CT image is derived, and a quantitative detection method based on the coefficient of variation of minor defect region in CT image is established for minor defects of AM metal materials. Industrial CT experiments were then conducted by processing a series of minor defect reference test blocks of different sizes with TC4 titanium. Experiment results show that the defect size and the coefficient of variation reflect a good linear relationship of monotonous increase, with correlation more than 0.996, and the quantitative results of minor defects based on the coefficient of variation method accord well with the measurement results of the microscope, with relative error controlled within 4%. Obviously, this method characterized by simple measurement process and high practical value has improved the quantitative precision of minor defects in industrial CT.

Quantitative Evaluation of Crack Depths on Thin Aluminum Plate Using Eddy Current Pulse-Compression Thermography

Eddy current stimulated thermography is an emerging technique for non-destructive testing and evaluation of conductive materials. However, quantitative estimation of the depth of subsurface defects in metallic materials by thermography techniques remains challenging due to significant lateral thermal diffusion. This work presents the application of eddy current pulse compression thermography to detect surface and subsurface defects with various depths in an aluminum sample. Kernel Principal Component analysis and Low Rank Sparse modelling were used to enhance the defective area, and cross point feature was exploited to quantitatively evaluate the defects depth. Based on experimental results, it is shown that the crossing point feature has a monotonic relationship with surface and subsurface defects depth, and it can also indicate whether the defect is within or beyond the eddy current skin depth. In addition, the comparison study between aluminum and composites in terms of impulse response and proposed features are also presented.

Defects identification using the improved ultrasonic measurement model and support vector machines

With a combination of the improved ultrasonic measurement model (IUMM) and support vector machines (SVM), a novel method to identify inclusions and cavities in metallic materials using scanning acoustic microscopy is proposed. In the IUMM, a hybrid model of Born approximation and Kirchhoff approximation is developed to calculate the far-field scattering amplitude of cavities, which improves the accuracy in phase and amplitude of the predicted pulse-echo signals of defects. The SVM classifier, with the amplitude and peak frequency of the predicted echo signals as major features, is applied to distinguish inclusions and cavities. The experimental result shows that the echo signals predicted by the proposed IUMM are more accurate than conventional UMM in amplitude and frequency. The SVM classifier, with the predicted signals as the training set, enables the identification of inclusions and cavities in metallic materials successfully. This work improves the performance of SAM in the identification of internal defects in metallic materials and realizes the intelligent analysis of ultrasonic signals.

Semi-analytical method for characterization slit defects in conducting metal by Eddy current nondestructive technique

Defects in the metal components is a typical cause of component failure in engineering, with a lack of timely detection and elimination of these defects usually causing serious accidents. A nondestructive testing method using eddy current, however, exhibits remarkable capabilities in quantitatively monitoring the formation of these artificial defects in metal materials. Owing that the commonly appeared defect species in metal structures is slit defect instead of cylindrical defect, a deeper understanding of the magnetic testing signal correlated to slit defects in eddy current nondestructive method is required. In this paper, based on pervious investigation on cylindrical defect, a semi-analytical method for the magnetic field of a slit defect in eddy current testing system is proposed. Firstly, an analytical expression to the magnetic field for a cylindrical defect in metal is briefly introduced. Then, a model with an elliptical cylindrical defect is studied, and a semi-analytical method is deduced in details. Next, a semi-analytical formula is proposed to calculate the magnetic signals generated by slit defect. Finally, with a case study, the proposed semi-analytical method is applied to calculate the slit defect magnetic testing signal for slit defect in eddy current technique. The results indicated that the maximal relative errors between the semi-analytical solution and FEM are less than 2 % and the computation efficiency is higher than 10 times. With the support of these results, not only our proposed semi-analytical method can be readily used to investigate the forward problem due to the slit defects in eddy current technique, but also it can be further extended to seek the theoretical expression for defects with complex shapes, including those natural defects in conductive metal components (e.g. defects in oil pipe, rail track, iron bridge, oil tank, ocean ship).

Acoustic microscopy signal processing method for detecting near-surface defects in metal materials

When ultrasonic pulse echo technique is used for the detection of defects in materials, the amplitude of the defect echo is considerably lower than that of the interface echo, and the echo generated by the defect in the near-surface always overlaps with the interface echo, leading to the difficult extraction of defect characteristics in the near-surface of a material. Therefore, a method combining adaptive morphological filtering with sparse minimum entropy deconvolution (M-S-MED) was proposed. First, adaptive morphological filtering was applied for the removal of background noise and for making the defect echo obvious. Then, sparse minimum entropy deconvolution was performed on the characteristic signals for the acquisition of the reflected pulse sequences of ultrasonic signals. The depth and size of the defect were accurately evaluated because of the effective separation of the interface and defect echoes. The effectiveness of the proposed method was validated by simulating the signals and detecting a near-surface defect in an actual galvanized sheet. The experimental result revealed that the depth and size errors of the actual defect were 1.9% and 3.5%, respectively.

Statistical analysis of the influence of defects on fatigue life using a Gumbel distribution

Location and scale parameters, λ and δ, of a Gumbel distribution are used to quantify defects in metallic materials. To determine the impact of these parameters on fatigue life performance and variability, fatigue crack growth simulations of 7075-T651 aluminum alloy were performed using a modified strip-yield model to produce S-N data. It was observed that defects representing initial crack lengths following a Gumbel distribution yield a fatigue life that follows a log-normal distribution. It was determined that location and scale parameters impact the fatigue life mean and standard deviation, but have little to no effect on the coefficient of variation.

Characterization of lattice defects in metallic materials by positron annihilation spectroscopy: A review

Positron is an excellent probe of lattice defects in solids. A thermallized positron delocalized in lattice can be trapped at open volume defects, e.g. vacancies, dislocations, grain boundaries etc. Positron annihilation spectroscopy is a non-destructive technique which enables characterization of open volume lattice defects in solids on the atomic scale. Positron lifetime and Doppler broadening of annihilation photo-peak are the most common observables related to positron annihilation process. Positron lifetime spectroscopy enables to identify defects in solids and to determine their concentrations while coincidence measurement of Doppler broadening provides information about local chemical environment of defects. This article provides a review of the state-of-art of defect characterization in bulk metallic materials by positron annihilation spectroscopy. Advanced analysis of positron annihilation data and recent developments of positron annihilation methodology are described and discussed on examples of defect studies of metallic materials. Future development in the field is proposed as well.

The Possibility of liquid-like and amorphous states existing in nano- and microregions of deformed metallic materials

The possibility of the periodic formation of a liquid-like state in nanoregions of highly nonequilibrium grain boundaries and other regions of defects in metallic materials upon superplastic deformation and intense plastic deformation is considered, based on the results from a thermodynamic analysis of a number of experimental data. Important differences between a liquid-like state and a glass-like (amorphous) state are also considered.

Modeling and optimization of open crack detection by flying spot thermography

The aim of this work is the detection of open defects in metallic materials using laser-material interaction coupled with infrared thermography. This process is a possible alternative for magnetic particles testing and dye penetrant testing in the field of non-destructive testing. In this context, a numerical simulation model is implemented with COMSOL Multiphysics®. This model allows us to look for the process optimal parameters through numerical experimental designs. It's an original work for modeling a back and forth scanning. Experimental works are also done in order to validate our numerical model and also to have reliable input data.

金属材料小缺陷超声反射信号建模及识别

金属材料小缺陷超声反射信号建模及识别

Detection of Subsurface Defects in Metal Materials Using Infrared Thermography; Image Processing and Finite Element Modeling

Infrared thermography is an emerging approach to non-contact, non-intrusive, and non-destructive inspection of various solid materials such as metals, composites, and semiconductors for industrial and research interests. In this study, data processing was applied to infrared thermography measurements to detect defects in metals that were widely used in industrial fields. When analyzing experimental data from infrared thermographic testing, raw images were often not appropriate. Thus, various data analysis methods were used at the pre-processing and processing levels in data processing programs for quantitative analysis of defect detection and characterization; these increased the infrared non-destructive testing capabilities since subtle defects signature became apparent. A 3D finite element simulation was performed to verify and analyze the data obtained from both the experiment and the image processing techniques.