Subsurface Voids Research Articles

Femtosecond laser surface modification of 4H-SiC improves machinability

Femtosecond laser surface modification of 4H-SiC improves machinability

High-Sensitivity Low-Energy Ion Spectroscopy with Sub-Nanometer Depth Resolution Reveals Oxidation Resistance of MoS2 Increases with Film Density and Shear-Induced Nanostructural Modifications of the Surface.

For decades, density has been attributed as a critical aspect of the structure of sputter-deposited nanocrystalline molybdenum disulfide (MoS2) coatings impacting oxidation resistance and wear resistance. Despite its importance, there are few examples in the literature that explicitly investigate the relationship between the density and oxidation behaviors of MoS2 coatings. Aging and oxidation are primary considerations for the use of MoS2 coatings in aerospace applications as they inevitably experience prolonged storage in water and oxygen-rich environments prior to use. Oxidation that is either limited to the first few nanometers or through the bulk of the coating can result in seizure due to high initial coefficients of friction or component failure from excessive wear. High-sensitivity low-energy ion spectroscopy (HS-LEIS) and Rutherford backscattering spectrometry (RBS) are both used to understand the extent of oxidation throughout the first ∼10 nanometers of the surface of pure sputtered nanocrystalline MoS2 coatings after high-temperature aging and how it is impacted by the density of coatings as measured by RBS. Results show that low-density coatings (ρ = 3.55 g/cm3) exhibit a more columnar microstructure and voiding, which act as pathways for oxidative species to penetrate and interact with edge sites, causing severe surface and subsurface oxidation. Furthermore, HS-LEIS of surfaces sheared prior to oxidation reveals that the oxidation resistance of low-density MoS2 coatings can be significantly improved by shear-induced reorientation of the surface microstructure to a basal orientation and elimination of pathways for oxygen into the bulk through compaction of surface and subsurface voids.

Siliciclastic content controls the sealing behavior of layers and karst development in a carbonate-siliciclastic sequence, Salitre Formation, Brazil

Although caves formed in carbonate units play a crucial role in fluid flow, most subsurface voids are below seismic resolution. This study investigates how sedimentary facies control the vertical distribution and the development of karst features from small vugs to caves in a carbonate succession with terrigenous input. We analyzed the sedimentary succession and the potential of layers to behave as sealing and conduit units in the karst system. The study area is located in the Neoproterozoic Salitre Formation in the Una-Utinga Basin, São Francisco Craton, Brazil. We selected two caves and their hosting units as analogs of carbonate reservoirs affected by karstification. Our findings indicate that the caves have similar sedimentary and petrographic patterns, composed of three sedimentary facies at a 20–30 m vertical section from the bottom to the top. At the bottom, the lime mudstone facies contains 10% siliciclastic content, which is the pure conduit facies that host most cave passages. A heterogeneous calcarenite containing lime mudstones and siliciclastic intercalation facies occurs in the middle portion, where the siliciclastic content varies between 10-30% and 30–50%. This aforementioned facies exhibits conduit and sealing layers. At the top, a mixed limestone siliciclastic facies has siliciclastic content above 50% and a high degree of compaction. It behaves as a sealing facies. The conduit-sealing system has the same pattern at various scales: macro (observed in drone imagery), meso (Light Detection and Ranging imagery - LiDAR), and microscale (thin section). We conclude that the siliciclastic content of layers controls the degree of karstification at various scales in a carbonate-siliciclastic sequence. • Sedimentary facies control the vertical distribution and the development of karst features from small vugs to caves in a carbonate platforms with terrigenous input. • Lime mudstone and heterogeneous calcarenite facies behave as soluble layers and mixed limestone-siliciclastic facies as an impermeable layer in a conduit-seal system. • The conduit-sealing system presents the same pattern at various scales: macro (observed in drone imagery), meso (LiDAR imagery) and microscale (thin section). . • The ascendant increase of siliciclastic content in lithological sections influences the conduit and sealing patterns of the sedimentary units hosting the caves.

Percussion-based quasi real-time void detection for concrete-filled steel tubular structures using dense learned features

• A percussion-based void detection method using dense learned features was proposed. • The void detector leverages CNN, GAP, and CAM to obtain high accuracy. • The void detector listens to the “echo” part carefully, rather than the “impact” part. • The classification results depend on both the vibration frequency and damping ratio. • High classification accuracies (more than 99%) was obtained. Concrete-filled steel tubular (CFST) structures have been widely used to provide high load-bearing capacity. Percussions are expected to provide simple and effective means of detecting subsurface voids of CFST structures, caused by poor grouting compactness and harsh operating conditions. Existing percussion data processing methods consist of two steps to deal with signals with high sampling rates: heuristic feature extraction and classification. High-dimensional feature representations tend to be selected to achieve visual interpretability and performance, increasing the complexity and computational cost of the entire process. This research aims at developing a computationally efficient end-to-end framework for the percussion-based void detection of CFST structures suitable for mobile computing devices available in the field, while maintaining the performance and a certain degree of visual interpretability. The proposed approach is based on the dense learned features; First, a classifier that processes raw percussion sound signals is developed using dilated convolutions and global max-pooling. The class activation mapping (CAM) technique is then applied to convert the classifier into the dense feature extractor. Feature visualization for the experimental data shows that (i) the classifier listens to the “echo” of the percussion sound carefully, rather than the impact sound itself, and (ii) the classification results depend on both the vibration frequency and its damping ratio. Finally, the feature extractor is extended to quasi real-time void detection and classification, where the processing time duration for a single 0.1-second (50,000-point) percussion signal is 8.28 ms without leveraging advanced computing capabilities of graphics processing units, and the average testing accuracy for the fivefold cross validation is 99.81%. The results demonstrate the potential of the proposed approach for improving the efficiency of inspections.

Material flow visualization during friction stir welding using high-speed X-ray imaging

This study employs high-speed X-ray imaging to capture the process dynamics during FSW in-situ, using a high-intensity X-ray beam to image a 2 mm × 2 mm area at 20,000 frames per second. The friction stir (FS) tool made of H13 tool steel with threads and 3-flats on the probe was used in an aluminum 6061-T6 workpiece. The process parameters employed result in a fully consolidated weldment without any observable sub-surface voids. The density changes captured by the high-intensity X-ray beam show the formation and filling of cavities in the wake of the tool three times per rotation.

Deep learning-based pavement subsurface distress detection via ground penetrating radar data

Pavement subsurface distress endangers driving safety and road serviceability. Ground penetrating radar (GPR) can non-destructively provides high-resolution profiles of road. However, the automatic interpretation of radar signals remains challenging. This study proposed an automatic pavement subsurface distress detection method using traditional signal processing and deep learning. Firstly, a piecewise linear function for radar signal automatic gain was proposed. Wavelet transform was applied to identify road layers for function segmentation. Each signal's power spectral density was calculated to determine the gain function coefficient. A specific pseudo-color mapping method was designed to convert reflected signal for deep learning model training. A radar reflection simulation was built to pre-train the model to enhance model performance. More than 270 km of field test data were collected for model training and validation. The proposed model has shown a 72.39% accuracy in shallow cracks and 68.74% in subsurface voids.

The effect of orientation and pore size on nano mechanical behaviour of Ag thin films: a comparison between experiment and atomistic simulation

ABSTRACT This paper describes the thickness-dependent texture evolution during annealing in Ag thin films, commonly used in micro and nano-devices. The effect of porosity and crystal orientation on mechanical properties for the films deposited by the magnetron sputtering method is particularly addressed using nano-indentation experiment and atomistic simulation. X-ray pole figure analysis revealed that the films exhibited sharp fibre texture with a major <1 1 1> component accompanied by minor twin components of <5 1 1> and <5 7 13> and a weak component of <1 0 0>. The mechanical behaviour of the films was investigated experimentally using a Berkovich tip to measure the hardness (H) and modulus of elasticity (E) at each individual grain and porous region. The average Young’s modulus of strongly oriented [1 1 1] grain at porous and non-porous regions is estimated to be 44 ± 7 and 67 ± 5 GPa. Molecular Dynamics simulations have been performed on (1 1 1), (1 1 0), (0 0 1), (5 1 1), and (5 7 13) surfaces of Ag single crystal using a spherical indenter to investigate the anisotropic nature of hardness and elastic modulus from the Load∼Indentation depth curves. The mechanical properties of [1 1 1] oriented single crystal in the presence of a sub-surface void have been studied. Our results have demonstrated that the void acts as an efficient absorber of dislocation limiting the extension of the plastic zone. The simulated modulus of elasticity (E: 54.2 GPa) results obtained at a temperature of 300 K in the presence of a void are compared with experimentally measured values with a variation of around 6%.

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. The PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

The origin of a complex breccia body within the Upper Cretaceous/Early Eocene succession on Pag Island (Karst Dinarides, Croatia): karstic dissolution and collapse or dilational faulting and collapse origin?

A kilometre west of Pag Town on Pag Island, Croatia, within the Upper Cretaceous shallow-water carbonate succession, there is a large breccia body that has an irregular, quasi-circular shape and a subvertical-oblique position in relation to the bedding of the host rock. The breccia clasts and fragments consist almost entirely of the Upper Cretaceous host rock with only sporadic clasts of the Lower Eocene foraminiferal (alveolinid) limestones. In the brecciated body, there are three breccia types. 1) crackle breccia, 2) mosaic breccia, and 3) chaotic breccia. Based on the textural and structural characteristics of these types of breccia such as chaotic appearance and random fabric, very poorly sorted material, angular fragments, the composition reflecting only the host rock lithology, two genetic scenarios or concepts for the origin of Pag Town breccia body were considered, with observations supporting each of them. The first concept involves host rock dissolution resulting in a widened dissolution cavity into which wall and roof rocks progressively collapsed, and the second concept involves the collapse of voids produced by dilational fault displacement. A common prerequisite to both opposing scenarios is the existence of a subsurface cavity or void where the accumulation of rock clasts and fragments occurred. It is assumed that the timing of the cavity formation is related mainly to karstification during the Late Cretaceous-Early Eocene emersion phase or is related to dilational faulting during the Palaeogene Dinarides thrusting event.

Detection and Classification of Potential Caves on the Flank of Elysium Mons, Mars

Martian caves have revived interest in the field of subsurface exploration because they are the potential destinations for future human habitats and astrobiological research. There are many pits on Mars, but some of them look like collapsed cave roofs. These special pits are formed by the collapse of surface materials into the subsurface void spaces. The signature of life is probable in a subsurface cave on Mars as the subsurface environment can protect life from the harsh and dangerous radiation environment of the surface. In a cave, there may be an abundance of minerals, fluids, and other key resources. Therefore, locating the access point of the subsurface cave is essential and crucial for formulating plans for robotic/human explorations of the Red Planet, Mars. We have used remote sensing data from Mars Reconnaissance Orbiter (MRO; NASA), Mars Global Surveyor (MGS; NASA), and Mars Odyssey (NASA) for identifying, mapping, and classifying selected special pit candidates on the flank of Elysium Mons, Mars. A total of 32 special pit candidates has been identified and classified based upon morphology and geological context. Out of these, 26 are newly discovered ones. The thermal behavior of 23 special pit candidates confirms that the special pits are radiating heat energy at nighttime, similar to potential caves. Also, cave entrances have been detected in nine candidates using data from the HiRISE camera onboard MRO. These sites could be important destinations for future robotic/human exploration and the search for life on Mars.

A novel solar-powered electrochemical mineralization system for persistent remediation of uranium-contaminated groundwater

Reduction of the migratory ability of uranium via reduction, co-precipitation or immobilization is a widely used technology for remediation of uranium contaminated groundwater (UCG). However, the re-released uranium due to environmental alterations such as oxidation, acid dissolution, or microbial decomposition limits the long-term effect of UCG remediation. Here, we developed a novel solar-powered electrochemical mineralization (SPEM) system for persistent remediation of UCG under laboratory conditions. The SPEM system incorporates uranium into magnetite crystal to achieve long-term stability of uranium. The effects of photoelectric conversion, subsurface void fraction, groundwater seepage velocity, and electrode configuration on uranium removal were systematically analyzed. The results showed that the remediation system had excellent adaptability to complex water quality and geological conditions, and could remediate large-area contamination. After 12h of persistent treatment, the system with newly hexagonal two-dimensional electrode configuration (1A6C) reduced uranium concentration by more than 85% in simulated subsurface environment. The mineralized uranium was not re-released within continuous rinsing of treated regions using an acid solution (pH=3.0), for 370h. The developed method solely requires metallic iron as a raw material, which has high and long-term efficiency, is eco-friendly, simple, and widely applicable, thus reliable for the remediation of deep UCG.

Laser-based ultrasound interrogation of surface and sub-surface features in advanced manufacturing materials

Structures formed by advanced manufacturing methods increasingly require nondestructive characterization to enable efficient fabrication and to ensure performance targets are met. This is especially important for aerospace, military, and high precision applications. Surface acoustic waves (SAW) generated by laser-based ultrasound can detect surface and sub-surface defects relevant for a broad range of advanced manufacturing processes, including laser powder bed fusion (LPBF). In particular, an all-optical SAW generation and detection configuration can effectively interrogate laser melt lines. Here we report on scattered acoustic energy from melt lines, voids, and surface features. Sub-surface voids are also characterized using X-ray Computed Tomography (CT). High resolution CT results are presented and compared with SAW measurements. Finite difference simulations inform experimental measurements and analysis.

Non-Contact Paper Thickness and Quality Monitoring Based on Mid-Infrared Optical Coherence Tomography and THz Time Domain Spectroscopy.

In industrial paper production, online monitoring of a range of quality parameters is essential for ensuring that the performance and appearance of the final product is suitable for a given application. In this article, two optical sensing techniques are investigated for non-destructive, non-contact characterization of paper thickness, surface roughness, and production defects. The first technique is optical coherence tomography based on a mid-infrared supercontinuum laser, which can cover thicknesses from ~20–90 μm and provide information about the surface finish. Detection of subsurface voids, cuts, and oil contamination was also demonstrated. The second technique is terahertz time domain spectroscopy, which is used to measure paper thicknesses of up to 443 μm. A proof-of-concept thickness measurement in freely suspended paper was also demonstrated. These demonstrations highlight the added functionality and potential of tomographic optical sensing methods towards industrial non-contact quality monitoring.

Corrosion Behavior of Candidate Functional Materials for Molten Salts Reactors in LiF–NaF–KF Containing Actinide Fluoride Imitators

Molten fluorides of alkali metals are considered a technological medium for molten salt reactors (MSRs). However, these media are known to be extremely corrosive. The successful implementation of high-temperature technological devices using molten alkali metal fluorides requires the selection of such structural materials that have high corrosion resistance in melts with compositional characteristic of MSRs. In this research, the corrosion behavior of 12Cr18Ni10Ti steel, the alloy Ni60Cr20Mo15, and the alloy Monel 404 (Ni50Cu50) was investigated in the LiF–NaF–KF eutectic melt, containing additions of CeF3 and NdF3 from 0 to 5 wt.% as imitator fluorides of actinides in an inert argon atmosphere at 550 °C for 100 h. Gravimetry, energy-dispersive X-ray (EDX) microanalysis of surfaces and cross-section of samples, and ICP-MS were used to establish the corrosion behavior of the investigated alloys. Corrosion resistance of the studied materials was found to decrease in a row from Monel 404 > Hastelloy C2000 > 12Cr18Ni10Ti. The addition of cerium fluoride into the melt resulted in the additional etching of the alloy surface. The addition of neodymium fluoride resulted in the formation of the point/inter-crystalline corrosion damages in the sample bulk. The samples of steel 12Cr18Ni10Ti were subjected to local cracking corrosion. The austenitic nickel-based alloys suffered specific local corrosion with formation of subsurface voids. Excellent corrosion resistance of the Monel alloy under the test conditions was found.

Detecting Subsurface Voids From GPR Images by 3-D Convolutional Neural Network Using 2-D Finite Difference Time Domain Method

In this article, an algorithm for detecting subsurface voids under the road from ground penetrating radar images is proposed. A multichannel radar system mounted on vehicle enables dense and highspeed monitoring. The novelty of the algorithm is a unique ElectroMagnetic simulation method and state-of-the-art deep learning technique to consider three-dimensional (3-D) reflection patterns of voids. To train deep learning models, 3-D reflection patterns were reproduced by 2-D finite difference time domain method to drastically reduce the calculation cost. Hyperboloid reflection patterns of voids were extracted by 3-D convolutional neural network (3D-CNN). The classification accuracy of 3D-CNN was up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$90$</tex-math></inline-formula> %, about 10% improvement compared to previous 2D-CNN to demonstrate the effectiveness of 3-D subsurface sensing and detection. The results were validated by real void measurement data. After applying trained 3D-CNN to radar data, regions of voids were plotted in a 3-D map, offering clear visualization of areas of voids.

Mapping subsurface karsts and voids using directional elastic wave packets

We have developed a new data-driven algorithm that uses directional elastic wave packets as seismic sources to image subsurface voids (i.e., cavities). Compared to a point source, the advantage of the new approach is that the wave packet illuminates only a small volume of the medium around the raypath to significantly reduce multiple scattering effects in the imaging. We take the difference of traces at identical source-receiver offsets from each of two neighboring source packets. The difference mainly contains the void scattering events but not the direct waves, the layer reflections, refractions, nor layer-related multiples. We use P-to-P and P-to-S scattered waves to locate the voids, and the results using scattered P- and S-waves can cross-validate each other to reduce the possibility of false detections. The directional wave packet can be numerically synthesized using existing shot gathers; therefore, no special physical source is required. We determine our method using data calculated using a boundary element method to model the seismic wavefield in an irregularly layered medium containing several empty voids. We test the robustness of our method using the same data but with 15% root-mean-square random noise added. Furthermore, we compare our method with the reverse time migration imaging method using the same data and find that our method provides superior results that are not dependent on the construction of a velocity model.

Deep void detection with 3D full waveform inversion of surface-based and in-depth source seismic wavefields

Detection of subsurface voids using nondestructive seismic methods is an ongoing problem in many areas of civil and environmental engineering (e.g., sinkholes and caves), homeland security (e.g., tunnel detection), and mining applications (e.g., abandoned mines). Recent advances in 3D full waveform inversion (FWI) technology have made it possible to scan large volumes of the underlying materials efficiently, providing a glimpse into the state of subsurface conditions. A challenge in applying 3D FWI methods to the detection of voids emerges from their embedment depths. Shallower voids are easier to detect due to their large signature on the surface seismic response, whereas deeper voids have a much smaller signature and are therefore much harder to detect. This is not a limitation of the FWI method, but rather that of the seismic field-testing techniques and data gathering processes. The goal of this study is to investigate ways to overcome these limitations and improve void detection depths. One way to achieve this is through the application of a large surface source, generating more energy at lower frequencies (longer wavelengths), thereby increasing the penetration depth. Another way is by increasing the contribution of body waves and utilizing the diffraction/transmission information embedded in the waveforms. The latter is achieved through the application of a recently developed SPT-seismic method, where the standard penetration test (SPT) device is used to generate wave motion from within the subsurface. Both source methods and a newly developed 3D Gauss-Newton FWI method are utilized here to detect a deep void (25–45 m depth) in limestone, on the southern peninsula of Florida. The results are compared with SPT and Sonar profiles obtained from the test site. Overall, a good image of the deep void is achieved, matching observations from the invasive results. The findings provide useful insight into the application of FWI technology for detecting deep subsurface voids and anomalies that are typically hard to identify.

Influence of Tool Runout on Force Measurement During Internal Void Monitoring for Friction Stir Welding of 6061-T6 Aluminum

Abstract The goal of this research was to examine how altering the amount of friction stir tool eccentricity while controlling the amount of slant in the tool shoulder (drivers of oscillatory process forces) effects the generation of process force transients during sub-surface void interaction. The knowledge gained will help improve the accuracy of force-based void monitoring methods that have the potential to reduce the need for post-weld inspection. Process force transients during sub-surface void formation were examined for multiple tools with varying magnitudes of kinematic runout. The eccentric motion of the tool produced oscillations in the process forces at the tools rotational frequency that became distorted when features (flats) on the tool probe interacted with voided volumes, generating an amplitude in the force signals at three times the tool rotational frequency (for three-flat tools). A larger tool eccentricity generates a larger amplitude in the force signals at the tool’s rotational frequency that holds a larger potential to create a distortion during void interaction. It was determined that once void becomes large enough to produce an interaction that generates an amplitude at the third harmonic larger than 30% of the amplitude at the rotational frequency in a weld with no interaction (amplitude solely at rotational frequency), the trailing edge of the tool shoulder cannot fully consolidate the void, i.e., it will remain in the final weld. Additionally, once the void exceeds a certain size, the amplitudes of the third harmonics saturate at 70% of the amplitude at the rotational frequency during full consolidation. The interaction between the eccentric probe and sub-surface void was isolated by ensuring any geometric imperfection in the shoulder (slant) with respect to the rotational axis was removed. The results suggest that geometric imperfections (eccentricity and slant) with respect to the tool’s rotational axis must be known when developing a void monitoring method from force transients of this nature.

Femtosecond laser-induced nanoporous layer for enhanced osteogenesis of titanium implants

The osteogenic activity of medical metal can be improved by lowering its surface stiffness and elastic modulus. However, it is very difficult to directly reduce the elastic modulus of medical metal surfaces. In this paper, with selected parameters, the titanium surface was treated via femtosecond laser irradiation. Micro indentation revealed that the femtosecond laser ablation can effectively reduce the surface Young's modulus and Vickers hardness of titanium. Besides, In order to explain the mechanical properties of degradation of titanium surface, Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) was used to simulate the process of laser ablation process of titanium surface, and it was found that after the ablation of titanium surface, voids were produced in the subsurface layer. The simulation showed that the voids are formed by the cavitation of metastable liquid induced by high tensile stress and high temperature during femtosecond laser irradiation. Subsurface voids with a thickness of about 40nm were observed under the oxide layer in the experiment. Cell experiments showed that the surface with low Young's modulus was more conducive to cell proliferation and osteogenic differentiation.

Mechanistic Mathematical Modelling of Pothole Development from Loss of Roadway Subsurface-Materials

In this paper, we developed a mechanistic mathematical model. It implies the engineering problem of pothole development on roadways. The issue involves internal erosion, a decline of subsurface materials, voids creation, depression, materials damage, and potholes’ appearance on the road’s surface. Our study aims to predict why, how, and when pothole develops from the loss of roadway subsurface materials. We reviewed many sources as our first method. It involved using and adapting the guiding principles for migrating particles upwards. We then changed specific parameters, formulated our model equation, solved it using the separation of variables, and then verified it. Observations from our review show that high traffic load pressure and water must be present on the road for particle migration to occur. They generate excess pore-water pressure that enables the movement of particles upwards. Particle relocation causes voids and dislocation of materials. Results show that an increase in time, cracks, soil erosion coefficient, and a decrease in the roadway’s height led to a rise in the number of materials lost from the pavement. Our study is relevant because it will better inform road managers and modelers on potholes, and they can-do preventive measures to avert total road failure.