Defect Geometry Research Articles

Machine Learning Modeling for Predicting Tensile Strain Capacity of Pipelines and Identifying Key Factors

Abstract Machine learning (ML) techniques have recently gained great attention across a multitude of engineering domains, including pipeline materials. However, their application to tensile strain capacity (TSC) modeling remains unexplored. To bridge this gap, this study developed and evaluated an ML model to predict the tensile strain capacity of girth-welded pipelines. The model was trained on over 20,000 data points derived from a TSC equation available in the literature. The ML model demonstrated robust performance in predicting tensile strain capacities. Evidence of this lies in the near-zero means, minimal standard deviations, and normal distribution of residuals for both the training and test datasets. These collectively suggest that the model provides a good fit for the data. Furthermore, the model's loss behavior indicates successful convergence and generalization, without signs of overfitting or underfitting. An analysis using the random forest method revealed that the geometry of the flaw, specifically the flaw depth, is the most influential variable in predicting the TSC. This could be attributed to its significant impact on the fracture toughness of materials. In contrast, material properties and fracture toughness exert less influence relatively, despite their contributions to the model. This finding underscores the importance of flaw geometry in TSC prediction models. Overall, the development of a data-driven TSC model has shown efficient TSC modeling. This model leverages ML techniques, allowing for continuous updates with new data via deep learning.

Holographic scattering and non-minimal RT surfaces

In the AdS/CFT correspondence, the causal structure of the bulk AdS spacetime is tied to entanglement in the dual CFT. This relationship is captured by the connected wedge theorem [1], which states that a bulk scattering process implies the existence of O(1/GN) entanglement between associated boundary subregions. In this paper, we study the connected wedge theorem in two asymptotically AdS2+1 spacetimes: the conical defect and BTZ black hole geometries. In these settings, we find that bulk scattering processes require not just large entanglement, but also additional restrictions related to candidate RT surfaces which are non-minimal. We argue these extra relationships imply a certain CFT entanglement structure involving internal degrees of freedom. Because bulk scattering relies on sub-AdS scale physics, this supports the idea that sub-AdS scale locality emerges from internal degrees of freedom. While the new restriction that we identify on non-minimal surfaces is stronger than the initial statement of the connected wedge theorem, we find that it is necessary but still not sufficient to imply bulk scattering in mixed states.

Co-effects of parallel flaws and interface characteristics on the fracture behavior of rock-concrete composite specimens

The fracture behaviour of rock-concrete composite (RCC) containing interfaces and flaws is important for assessing the stability and safety of concrete systems constructed on rock foundations. Uniaxial compression experiments were performed on RCC specimens, and acoustic emission (AE) and digital image correlation (DIC) techniques were adopted to capture the characteristics of AE and crack extension process. The impacts of interface dip angle, concrete strength, and flaw geometry on the coalescence behavior of RCC specimens were analyzed. The findings indicate that four kinds of crack coalescence patterns were recognized: indirect coalescence, left-tip and right-tip cracks connected, coalescence of same-side tip cracks, and no connection. The peak strength displays a reducing trend with increasing the interface dip angle, while the peak strength presents an increasing trend with increasing concrete strength. The overlapping parallel distribution of composite specimens presents a higher peak strength. The crack coalescence mode of overlapping parallel distribution changes from the coalescence of same-side tip cracks to the no-connection mode when interface dip angle is larger. It is worth noting that cracks tend to appear first in the concrete when the strength is relatively low. The AE counts tended to increase as the interface dip angle increased, with the earliest rise in AE counts in the left-stepping distribution and longer duration in the overlapping parallel distribution. Additionally, the energy characteristics of composite specimens correlate closely with the interface dip angle and flaw geometry.

Mechanical property improvements of LPBF-AlSi10Mg via forging to modify microstructure and defect characteristics

Additive Manufacturing (AM) processes have versatile capabilities but are susceptible to the formation of as-cast non-equilibrium microstructures, process-induced defects, and porosity, which have deleterious effects on the mechanical performance. As part of our NSF-ERC-HAMMER program, isothermal forging was investigated as a novel post-processing technique for refining microstructure, reducing process defect severity, and thereby improving mechanical properties. Specimens of Laser Powderbed Fusion (LPBF) AlSi10Mg were fabricated over a range of process parameters and tensile tested as a baseline. Initial work focused on duplicate AM material that was then hot forged with 20 % strain to investigate the effects of isothermal forging at one temperature and strain rate on the microstructure, tensile, and fatigue properties of the as-deposited materials. The microstructures, process-induced defect populations, and tensile/fatigue properties of both as-deposited and forged materials were quantified and analysed by OM, EBSD, XCT, and SEM by various NSF-ERC-HAMMER team members. Isothermal hot forging was found to induce recrystallisation and modify process-induced defect geometry along with increasing tensile ductility. The effects of AM deposition parameters and forge post-processing conditions on LPBF AlSi10Mg will be discussed in terms of microstructure, mechanical properties, and fractography.

Effect of welding gas mixtures on weld material and bead geometry

Abstract Shielding gas is an essential parameter in welding processes, serves to shield the molten material from the atmosphere and external contaminations while providing a stable electric-arc pathway during the process, thus influencing the quality of the weld bead and several other aspects of the process. Therefore, any welding-based process, such as wire-arc additive manufacturing, could benefit from shielding gas mixture optimisation to selectively improve the processing and possibly the final mechanical properties of the welded material. In this study welding of EN AW 5183 aluminium (single weld bead and wire-arc additive manufacturing build-up) was conducted using several gas mixtures and their influence on final geometry defects, and microstructure measured. The gas mixtures used were a combination of varying amounts of carbon dioxide, nitrogen, and oxygen. These were added to commercially available argon gas. It was observed that: CO2 additions led to an unfavourable bonding defect and increased mean grain size; nitrogen additions caused a decrease in mean grain size, decreased pore count while maintaining a similar pore volume fraction to the reference argon mixture samples; oxygen additions showed slight reduction of mean grain size, decreased pore count and decreased pore volume fraction. These results further showcase that varying shielding gas mixtures influence the final material properties and should be considered as an additional improvement route for conventional welding and additive manufacturing.

Critical physics-informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects

The significant scatter in high cycle fatigue life of additively manufactured metallic components presents an increasing challenge to structural integrity. This fatigue life variation is radically attributed to the differences in physical features of critical defects that lead to crack initiation. To address this issue, this paper proposes an integrated framework for identifying critical defects and predicting fatigue life using physics-informed machine learning, with a focus on the impact of 3D defect features. By employing X-ray tomography, high cycle fatigue tests, and fractography analyses on post-mortem specimens, a dataset associated with mass internal defects is first built up to correlate the spatial geometric features of critical defects with fatigue life. A kernel support vector machine is then used to formulate a critical defect identification model, aimed at identifying critical defects among numerous defects by evaluating their geometric attributes. Finally, a fatigue life prediction model is developed using a physics-informed neural network, which incorporates the influence of defect geometry on fatigue life as physical constraints in the loss function. The integrated framework demonstrates that fatigue life predictions from identified critical defects in each specimen exhibit small deviations, with the average prediction falling within twice the error bands. This study is expected to provide a valuable reference for fatigue assessment of additively manufactured components through sequential critical defect identification and fatigue life prediction.

Generative AI model trained by molecular dynamics for rapid mechanical design of architected graphene

Generative artificial intelligence (AI) is shown to be a useful tool to automatically learn from existing information and generate new information based on their connections, but its usage for quantitative mechanical research is less understood. Here, we focus on the structure-mechanics relationship of architected graphene as graphene with void defects of specific patterns. We use Molecular Dynamics (MD) to simulate uniaxial tension on architected graphene, extract the von Mises stress field in mechanical loading, and use the results to train a fine-tuned generative AI model through a Low-Rank Adaptation method. This model enables the freely designed architected graphene structures and predicts its associated stress field in uniaxial tension loading through simple descriptive language. We demonstrate that the fine-tuned model can be established with a few training images and can quantitatively predict the stress field for graphene with various defect geometries and distributions not included in the training set. We validate the accuracy of the stress field with MD simulations. Moreover, we illustrate that our generative AI model can predict the stress field from a schematic drawing of the architected graphene through image-to-image generation. These features underscore the promising future for employing advanced generative AI models in end-to-end advanced nanomaterial design and characterization, enabling the creation of functional, structural materials without using complex numerical modeling and data processing.

Determination of equivalent crack length for voids in metallic alloys based on continuum damage mechanics modeling

Metals contain different kinds of spatial defects significantly reducing the fatigue performance. Accurate defect-based life prediction methods are required to improve structural integrity analysis and life design reliability. The present work developed a method to determine the equivalent crack length for defects based on continuum damage mechanics. The spatial effects of defects in the calculation of the equivalent crack were quantified and verified by fatigue tests. It is confirmed that the equivalent crack length of both slit and elliptic defects is affected not only by the defect geometry but also by the applied load. Based on the equivalent crack and combined with Paris’ law, the predicted fatigue lives of various slit holes under different loading orientations are within the double scatter band and are much more accurate than the conventional projection method.

Experimental Investigations on the Fabrication of Low Alloy Steels Using Wire Arc Additive Method

Wire arc additive manufacturing (WAAM) has emerged as a transformational technology with the capacity to redefine the landscape of alloy steel component production. This method utilizes an electric arc to selectively fuse a continuous wire feed, progressively constructing intricate metal structures layer by layer. GMAW-based WAAM adaptability enables the creation of complex geometries and customized part designs, making it applicable across diverse industrial sectors. This paper investigates WAAM-specific applications in alloy steel fabrication, focusing on key process variables such as voltage, travel speed, and Gas mixture ratio. The experimental parameters for a single layer are examined with voltage ranging from 20 to 22, travel speed from 23, 25, and 27 mm/min, and Gas mixture ratio 1,5,9 mm/min. It utilizes materials like TM B9, a 1.2- diameter flux-cored wire. Additionally, the study considers the nominal composition (wt-%) of 9 Chromium, and 1 Molybdenum, with small additions of nitrogen and niobium to improve the creep resistance. Furthermore, the final findings of the study suggest that the optimal combination of process variables for achieving highquality weld beads in WAAM of alloy steel components can be determined through Response Surface Methodology (RSM). RSM is a statistical method for analysing and modeling the relationship between the response of interest and some independent variables. In this case, the response variable would be the quality of the weld bead, which encompasses factors such as bead geometry, integrity, and absence of defects.

Using fractal voids to understand Mode I compressive fracture in brittle materials – A two-dimensional analysis

Unlike cases of direct or indirect tensile loading, analysis of Mode I fracture in cases of compressive loading must necessarily consider two-dimensional flaw geometries. The consequent analytical complications have resulted in little theoretical evolution towards understanding Mode I fracture in compressive loading. Researchers have recently observed that the linear elastic (LE) stress fields surrounding two-dimensional flaws in compression appear to have indicative value regarding Mode I fracture, but no rational framework has yet been proposed for their interpretation. Herein it is demonstrated that by idealizing void surfaces as fractal, and using what will be called the “small flaw assumption,” LE stress fields can be interpreted to obtain significant insight regarding brittle Mode I compressive fracture. Using fractal voids, a rational and consistent explanation for the satisfaction of the stress and energy criteria at the surface of two-dimensional flaws is presented. The discussion resolves many typical theoretical issues in the literature in a manner consistent with fundamental Griffith theory, and accounts for size and shape effects. The framework further enables the computationally efficient prediction of peak KI values associated with the propagation of line cracks from two-dimensional flaws from a brittle medium subject to macroscopic uniaxial compression via LE stress fields.

Pre- and Post-Improvement Performance of a Railway Embankment Stabilized with Ductile Inclusions

Large deflections and accumulation of permanent track geometry defects at railway soft spots cause significant operational issues including increased maintenance costs, decreased train speed, and, at an extreme, derailments and track failures. Reinforcing inclusions placed between the ties are a promising rehabilitation scheme to locally stabilize the embankment and transfer the loads to deeper, more suitable soils. In this paper we report the results of a long-term monitoring program to assess the performance of ductile inclusions, known commercially as “GeoSpike® elements”, to stabilize a rapidly deteriorating railway soft spot. Observations of track bed behavior are presented for 8 months prior to installation and 15 months after installation using a combination of LIDAR, Digital Image Correlation (DIC), and ShapeAccelArrays (SAA). Through the monitoring program, it was determined that the ductile inclusions decreased the average rate of deterioration by a factor of approximately 3, which allowed the frequency of site maintenance to be decreased from three times a year to once a year.

Exploring defect behavior in helium-irradiated single-crystal and nanocrystalline 3C-SiC at 800°C: A synergy of experimental and simulation techniques

In this study, single crystal (sc) and nanocrystalline (nc) 3C-SiC samples were subjected to 30 keV He ion irradiation across various doses while maintaining a temperature of 800 °C. Employing techniques including Raman spectroscopy, transmission electron microscopy (TEM), and nanoindentation, the alterations in microstructure and hardness resulting from He irradiation with various fluences were examined.In sc-SiC, irradiation prompted the formation of He platelets, resulting in a hardness increase of 7 GPa. In contrast, nc-SiC, characterized by a higher stacking fault density, exhibited the formation of bubbles, primarily at grain boundaries (GBs), with fewer occurrences within the grain interior, leading to a hardness increase of 1 GPa. Notably, in both sc- and nc-SiC, hardness reached saturation and subsequently stabilized or declined with increasing He fluence.Through molecular dynamics (MD) cascade simulations, we discerned that various planar defects do not uniformly contribute to enhancing radiation resistance. For example, intrinsic stacking faults (ISF) and twins in SiC played a substantial role in altering defect density and configurations, thereby facilitating point defect annihilation. Conversely, extrinsic stacking faults (ESF) and Σ3 GBs had a limited impact on defect production during a cascade.Furthermore, calculations of cluster diffusivity revealed an accelerated movement of He-vacancy towards GBs compared to bulk material and other planar defects. Moreover, the scarcity of point defects and constrained mobility of He atoms towards stacking faults in nc-SiC elucidated the marked tendency of He to form platelets in sc-SiC.Additionally, our findings established a correlation between the calculated indentation hardness and the geometry of He defects, consistent with experimental results from nanoindentation. These results significantly contribute to ongoing efforts to design SiC materials with heightened radiation tolerance.

Bridging the Gap: Unraveling the Mechanisms of Nanobubble Detachment from Electrode Surfaces

Gas evolution reactions play a fundamental role in the functionality of various electrochemical devices. Recent experimental studies have provided valuable insights into the behavior of gas bubble formation on nanoelectrodes; however, the experiments have limited spatial resolution to fully understand the underlying mechanisms governing the formation of nanobubbles and their stability on the surface as well as the effects of nanoscopic surface defects on their behavior. A comprehensive understanding of how bubbles behave on electrode surfaces and in the electrolyte is essential for optimizing and designing electrochemical processes. In this work, we employ all-atom molecular simulations to explore the effects of surface chemistry and defect geometry on detachment behavior of hydrogen gas bubbles on nickel electrodes at molecular level. Analyzing the dynamics of bubble detachment on the surfaces with different chemistries reveals that the rate of bubble detachment is higher on the strongly hydrophilic surface compared to the surface exhibiting weak hydrophilicity. In addition, our findings indicate that the defect geometry has significant effects on the detachment of bubbles from the surface. Specifically, the width and depth of canonical-shaped defects are demonstrated to be critical parameters affecting the behavior of the hydrogen bubbles on the nickel surface. Overall, our results show there is a complex balance between the degree of surface wettability and surface defects, determining the stability of nanobubble formation on the electrode surfaces. The results of this study could enhance our understanding of bubble detachment dynamics on the electrode surfaces, providing valuable insights for the future design of practical applications.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC5207NA27344. Release: LLNL-ABS-858292

Numerical Modeling of a Low-Cobalt All-Solid-State Cell with Ceramic Electrolyte Using a Deformable Geometry

All-solid-state batteries with a lithium negative electrode and a ceramic electrolyte are key toward high energy density. To ensure a safe, fast, accurate, and cost-effective development of this technology, the experimental methodology must be supported by the numerical modeling approach. This work proposes and describes an electrochemical model of a Li7La3Zr2O12 (LLZO) and Ni-rich NMC-based lithium cell with a deformable lithium negative electrode. Simulations were computed using the finite element method at different operating conditions to demonstrate the scope of the modeling work. Discharge rate tests, deformation tracking, geometric defect investigation, and polarization decomposition are described. Theoretical validation of the mass balance, the stripping rate, the ohmic polarization, and the mesh deformation demonstrated the consistency of the volumetric deformation strategy. We demonstrated in this study a deformable modeling strategy, which was found to be useful for the electrostripping analysis of anodic geometry defects during discharge. Non-uniformity in the lithium stripping rate was found along the anodic interface with defects, and this non-uniformity was accentuated with a higher discharge rate. The cell’s discharge potential was decomposed by considering the equilibrium potential and the polarizations of the main components of the cell. This post-processing was found to be useful for the understanding of the cell’s behavior.

A numerical analysis of the mechano-electrochemical effects on localised corrosion in pipelines using a multi-physical field coupling approach

In this study, we used a multi-physics coupling approach integrating the electrochemical and solid mechanics fields to develop a three-dimensional finite element model. This model was specifically designed to investigate the effects of mechano-electrochemical (M-E) effects on pipeline corrosion. The boundary conditions for the finite element simulation were established using experimental data, enabling the model to effectively predict the corrosion potential and current density, which aligned well with the experimental findings. Subsequently, the validated model was applied to examine the M-E effects near the pipe corrosion defects, considering various depths of corrosion defects and axial strain. Our results indicate that the stress concentration at the core of the defect increases with increasing axial tensile strain and deepening corrosion. The investigation of the correlation between the corrosion potential and net current density revealed that mechanical forces significantly impact metal corrosion rates, thus influencing pipeline longevity. When the metal structure of the pipeline is deformed within the elastic deformation range, the impact of the M-E effects on corrosion is minimal. However, when a tensile strain is applied or the defect geometry induces plastic deformation at the defect site, a notable increase in the local corrosion activity is observed.

Topological defects on solutions of the non-relativistic equation for extended double ring-shaped potential

In this study, we focus into the non-relativistic wave equation described by the Schrödinger equation, specifically considering angular-dependent potentials within the context of a topological defect background generated by a cosmic string. Our primary goal is to explore quasi-exactly solvable problems by introducing an extended ring-shaped potential. We utilize the Bethe ansatz method to determine the angular solutions, while the radial solutions are obtained using special functions. Our findings demonstrate that the eigenvalue solutions of quantum particles are intricately influenced by the presence of the topological defect of the cosmic string, resulting in significant modifications compared to those in a flat space background. The existence of the topological defect induces alterations in the energy spectra, disrupting degeneracy. Afterwards, we extend our analysis to study the same problem in the presence of a ring-shaped potential against the background of another topological defect geometry known as a point-like global monopole. Following a similar procedure, we obtain the eigenvalue solutions and analyze the results. Remarkably, we observe that the presence of a global monopole leads to a decrease in the energy levels compared to the flat space results. In both cases, we conduct a thorough numerical analysis to validate our findings.

Effects of Pöschl-Teller potential on approximate ℓ ≠ 0-states solution in topological defect geometry and Shannon entropy

This study is centered on examining the behavior of quantum particles governed by the Schrödinger equation, particularly when subjected to a trigonometric Pöschl-Teller potential within the context of a topological defect environment. We set out to derive the radial wave equation and employ the Nikiforov-Uvarov method to solve it and present the eigenvalue solution of the quantum system. In fact, it is shown that the topological defect alters both the energy eigenvalues and the corresponding wave functions of quantum particles, diverging from the behavior observed in flat space with this potential. Moreover, we compute the Shannon entropy for this quantum system under investigation and assess how the presence of the topological defect and potential influences it.

On Defect Evolution in EBM Additively Manufactured Ti-6Al-4V via In Situ Investigations.

This study concerned the in situ investigation of the defect evolution and fracture mechanism of additively manufactured (AM) Ti-6Al-4V under uniaxial tensile tests. In order to achieve this, microstructure characterization was initially carried out in order to identify the defects within the matrix of the candidate material. In situ testing was then performed, focusing on the spherical defect to observe its evolution under tensile loading. It was found that, before the fracture stage, the geometric evolution of the spherical defect towards an ellipse shape was dominated by the load in the tensile direction. In addition, the slip band density was found to be aggravated near the spherical defect due to the geometric discontinuity-induced stress concentration. During the fracture process, the defect geometry evolved as an irregular shape, which was mainly attributed to the micro-void-induced localized multi-axial stress state. The fracture analysis indicated that defects play a key role in crack initiation, leading to the fracture of LPBF materials.

Machine-learning structural reconstructions for accelerated point defect calculations

Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the structural landscape of neutral point defects. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73%. Using CdSexTe1−x alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurationally complex systems.

Nondestructive Inspection and Quantification of Select Interface Defects in Honeycomb Sandwich Panels.

Honeycomb sandwich panels are utilized in many industrial applications due to their high bending resistance relative to their weight. Defects between the core and the facesheet compromise their integrity and efficiency due to the inability to transfer loads. The material system studied in the present paper is a unidirectional carbon fiber composite facesheet with a honeycomb core with a variety of defects at the interface between the two material systems. Current nondestructive techniques focus on defect detectability, whereas the presented method uses high-frequency ultrasound testing (UT) to detect and quantify the defect geometry and defect type. Testing is performed using two approaches, a laboratory scale immersion tank and a novel portable UT system, both of which utilize only single-side access to the part. Coupons are presented with defects spanning from 5 to 40 mm in diameter, whereas defects in the range of 15-25 mm and smaller are considered below the detectability limits of existing inspection methods. Defect types studied include missing adhesive, unintentional foreign objects that occur during the manufacturing process, damaged core, and removed core sections. An algorithm is presented to quantify the defect perimeter. The provided results demonstrate successful defect detection, with an average defect diameter error of 0.6 mm across all coupons studied in the immersion system and 1.1 mm for the portable system. The best accuracy comes from the missing adhesive coupons, with an average error of 0.3 mm. Conversely, the worst results come from the missing or damaged honeycomb coupons, with an error average of 0.7 mm, well below the standard detectability levels of 15-25 mm.