Search Results (1,829)

To study the fracture performance of the concrete-epoxy mortar interface (CEMI) pre-coated with epoxy solutions with different concentrations, a total of nine specimens were fabricated to be subjected to four-point bending tests. DIC technology was used to monitor the deformation of the pure bending region of specimens. A triple-fold stiffness model was developed based on the test results of applied load–displacement curves. A generalized method for determining the parameters of the bilinear softening model was proposed and validated by the test results. Additionally, the fracture performance and crack extension of CEMI specimens were deeply analyzed using the double-K fracture criterion. The fracture initiation toughness K_ICⁱⁿⁱ was calculated by introducing the cohesive fracture toughness, and the crack extension resistance K_R curves of the CEMI specimens were calculated by combining the linear-elastic fracture mechanics and the proposed bilinear softening model. It was indicated that the initiation locations and extension paths of interfacial cracks could be effectively identified by the DIC technique, with an error of less than 8% between test results and predictions. The bridging effect was strengthened by pre-coating with an epoxy solution of the CEMI specimens by filling the microscopic defects on the concrete surface, thereby improving K_ICⁱⁿⁱ, delaying unstable crack extension, and enhancing interfacial fracture resistance. Full article

Heat-treatable aluminum alloys are widely used in aerospace and automotive industries for high-performance structural applications. However, their processing through conventional fusion-based additive manufacturing is limited by solidification-related defects, such as hot cracking, porosity, and elemental segregation. To overcome these limitations, friction-based additive manufacturing (FBAM) has emerged as a promising solid-state alternative. FBAM primarily includes friction stir additive manufacturing (FSAM), additive friction stir deposition (AFSD), friction screw extrusion additive manufacturing (FSEAM), and friction rolling additive manufacturing (FRAM), which differ in feedstock form and process configuration. In these processes, feed material is consolidated through frictional heat generated below the melting temperature, enabling the formation of refined equiaxed microstructures while minimizing solidification defects. Despite these advantages, significant challenges persist in processing heat-treatable aluminum alloys, particularly the 2xxx, 6xxx, and 7xxx series. These include non-uniform microstructure and mechanical properties along the build direction; precipitation instability; process-induced defects, such as tunnel formation; and mechanical properties that are often inferior to those of the corresponding base materials (BMs). Reported FBAM builds generally exhibit equiaxed ultrafine grains below 1 μm; however, the strength and microhardness of heat-treated alloy builds commonly remain around 70–75% of the corresponding BM. Following post-heat treatment, microhardness can be nearly fully recovered, whereas UTS typically reaches about 80–85% of BMs, often with an associated ductility reduction of nearly 50%. This review critically analyzes research reported over the past decade on FBAM processing of heat-treatable aluminum alloys, covering FSAM, AFSD, FSEAM, and FRAM. The key challenges related to microstructural evolution and mechanical performance are systematically discussed for each alloy series. Furthermore, mitigation strategies proposed in the literature, including process parameter optimization, in-process cooling, post-heat treatment, and nanoparticle reinforcement (e.g., SiC, TiC, Ni and ZrO₂), are evaluated. Finally, existing research gaps are identified, and future directions are proposed to support the development of robust, scalable, and high-performance FBAM processes for heat-treatable aluminum alloys. Full article

The interlayer pores of 3D-printed concrete (3DPC) significantly weaken its macroscopic mechanical properties. In this study, the phase-field cohesive zone model (PF-CZM) is employed as a numerical tool to systematically investigate the weakening mechanisms and crack evolution behavior associated with pore characteristics, including pore size, morphology, spatial orientation, and arrangement, through single-factor numerical simulations with different pore numbers. The results demonstrate that the degradation induced by a single pore is controlled by its effective projection length in the direction perpendicular to the principal tensile stress, with horizontal flat pores being the most detrimental under the same porosity. In the multi-pore system, the connection angle between pores, rather than their spacing, is the key factor determining structural degradation, and a horizontal collinear arrangement is prone to triggering brittle fracture. Furthermore, locally aggregated small pores can form combined defects, whose strength-weakening effect surpasses that of isolated large pores, thereby triggering crack path competition and leading to asymmetrical structural failure. This study reveals the fracture mechanisms driven by complex pore configurations and provides a reference for strength prediction of 3DPC. Full article

Due to climate change, corrosive conditions, and hydrogen-rich environments, steel energy pipelines inevitably develop a variety of defects. These deficiencies compromise pipeline safety and reliability, and neglecting them may result in pipeline leaks, fractures, and even potentially catastrophic explosions. Although a considerable body of literature reviews the effects of metal-loss defects like corrosion and cracks on pipeline safety and reliability, the impact of geometric deformation, like dents, lacks a comprehensive review. This work employs a hybrid systematic literature review (SLR) and bibliometric analysis (BA) to investigate the current research status of pipeline dent assessment. Four questions are answered: (1) What are the publication distribution characteristics, active journals, production organizations, and production authors related to research on pipeline dents? (2) What criteria have been employed for evaluating the pipeline dent? (3) From what perspective has the integrity of dented pipelines been assessed, and what research approaches have been used? (4) What are the future challenges and prospects of pipeline dent studies? The findings demonstrate that depth-, strain-, and damage-based evaluation criteria are widely employed to assess pipeline dents, each with merits and limitations. Despite the simplicity and ease of use of depth- and strain-based criteria, they are prone to underestimation flaws. In contrast, damage-based criteria, which consider multiple factors, are limited by their complexity and high computational resource requirements. The reliability of dented pipelines is predicted with remaining strength, fatigue life, and failure pressure using theoretical modeling, experimental testing, numerical simulation, or a combination of these methods. Future dent studies should involve refining numerical models, full-scale testing under varied loading conditions, and integrating advanced sensing techniques for real-time inspection. Full article

This study examines the nature of enzymatic degradation of polyethylene terephthalate (PET) films mediated by a novel recombinant LCC^ICCG PETase enzyme preparation based on P. verruculosum fungus. The investigation was conducted using amorphous PET samples and PET samples with varying degrees of crystallinity as substrates for PETase-catalyzed hydrolysis under different temperature and pH conditions. Mechanical testing revealed that enzymatic treatment reduced the yield stress by 20–25%, tensile strength by approximately twofold, and elongation at break by 5–10 times, while the deformation mechanism remained unchanged. Enzymatic degradation under acidic conditions was ineffective, whereas increasing the pH to 9–10 markedly accelerated PET degradation and the associated deterioration of mechanical properties. Thermal analysis (TGA, DSC) and microscopy (optical and scanning electron microscopy) demonstrated that degradation was localized at the polymer surface, leading to the formation of cavities, cracks, and submicron-sized pores rather than bulk material disintegration. An inverse correlation was observed between PET crystallinity and susceptibility to enzymatic degradation: samples with crystallinity below 13% could be almost completely degraded, whereas samples with crystallinity above 30% exhibited little or no measurable weight loss over the same period. Low-crystallinity PET underwent rapid degradation accompanied by a transient increase in crystallinity, while highly crystalline PET primarily accumulated surface defects that nevertheless caused a substantial loss of mechanical strength. Consequently, the experimental data obtained in this study provide useful information for understanding PET degradation and for future studies on enzymatic PET recycling. The systematization of feedstock characteristics and the elucidated patterns of enzymatic degradation will enable optimization of pretreatment, enzymatic hydrolysis, and monomer recovery process parameters, thereby facilitating the eventual production of secondary raw materials. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article

GH4141 + 0.2 wt.% Y₂O₃ superalloy was fabricated using laser powder bed fusion (LPBF) technology and subjected to solution and ageing heat treatments. The effects of laser power (1100, 1300, 1500 W) on the microstructure and mechanical properties of the ODS nickel-based superalloy were investigated. The results indicate that as the laser power increased from 1100 W to 1300 W, defects such as cracks and pores in the specimens decreased, the grains were refined, and the microstructure became more uniform; when the laser power was further increased to 1500 W, the grain size coarsened significantly, precipitation phases at the grain boundaries became coarser or locally aggregated, and crack sensitivity increased. EDS analysis revealed enrichment of C, Cr, Mo and Ti in the dark phases at the grain boundaries, which may be associated with MC-type and M₂₃C₆-type carbides; no significant agglomeration of Y₂O₃ particles was observed in the matrix. Room-temperature tensile properties exhibited a pattern of initially increasing and then decreasing with increasing laser power. The tensile strength and elongation after fracture of the specimens were relatively similar under 1100 W and 1500 W conditions, whilst the specimen tested at 1300 W achieved the optimal balance of strength and toughness, with a tensile strength of 1460 MPa and an elongation after fracture of 14.3%, representing increases of approximately 9.8% and 54% compared to the 1100 W and 1500 W conditions, respectively. At 760 °C, the 1300 W specimens still maintained excellent high-temperature strength. Full article

Recent advances in chiplet architectures, heterogeneous integration, 2.5D/3D packaging, high-performance computing, and RF applications have increased the demand for high-density vertical interconnects and low-loss packaging platforms. Glass substrates have attracted considerable attention for next-generation advanced packaging because of their low dielectric loss, high dimensional stability, smooth surface, and compatibility with large-area panel-level processing. Through-glass vias (TGVs) are essential vertical interconnect structures that enable the electrical integration of glass substrates. This focused review summarizes TGV technologies for glass-based advanced packaging from the perspectives of via formation, seed layer deposition, metallization, Cu filling, defect formation, reliability, and plugging-based alternative architectures. Representative TGV formation methods, including laser drilling, selective laser etching, laser-induced deep etching, wet/dry etching, and photosensitive glass processing, are compared. Metallization approaches based on sputtering, electroless plating, ALD/CVD, and hybrid processes are discussed together with Cu electroplating strategies such as conformal plating, bottom-up filling, pulse or pulse-reverse plating, and engineered-geometry filling. Key defects, including voids, seams, pinch-off, seed discontinuity, Cu/glass interfacial delamination, glass cracking, and Cu protrusion, are reviewed in relation to thermomechanical reliability. Finally, polymer/dielectric plugging, plugging/re-drilling, conductive paste plugging, and hybrid Cu/plugging structures are discussed as application-specific alternatives for balancing electrical performance, reliability, manufacturability, yield, and cost. Full article

Electroluminescence (EL) imaging is widely used for photovoltaic (PV) defect inspection, yet fair comparison of deep learning backbones remains difficult because datasets, labels, and protocols vary across studies. This work presents a controlled image-level benchmark of six architectures (ConvNeXt-T, ViT-B/16, DeiT-B/16, Swin-T, DenseNet121, and MobileNetV3-Large) across five hierarchical tasks for monocrystalline and polycrystalline cells with binary and multi-class labels. A balanced proprietary dataset of 20,000 single-cell EL images was evaluated with identical preprocessing, augmentation, training, and stratified five-fold cross-validation, yielding 150 runs. ConvNeXt-T achieved the highest mean macro-F1 (93.12%) while using about one-third of the parameters of base ViT/DeiT models. On the four-class polycrystalline task, it reached 84.94 ± 0.45% macro-F1, compared with 70.08 ± 1.19% for DenseNet121 and 59.43 ± 1.71% for MobileNetV3-Large. Error analysis revealed conservative missed-defect behavior in lightweight CNNs, especially for surface-level degradation and crack categories. The results provide image-level cross-validation evidence for controlled benchmarking and motivate future module-level grouped validation. Full article

This study investigates the effect of laser powder bed fusion (L-PBF) parameters and T7 heat treatment on the defect formation, microstructure, and mechanical properties of a high-strength Al-Cu 224 aluminum alloy. The laser power (200–370 W), scanning speed (130–1900 mm/s), and hatch spacing (90–130 μm) were varied to evaluate their influence on hot cracking and porosity. Microstructural characterization using optical microscopy, scanning electron microscopy, and electron backscatter diffraction revealed that an energy density of 400 J/mm³ substantially reduced visible hot cracking in the examined microscopic regions by reducing the thermal gradients. However, this resulted in increased keyhole porosity, thereby limiting the relative density to 95%. The as-built samples exhibited a yield strength of 152 MPa and an elongation of 9.2%, and the T7 heat treatment improved the yield strength to 233 MPa, whereas the elongation remained unchanged. Keyhole pores served as primary crack initiation/propagation sites during tensile loading, reducing ductility. Lower energy densities increased the geometrically necessary dislocation density and promoted cracking because of higher residual stresses due to greater accumulated plastic strain and lattice curvature. These results clarify process–structure–property relationships, emphasize the trade-offs between defect types and performance, and provide a robust framework for optimizing L-PBF processing of high-strength Al alloys through parameter tuning and post-heat treatment. Full article

To meet the increasing demands of aircraft engines for high thrust-to-weight ratios and elevated turbine inlet temperatures, turbine blades have been progressively designed with thin-walled structures. It has been demonstrated that the mechanical properties of Ni₃Al-based single-crystal alloys (SXs) are highly sensitive to specimen thickness. In this study, the high-cycle fatigue behavior of [111]-oriented Ni₃Al-based SXs with wall thicknesses of 0.3, 0.5, and 0.8 mm was systematically investigated under tensile–tensile loading conditions at 1000 °C. The results revealed that, as the wall thickness decreased, the fatigue life of the alloy significantly deteriorated, while the crack initiation site gradually shifted from the specimen interior toward the surface and near-surface regions. Furthermore, the fatigue failure mode transitioned from being dominated by internal defects to being controlled primarily by near-surface damage. Near-surface damage induced by high-temperature oxidation and geometric constraints was identified as the primary factor responsible for the degradation of the high-cycle fatigue performance of the SXs. In addition, the cyclic deformation behavior at 1000 °C was governed by the synergistic effects of dislocation climb, cross-slip, and γ′-phase shearing. This study provides both theoretical guidance and experimental evidence for the structural optimization of next-generation single-crystal turbine blades for advanced aircraft engines. Full article

Surface defect detection is an important task for quality assurance in steel manufacturing. Although YOLO-style detectors are widely used due to their strong performance, they often struggle to accurately localize edge-dominant defects such as crazing and fine cracks. This limitation arises because such defects exhibit weak feature representations. In addition, their high-frequency structural details are progressively degraded during repeated downsampling. To address this issue, a YOLO-based detection framework named EDEN-YOLO is proposed. It incorporates an in-place Edge-Enhance module into the YOLOv8 baseline to improve structural sensitivity. Specifically, a Local Feature Enhancement (LFE) module is designed to capture edge-sensitive patterns. A Gated Module is further introduced to perform spatially selective recalibration of backbone features. This design enhances edge responses while suppressing noise. Experiments on the NEU-DET benchmark demonstrate the effectiveness of the proposed method. EDEN-YOLO achieves 80.5% mAP@0.5 on NEU-DET, showing an improvement over the reproduced YOLOv8 baseline while introducing a moderate increase in model complexity by 0.52M parameters and 1.3 GFLOPs. A supplementary evaluation on the GC10-DET dataset shows that EDEN-YOLO achieves 65.2% mAP@0.5, compared with 61.0% for the reproduced YOLOv8 baseline. The qualitative results show that the proposed module produces more compact feature responses. Full article

Lithium-containing ceramics were significant tritium breeders for the fusion blanket concept, for which tritium release performance and mechanical properties serve as the core indicators for evaluating their performance as tritium breeders. The Li₄Si_0.8Ti_0.2O₄ material was designed as an advanced tritium breeder and fabricated into ceramic pebbles via the freeze-drying method. The tritium release properties of the Li₄Si_0.8Ti_0.2O₄ sample pebbles were investigated via temperature-programmed desorption (TPD). The mechanical properties of the same batch of tritium breeder pebbles were analyzed comparatively, specifically examining the change in their compressive strength before and after irradiation. The sample pebbles irradiated with different neutron doses show different tritium release characteristics, and the tritium release temperature was about 293–553 °C. This was due to the H₂-tritium isotope exchange reaction, and radiation with different neutron doses will lead to different release temperatures of tritium. The mechanical properties of the Li₄Si_0.8Ti_0.2O₄ ceramic pebbles decreased significantly after irradiation. The main reason was that the accumulation of lattice defects and helium bubbles produced by high-energy neutron irradiation leads to internal cracks and helium embrittlement in the material. These results indicate that Li₄Si_0.8Ti_0.2O₄ solid solution may be considered a potential candidate for tritium breeder materials. Full article

The development of multifunctional biomaterials for bone repair requires precursors that combine bioactivity, moderate antimicrobial growth-inhibitory effect, and imaging. This study demonstrates the multifunctional versatility of a single family of rare-earth-doped β-tricalcium phosphates (β-TCPs), Ca₉Eu(PO₄)₇ and Ca₉Dy(PO₄)₇, across three distinct formats: bioactive thin films (for implant coatings), brushite cements (for injectable bone fillers), and radiopaque PMMA bone composites (for load-bearing applications). This work serves as a proof-of-concept that the same doped phosphate precursors can address different clinical needs while retaining bioactivity, antimicrobial properties, and radiopacity. The phosphate precursors were synthesized via solid-state reaction. Pulsed laser deposition (PLD) was used to form amorphous, dense, and crack-free coatings, which exhibited excellent in vitro bioactivity through the rapid dissolution–reprecipitation of a carbonated apatite layer in simulated body fluid. The brushite-based bone cements were produced from doped β-TCPs. These cements demonstrated high cytocompatibility with mesenchymal stromal cells (>89% viability) and significantly enhanced osteogenic differentiation with antimicrobial activity against common pathogens (S. aureus, E. coli, P. aeruginosa). Furthermore, incorporation of these phosphates as fillers into PMMA bone cement resulted in a homogeneous particle distribution with reduced agglomeration compared to undoped β-TCPs, achieving clinically relevant radiopacity values (913 ± 22.4 HU for Dy-doped sample). Post-mortem studies by the CT method were performed on the vertebrae with PMMA–phosphate composites and brushite cements. It was shown that brushite cement in ovine lumbar vertebrae defects exhibited the highest radiopacity (1450–1550 ± 25 HU). The findings establish rare-earth-doped β-TCP as a unified multifunctional precursor that imparts bioactivity, the ability to support in vitro mineralization, antimicrobial properties, and enhanced radiopacity to thin films, phosphate cements, and polymer composite materials. Full article

Accurate surface defect classification is a critical requirement in structural health monitoring and infrastructure inspection, where defects, including cracks, spalling, delamination and noncrack regions, often appear with low-contrast and complex background textures. Motivated by the need for a robust and discriminative framework that can enhance defect visibility and focus learning on damage-critical regions, this research proposes a novel damage-aware DenseNet-201 (DA-DenseNet-201) model for surface defect classification. As a critical novelty, a damage-aware adaptive contrast-limited adaptive histogram equalisation (DAC) filtering strategy is introduced as a preprocessing stage. The proposed DAC filter dynamically adjusts contrast enhancement parameters based on damage indicators, selectively amplifying crack edges and defect textures while preserving healthy surface regions and suppressing noise. Building on this method, enhanced images are processed using a pretrained DenseNet-201 backbone, retaining the benefits of dense feature propagation and efficient gradient flow. To strengthen the discriminative learning of DA-DenseNet-201 further, an attention refinement block is integrated into the network, combining channel attention to emphasise defect-relevant feature responses and spatial attention to localise damage regions accurately. In addition, a multiscale feature fusion mechanism aggregates feature maps from multiple dense blocks to capture fine-grained crack patterns, texture-level degradation and high-level semantic damage information. Extensive experiments conducted on surface defect datasets demonstrate its effectiveness, achieving a superior classification accuracy of 98.93%, along with notable improvements in sensitivity, specificity and the intersection over union compared with state-of-the-art models. These results confirm that the proposed DA-DenseNet-201 provides a reliable and high-performance solution for automated surface defect classification. Full article