Subsurface Analysis Research Articles

Seasonal Mixed Layer Temperature in the Congolese Upwelling System

AbstractThe Congolese upwelling system (CoUS), located along the West African coast north of the Congo River, is one of the most productive and least studied systems in the Gulf of Guinea. The minimum sea surface temperature in the CoUS occurs in austral winter, when the winds are weak and not particularly favorable to coastal upwelling. Here, for the first time, we use a high‐resolution regional ocean model to identify the key atmospheric and oceanic processes that control the seasonal evolution of the mixed layer temperature in a 1°‐wide coastal band from 6°S to 4°S. The model is in good agreement with observations on seasonal timescales, and in particular, it realistically reproduces the signature of the surface upwelling during the austral winter, the shallow mixed layer due to salinity stratification, and the signature of coastal wave propagation. The analysis of the mixed layer heat budget for the year 2016 reveals a competition between warming by air‐sea fluxes, dominated by the incoming shortwave radiation throughout the year, and cooling by vertical mixing at the base of the mixed layer, as other tendency terms remain weak. The seasonal cooling is induced by vertical mixing, where local wind‐driven dynamics play a secondary role compared to subsurface processes. A subsurface analysis shows that remotely forced coastal‐trapped waves raise the thermocline from April to August, which strengthens the vertical temperature gradient at the base of the mixed layer and leads to the mixing‐induced seasonal cooling in the Congolese upwelling system.

Comprehensive seismic refraction analysis and 2-D geological modeling in Ini Local Government Area, Akwa Ibom State, Nigeria

Seismic refraction methods are vital in geophysical investigations to reveal subsurface geological structures. This study focuses on the Ini Local Government Area, Akwa Ibom State, Nigeria, analyzing geological features beneath the surface, with emphasis on seismic velocities and layer thicknesses. The primary aim was to determine subsurface composition, identify geological disruptions, and develop 2-D subsurface models using seismic refraction data. Data were collected from forty locations using a Geometric ES 3000 12-Channel Seismograph, a Vulcan Sledge Hammer as the energy source, and geophones as detectors. The data were processed into Two-Way Time (TWT) graphs, and models were generated using strata modeling software. Three primary layers were identified: Layer 1 (topsoil) with lower seismic velocities (209-500m/s) and shallow depths (1-3m), Layer 2 (consolidated subsoil) with higher velocities (221-1210m/s) and greater depths (4-9m), and Layer 3 (bedrock) with the highest velocities (510-1700m/s) and depths (4.5-14.5m). Variations in seismic velocities and thicknesses highlighted geological instabilities. The VP/VS and Poisson’s ratios provided insights into lithological variations. 2-D sections showed lateral variations and models revealed disruptions like faults. The study delineated three seismic layers, highlighting areas with suitable and unsuitable conditions for construction. This research’s novelty lies in integrating seismic refraction data with advanced 2-D modeling, offering a comprehensive subsurface analysis crucial for construction planning and geological hazard assessment in Ini Local Government Area (LGA).

Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder

Thermal wave imaging uses the temporal temperature distribution over the object’s surface for subsurface analysis. However, the noise generated during experimentation corrupts this temporal history and hampers the detection of defect signatures. As denoising of the temporal thermal history enhances the defect detectability, this study offers a Stacked Denoising Convolution Autoencoder (SDCAE) in frequency-modulated thermal wave imaging with one-dimensional convolution layers to reduce noise in temporal thermal evolution and train high-level features resulting in improved defect signs. Experimental results on mild steel and carbon fiber reinforced polymer specimens with different sizes of defects at various depths demonstrate that integrating temporal denoising and deep feature learning techniques into a single novel framework significantly improved defect detectability. In addition, defect signal-to-noise ratios of the denoised thermal data and latent space of the proposed model compared to conventional autoencoder and dimensionality reduction techniques recommend the superiority of the proposed method.

Seismic and outcrop-based 3D characterization of fault damage zones in sandstones, Rio do Peixe Basin, Brazil

Fault damage zones, composed of sub-seismic deformation structures, are difficult to detect using seismic data. Still, they can be related to fault throw, which is widely measured in the subsurface. This research employs a multiscale approach that integrates outcrop studies with seismic reflection data to investigate the attributes of fault damage zones affecting porous sandstones. We provide an in-depth understanding of the 3D fault zone volume, investigating correlations between geometric attributes of faults (width of damage zone, frequency of subsidiaries structures, and fault termination) in the outcrop and the fault throw in the subsurface, with insights from deformation mechanisms within the fault zone. The methods encompass 1D scanlines to constrain the damage zone width on the outcrop. At the same time, the subsurface analysis uses 3D seismic data, seismic attributes, and deep learning neural network (DNN) fault volumes to interpret fault geometries and quantify fault throws at different depths. Results show that the area presents a complex fault zone with multiple fault sets, deformation bands, and fracture corridors trending mainly NE-SW, NW-SE, and E-W. The integration of surface and subsurface fault data enabled the identification of two portions in each fault set outcropping at the fault tip for E-W/ESE-WNW-striking faults and the central part of the fault for NE-SW-striking faults. Faults with the greatest length do not outcrop with the largest damage zone width since they are outcropping the tip of the fault. The parallel faults overlap their damage zones, increasing the deformation zones in the affected sandstones. Fault throw and damage zone width present a positive correlation. This relation is affected by fault segments and subsidiary faults.

Photothermal AFM-IR Depth Sensitivity: An Original Pathway to Tomographic Reconstruction.

Photothermal atomic force microscopy-infrared (AFM-IR) enables label-free chemical imaging and spectroscopy with nanometer-scale spatial resolution through the integration of atomic force microscopy (AFM) and infrared radiation. The capability for subsurface and three-dimensional (3D) tomographic material analysis remains, however, largely unexplored. Here, we establish a simple and robust empirical relationship between the probing depth and laser repetition rate for three important modes of AFM-IR operation: resonance-enhanced, tapping, and surface-sensitive AFM-IR. Using this empirical relationship, we demonstrate, based on the example of resonance-enhanced operation, how photothermal AFM-IR of thin surface/subsurface layers of polystyrene domains in the poly(methyl methacrylate) matrix can result in 3D representations revealing the size and thickness of small polystyrene domains in the poly(methyl methacrylate) matrix with nanometer-scale resolution. Experimental findings are confirmed by analytical models.

Short-cycle borehole thermal energy storage: Impact of thermal cycle duration on overall performance

Borehole thermal energy storage arrays are a common form of seasonal underground energy storage which relies on the thermal mass of subsurface rock or sediment to store hot or cold thermal energy. However in practice, typical seasonal operation seldom leads to cyclic energy recovery factors greater than 50%–60% due to significant thermal losses during prolonged periods between consecutive charges. In this paper, the impact of short cycle operation (down to a few days) on energy recovery and overall performance is investigated. Analytical and numerical models are used to estimate energy recovery as a function of cycle length for a typical array and for two distinct charging and discharging scenarios (constant temperature or thermal charge load), which is novel and has not been done before. The results show that shorter cycle duration yields increased recovery factors and storage capacity, which is ascribed to a greater rate of charge to rate of thermal loss ratio. In particular, short-cycle operation at the scale of days unlocks recovery factors in excess of 85 %. The results, supported by a numerical subsurface analysis of the temperature distribution, also show that the recovery efficiency increases with time as losses approach, but never fully reach, a steady-state.

Production perspectives of a high-status polychrome jewellery set from the Hunnic period (mid-5th century AD) Carpathian Basin

Fifty years after the discovery of the Regöly grave, the emblematic Hunnic period archaeological assemblage from Hungary, an extensive scientific investigation was performed on the polychrome dress accessories of the high-status woman, often referred to as the “Princess of Regöly”, buried there. The multi-instrumental analyses aimed to characterise material and technological aspects of the gemstone-inlaid artefacts (a set of a pair of bow-brooches and a belt buckle), i.e., the manufacturing and decorative techniques as well as the chemical and mineralogical compositions. The non-destructive and non-invasive gemmological and geochemical analyses applied optical microscopes, handheld XRF, SEM-EDX and Raman microspectrometer. This study provides the first comprehensive examination of the Hunnic period polychrome jewellery, and highlights the potential of combining surface and subsurface analyses to specify garnet provenances. It presents the first evidence of use of antique and early medieval garnet sources during the early Migration period. The findings enhance understanding of the garnet supply chain and trade network, the production organisation and potential workshop connections. Significant differences of the brooches and the buckle reveal varying levels of luxury among the elite, providing insight into the social context of the polychrome jewellery associated with the “Princess of Regöly”.

Full‐field effect of flow‐induced fiber orientation during compression molding of carbon fiber sheet molding compounds

AbstractAn image‐based fiber orientation analysis technique has been employed to study the effect of initial charge coverage and preferential fiber alignment on the flow‐induced fiber orientations in carbon fiber sheet molding compounds (SMCs). The initial charge coverage area exhibited minimal fiber reorientation while significant fiber alignment in the flow direction was observed in the regions of greatest flow. Novel subsurface analysis using the same full‐field orientation analysis technique also showed the presence of a skin‐core structure with greater flow‐induced fiber alignment at the mold surfaces, while the core retained more of the initial fiber orientation distribution. Edge effects were also revealed by this analysis at the mold walls. Tensile testing from two orthogonal directions showed that panels molded from lower charge coverage resulted in better mechanical performance and lower anisotropy. Moreover, by encouraging charge flow in the preform's preferential alignment direction, high tensile stiffness and strength in the preferred direction were achieved (56.7 GPa and 238.9 MPa, respectively) at 30% fiber volume fraction. This provides a practical demonstration of the value in controlling fiber orientation, with respect to charge positioning and mold flow, to maximize and tailor the composite performance of SMCs typically used for automotive applications.Highlights Low‐cost scanning technique is extended to assess internal fiber orientations Full‐field orientations are measured before and after molding Investigation of how charge size and shape can affect orientations Combining effects of flow and initial orientation to tailor SMC performance Circular statistics provides a thorough analysis of orientation distributions

Automated Borehole Image Interpretation Using Computer Vision and Deep Learning

Summary Reservoir characterization is critical for successful oil and gas exploration, heavily reliant on detailed formation analysis from formation microimager (FMI) logs. However, interpreting these complex logs is time-consuming, subjective, and requires expert-level knowledge. This study addresses this challenge by proposing a novel approach that integrates computer vision (CV) and deep learning (DL) for automated and real-time interpretation of FMI logs. Our methodology leverages CV and DL techniques for automated feature extraction and classification from FMI data. A comprehensive training data set encompassing more than 2,500 images is utilized to train the DL model, enabling the identification of more than 50 distinct geological features, including bedding planes, fractures, and mineral variations. In addition, the study explores the creation of local stress maps from drilling-induced fractures to determine the present-day maximum horizontal stress (SHmax) orientation, crucial for optimizing wellbore stability and hydraulic fracturing strategies. This research presents a groundbreaking advancement in reservoir characterization through the synergy of automated FMI interpretation, CV, and DL. The developed model exhibits exceptional accuracy in geological feature classification, significantly surpassing traditional, human-centric interpretations. For instance, the model achieves a remarkable 92% accuracy in classifying ore than 50 geological features, demonstrably outperforming conventional methods. Furthermore, the developed model was applied to actual field cases to predict the stress field. The model was able to accurately predict the minimum horizontal stress (Shmin) and SHmax based on FMI logs, and the results were used to refine the geomechanical modeling and optimize hydraulic fracture orientation for enhanced hydrocarbon recovery. This work establishes a new benchmark for applying artificial intelligence in subsurface analysis, paving the way for future advancements in reservoir management and geomechanics. The implications are far-reaching, offering greater precision in geological interpretations, improved decision-making for production strategies, and ultimately, a more sustainable approach to hydrocarbon extraction. By automating tedious and subjective tasks, this approach not only reduces reliance on experts but also frees up valuable time for more strategic tasks. The ability to extract critical geological information with such accuracy from complex FMI logs translates to significant improvements in reservoir characterization and production efficiency.

Alpha-case promotes fatigue cracks initiation from the surface in heat treated Ti-6Al-4V fabricated by Laser Powder Bed Fusion

This research investigates the effect of the formation of an oxygen-stabilised titanium alpha layer – called alpha-case at the surface – on the fatigue properties of Ti-6Al-4V (Ti64) alloy components produced by Laser Powder Bed Fusion (L-PBF). Three post processing heat treatments with different controlled atmospheres were carried out on samples with as-built surfaces to evaluate how differences in alpha-case layer thickness and hardness affect the material’s susceptibility to surface embrittlement and its overall fatigue performance. The investigation includes bulk and subsurface microstructural analysis, surface characterisation by X-ray computed tomography (XCT), and fatigue testing. Key findings show that alpha-case layers can reduce the fatigue resistance of L-PBF fabricated Ti64. The presence of a 70±3 µm thick alpha-case layer was found to promote crack initiation. This is emphasised by a higher density of initiated cracks, thus leading to a reduction in fatigue life. Conversely, thinner alpha-case layers were found to have a reduced impact on the fatigue performance, highlighting the critical role of post processing heat treatments in modulating the fatigue resistance of the material. The use of XCT to characterise the surfaces of the specimens in 3D confirms that fatigue cracks primarily initiate at surface notches, highlighting the predominance of as-built surfaces over microstructure in determining the fatigue resistance of L-PBF Ti64 components.

Subsurface Analysis of Sibayak Mountain Karo Regency Using Geomagnetic Method with 2D and 3D Modeling

Subsurface Analysis of Sibayak Mountain Karo Regency Using Geomagnetic Method with 2D and 3D Modeling

Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF

In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.

Insights into Afikpo Synclinorium structures: Subsurface analysis and intrusion outlining from airborne magnetic data

The airborne magnetic data over Afikpo Synclinorium were used to highlight subsurface structures and establish the orientations of tectonic features and their influence on the hydrocarbon play of the area. The characteristic trends of the lineaments were achieved through the center for exploration targeting of grid data analysis. The results of the TMI, residual magnetic field, first vertical, horizontal derivative, Analytic signal, and Tilt angle derivative classified the area into regions of high magnetic intensity observed around the northeastern, southwestern, and central portions of the study area. These areas characterized by anomalous signatures of short magnetic wavelengths delineate shallow magnetic sources. Conversely, regions with medium to low magnetic intensities are characterized by long magnetic wavelengths which indicates deep-seated magnetic source(s) and prospect for hydrocarbon (if other play concepts are emplace); due the thick sediment cover at the southern portion of the area (around, southern part of Nguzu, Ohafia, Biakpan, Abriba and Bende areas). The sediment thickness decreases towards north, west, North-east and north-west (dominated by Benue Trough deposit). Depth to the anomaly sources is of two categories, namely, shallow <1.0 km (dominated by NE trending structures) and deep ≥2.5 km. Ground-truthing confirmed the anomalous zone of high magnetic sources in the northeastern and central regions as Tertiary related tectonic-magmatic intrusion. The most prominent intrusion cuts across Amangwu Edda in Afikpo South through Amaeta-Oziza to Cross River State in a fissure form. This intrusion occurrence conformed to the high magnetic signature identified as a dolerite sill within the Afikpo Sub-basin. It stretches over 16 km in length with an average width of about 1.2 km, having its widest part in the northern parts of Mata Hospital Afikpo. The subsurface topographic model indicated that no fewer than 50% of the study area is characterized by sporadically distributed intrusive rocks. The result revealed several structural lineaments with high lineament density at the northcentral corner of the area trending NE-SW with fault lines (slip fault) perpendicular to it, offsetting the Akpoha, Ibii, Amasiri. and Ozara-ukwu ridges. The major drainage systems across the area followed major structural trends perpendicular to the identified major lineaments, passing through the major regional structural displacement pattern of the Basin. The emplacement of these intrusions might have impacted the hydrocarbon potential of the basin.

Logarithmic Frequency Modulated Thermal Wave Imaging for Subsurface Analysis

Logarithmic Frequency Modulated Thermal Wave Imaging for Subsurface Analysis

An unsupervised machine learning algorithm approach using K-Means Clustering for optimizing Surface Wave Filtering in seismic reflection data

Surface waves often cause significant noise in seismic data, complicating the interpretation of subsurface structures. Traditional filtering methods, such as FK filtering, usually struggle with non-stationary noise and require extensive manual parameter tuning. This study explores the effectiveness of using K-means clustering, incorporating attributes such as amplitude, frequency, and phase to filter surface waves from seismic data. Synthetic seismic data were first generated to test the proposed method, ensuring its robustness before application to real field data. Attributes were extracted from each seismic trace, including instantaneous amplitude, frequency, and phase. These attributes were used as input parameters for the K-means clustering algorithm. The identified clusters corresponding to surface waves were then used to filter these waves from the seismic data. The K-Means clustering effectively differentiated surface waves from reflected waves in both synthetic and real seismic datasets. The method demonstrated that by including phase as an attribute, alongside amplitude and frequency, the accuracy of surface wave detection and filtering significantly improved. The synthetic data showed a clear separation of wave types, validating the method. When applied to real field data, the approach consistently removed surface waves, clarity of seismic reflections crucial for subsurface analysis.

Morphology Characterization and Refractive Index Analysis of Subsurface Ultrashort‐Pulsed Laser Modifications in ZnS

A parameter study of ultrashort pulse laser‐induced modifications in the bulk of ZnS crystals is reported. Experimental results on these modifications and their dependence on the pulse energy, writing speed, and depth are presented, with an emphasis on cross‐sectional morphology and induced refractive index changes. Localized permanent material modifications have been inscribed in the bulk of the crystal using a laser with a center emission wavelength of 2.09 μm and a pulse duration of ≈4 ps. The morphology strongly depends on the laser and optical focusing parameters, in particular, on the pulse energy and processing speed, with a significant shift in depth dependence for short pulse‐to‐pulse separations. Depending on the applied pulse energy, distortions of the lateral profile of the refractive index changes appear in the form of oscillatory features in the transverse plane relative to the inducing laser beam. The true extent of the modified material is revealed by the alternating lateral profile with largest induced negative refractive index change, Δn, of −3.88 × 10−2 ± 0.18. Such parametric insight is of critical importance for the understanding and optimization of the fabrication process and for realizing compact 3D photonic devices in the bulk of materials in a reproducible manner.

Is the impact of groundwater on lake greenhouse gas dynamics underestimated? A comparative analysis of subsurface and ecological factors

Lakes are recognized as important sources of greenhouse gases (GHGs) emissions to the atmosphere, influenced by various ecological processes. Groundwater discharge into lakes, despite its small volume, has high concentrations of dissolved carbon and nitrogen that significantly affect the production and emission of GHGs from lakes. Therefore, a comprehensive investigation of the mechanisms behind lake GHGs emissions under the influence of groundwater discharge and ecological processes, holds substantial scientific importance. However, due to uncertainties and quantification challenges associated with groundwater discharge, related research is currently limited. Here, we conducted year-round field observations on Honghu Lake, a large shallow eutrophic lake in Hubei Province, China. We analyzed the seasonal variation of GHGs emissions and quantified the groundwater discharge flux using radon (222Rn). The fluxes of methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) at the water–air interface were estimated to be 31.1 ± 4.88 mg m−2 d−1, 386 ± 90.7 mg m−2 d−1, and 0.327 ± 0.072 mg m−2 d−1, respectively. CH4 is the primary greenhouse gas in Honghu Lake, contributing to 82.2 % and 62.2 % of the lake’s total emissions over 20-year and 100-year frames. The average rate of groundwater discharge was 8.19 ± 0.471 mm d−1, with the highest discharge rate in winter and the lowest in spring. The daily groundwater discharge volume accounts for 0.649 % of the lake’s total water volume. The daily contributions of groundwater discharge to the lake’s total emissions of CH4, CO2, and N2O were 0.318 %, 12.1 %, and 2.59 %, respectively. The facilitative role of groundwater discharge in CO2 emissions primarily manifests through the transport of dissolved organic carbon (DOC) and high concentrations of CO2 into the lake. Meanwhile, CH4 emissions depend on the activity of methanogenic bacteria, substrate availability, and anaerobic conditions, whereas N2O emissions are influenced by temperature and nutrient levels. Our study reveals that in the short term, the effect of groundwater on lakes is relatively minimal. Yet, its long-term role as a steady supplier of carbon and nutrients to lakes should not be overlooked. This study reveals the ways in which groundwater discharge and internal lake dynamics collectively fuel GHGs emissions. Our findings offer a new perspective and critical theoretical support for the assessment of lake greenhouse gas emissions.

Insights into in situ surface reconstruction in cobalt perovskite oxides for enhanced catalytic activity

An depth understanding of the fundamental interactions between surface termination and catalytic activity is crucial to prompt the properties of functional perovskite materials. The elastic energy due to size mismatch and electrostatic attraction of the charged Sr dopant by positively charged oxygen vacancies induced inert A-site surface enrichment rearrangement for perovskites. Lower temperatures could reduce A-site enrichment, but it is difficult to form perovskite crystals. La0.8Sr0.2CoO3-δ (LSCO) as a model perovskite oxide was modified with additive urea to reduce the crystallization temperature, and suppress Sr segregation. The LSCO catalysts with 600 °C annealing temperature (LSCO-600) exhibited a 19.4-fold reaction reactivity of toluene oxidation than that with 800 °C annealing temperature (LSCO-800). Combined surface-sensitive and depth-resolved techniques for surface and sub-surface analysis, surface Sr enrichment was effectively suppressed due to decreased oxygen vacancy concentration and smaller electrostatic driving force. DFT calculations and in-situ DRIFTs spectra well revealed that tuning the surface composition/termination affected the intrinsic reactivity. The catalyst surface with lower Sr enrichment could easily adsorb toluene, cleave, and decompose benzene rings, thus contributing to toluene degradation to CO2. This work demonstrates a green and efficient way to control surface composition and termination at the atomic scale for higher catalytic activity.

Brittle-Ductile Threshold in Lithium Disilicate under Sharp Sliding Contact.

Computer-aided design (CAD)/computer-aided manufacturing (CAM) milling and handpiece grinding are critical procedures in the fabrication and adjustment of ceramic dental restorations. However, due to the formation of microfractures, these procedures are detrimental to the strength of ceramics. This study analyzes the damage associated with current brittle-regime grinding and presents a potential remedy in the application of a safer yet still efficient grinding regime known as "ductile-regime grinding." Disc-shaped specimens of a lithium disilicate glass-ceramic material (IPS e.max CAD) were obtained by cutting and crystallizing the lithium metasilicate CAD/CAM blanks (the so-called blue blocks) following the manufacturer's instructions. The discs were then polished to a 1 µm diamond suspension finish. Single-particle micro-scratch tests (n = 10) with a conical diamond indenter were conducted to reproduce basic modes of deformation and fracture. Key parameters such as coefficient of friction and penetration depth were recorded as a function of scratch load. Further, biaxial flexure strength tests (n = 6) were performed after applying various scratch loads to analyze their effects on ceramic strength. Scanning electron microscopy (SEM) and focused ion beam (FIB) were used to characterize surface and subsurface damage. Statistical analysis was performed using one-way analysis of variance and Tukey tests. While the SEM surface analysis of scratch tracks revealed the occurrence of both ductile and brittle removal modes, it failed to accurately determine the threshold load for the brittle-ductile transition. The threshold load for brittle-ductile transition was determined to be 70 mN based on FIB subsurface damage analyses in conjunction with strength degradation studies. Below 70 mN, the specimens exhibited neither strength degradation nor the formation of subsurface cracks. Determination of the brittle-ductile thresholds is significant because it sets a foundation for future research on the feasibility of implementing ductile-regime milling/grinding protocols for fabricating damage-free ceramic dental restorations.

Relating rolling contact fatigue (RCF) life to the microstructure evolution in aerospace grade bearing steels: A comparison of Cronidur-30 with AISI 440C

This article presents an extensive analysis of the microstructural evolution under various heat treatment conditions, mechanical properties (hardness), and rolling contact fatigue (RCF) life assessment and sub-surface crack analysis are studied on high nitrogen bearing steel Cronidur-30(Cr-30). An optimum solutionising temperature range has been identified which ensures desirable hardness as well as low retained austenite content. The microstructure consists of primary M2 (C, N) and secondary M23C6 carbides. Sizes of carbides in Cr-30 are significantly smaller than in AISI 440C and this plays a critical role in enhancing the RCF life of Cr-30. A larger area fraction of fine carbides results in branches and subsequent sub-branches of cracks in Cr-30 steel. However, the AISI 440C sample exhibits extensively interconnected cracks due to the presence of larger carbides. The tempering response of Cronidur-30 exhibited a peak hardness of 61 HRC at both lower (150 °C) and higher (475 °C) temperatures. Higher temperature tempering showed L1 life improvement of over 0.5 fold compared to lower temperature tempering.