Search Results (3,338)

To address surface defects and enhance the wear resistance of 316L stainless steel parts fabricated by Selective Laser Melting (SLM), this study applied shot peening (SP) surface treatment to the SLM-processed samples. Ball-on-disk tribological tests were systematically conducted under water-lubricated conditions to investigate the evolution of surface morphology, microstructure, microhardness, and tribological performance before and after SP. The results indicate that SP induced severe plastic deformation in the surface layer, effectively refining the coarse columnar crystals and melt pool structures characteristic of SLM, and forming a crystalline hardened layer with a depth of 70–80 μm. Consequently, the surface microhardness increased by 21.97% compared to the un-peened samples. Under loads of 20 N and 30 N, the coefficient of friction (COF) of the SP-treated samples decreased by 16.36% and 12.4%, while the wear rate was reduced by 17.09% and 14.9%, respectively. In this load range, the samples primarily exhibited uniform plowing and localized adhesive wear, demonstrating significantly improved resistance to plastic deformation and crack initiation. However, when the load increased to 40 N, intense stress and thermal effects diminished the strengthening benefits of SP, resulting in no significant difference in tribological performance between the SP-treated and untreated samples. At this stage, the dominant wear mechanism transitioned to severe plastic deformation, extensive delamination, and thermally induced adhesion. Full article

This study examines the joining characteristics of Cu foil stacks to lead tabs using green-laser welding in the main-welding step of a sequential welding process for lithium-ion pouch cells. The influence of lap configuration, line and wobble seam strategies, and process parameters was systematically investigated in terms of bead morphology, mechanical performance, metallurgical characteristics, and electrical resistance. Under the present line-welding parameter window (2.0 kW, 100–200 mm/s), humping, pinholes, and porosity were observed, particularly in the upper lead-tab configuration, which is attributed to melt-pool/keyhole instability under the applied conditions. Wobble welding effectively suppressed these defects in the foil-stack configuration by promoting stable melt flow and efficient bubble expulsion. Mechanical tests revealed that the wobble-based seam strategy achieved a maximum tensile–shear load of approximately 1.28 kN at a wobble amplitude of 0.8 mm. Fracture analysis confirmed a transition from seam-type interfacial failure in line welding to ductile tearing in the heat-affected zone with wobble welding. In electrical performance, wobble welding reduced resistance to as low as 45 µΩ at a wobble amplitude of 1.2 mm, while line welding yielded higher and scattered values. These results should be interpreted as the combined outcome of the wobble-based seam strategy (beam oscillation together with overlapped stitch welding at a lower travel speed) under the present processing windows. A strictly matched A/B comparison at identical linear energy density and seam layout will be investigated in future work to isolate the effect of oscillation. Full article

The article considers issues related to improving the surface characteristics of titanium Gr2 using one of the lightest, cheapest and most ecological methods—electrospark deposition with low pulse energy and with ultradisperse electrodes TiB₂-TiAl with nanosized additives of NbC and ZrO₂. Using profilometric, metallographic, XRD, SEM and EDS methods, the change in the geometric characteristics, composition, structure, micro and nanohardness of the coatings as a function of the electrical parameters of the ESD regime has been studied. The results show that the use of TiB₂-TiAl electrodes and low pulse energy allows the formation of dense, continuous and uniform coatings that demonstrate a significant reduction in roughness, inherent irregularities and structural defects of electrospark coatings. Coatings with minimal defects, with crystalline–amorphous structures, with newly formed intermetallic and wear-resistant double and triple phases of the type AlTi3, TiAl₃, TiB, TiN_0.3, Al₂O₃, AlB₂, TiC_0.3N_0.7, Ti_3.2B_1.6N_2.4, Al_2.86O_3.45N_0.55 have been obtained. Possibilities have been found for controlling and obtaining specific values for the roughness and thickness of coatings in the ranges Ra = 1.5–3.2 µm and δ = 8–19.5 µm, respectively. The electrical parameters of the modes ensure the production of coatings with previously known thickness and roughness, with increased microhardness up to 13 GPa, with the maximum possible content of deliberately synthesized high-hard phases and with ultra-fine-grained structures have been defined. Full article

The regeneration of bone and the repair of large segmental bone defects represent critical challenges in regenerative medicine. Natural bone tissue is an anisotropic material characterized by an intricate gradient distribution in structure, mechanical properties, and biochemical composition; this multi-dimensional heterogeneity is crucial for maintaining its physiological functions and guiding regeneration. Although tissue engineering scaffolds have demonstrated significant potential in the treatment of bone defects, homogeneous or single-gradient scaffolds often struggle to precisely recapitulate the high degree of heterogeneity and anisotropy of natural bone from the macroscopic to the microscopic level, thereby limiting their capability in repairing complex bone defects. In recent years, biomimetic gradient scaffolds—particularly those employing multi-gradient synergistic designs that integrate physical structure, biochemical composition, and mechanical properties—have emerged as a research frontier in this field due to their ability to accurately mimic the natural bone microenvironment and regulate cellular behavior. This research aims to systematically review the latest research progress in gradient scaffolds for bone tissue engineering. First, gradient characteristics of biomimetic gradient bone scaffolds are summarized; second, the design strategies for gradient scaffolds are discussed in depth, with a focus on the applications and advantages of advanced fabrication techniques, such as additive manufacturing, in constructing multi-dimensional gradient structures; finally, based on current research findings, the emerging development trends and future research directions of biomimetic gradient bone scaffolds are outlined to provide a reference for innovative breakthroughs in the field of bone tissue engineering. Full article

Insulation defects are the main cause of transformer faults, and the partial discharge phenomenon generated by defects under high-voltage excitation can reflect the internal characteristics of the defects. Therefore, studying the characteristics of partial discharge signals can provide an important basis for the analysis of transformer partial discharge problems. This article proposes a transformer partial discharge ultra-high-frequency signal classification and recognition method based on the Adaptive Hybrid Attention Fusion Network. The feature extraction of partial discharge waveform is carried out through a dual flow network structure, where the ResNet branch focuses on extracting local features and the Swin Transformer branch focuses on extracting global features. Then, a new Adaptive Hybrid Attention Fusion Network fusion model is used to weight the extracted features according to adaptive allocation weights, ultimately achieving the classification and recognition of transformer partial discharge ultra-high-frequency signals. The experiment shows that this method achieves a fault detection accuracy of 99.58%, with a loss rate of only 0.73%. Compared to various existing network models, the accuracy of the proposed model reached 99.58%, the recall was 99.58%, and the F1 score was 99.58%, which is significantly better than other model methods, indicating that the model has significant advantages in detection performance. Full article

Xanthomonas arboricola pv. pruni causes bacterial spot in peaches, a major disease affecting global Prunus production. Despite its economic significance, the virulence mechanisms that enable X. arboricola pv. pruni to colonize peach tissues and induce characteristic necrotic symptoms remain poorly understood. To identify key virulence determinants, a robust and reliable detached-leaf inoculation system was developed, and a genome-wide forward genetic screen of 2400 Tn5 mutants was conducted. A total of 34 mutants with consistently reduced virulence were identified, representing diverse functional categories including secretion systems, nutrient acquisition, primary metabolism, and regulatory pathways. The most prominent findings were the repeated identification of independent mutants in two type III effector genes, xopN and xopX, highlighting these effectors as central and nonredundant contributors to symptom induction. Mutants in the type III secretion system translocon-associated gene hrpF also showed virulence defects. Additional mutants affecting phosphate uptake (pstS), ammonium transport, and vitamin B₆ biosynthesis (pdxA, serC) revealed metabolic requirements essential for in planta fitness. Notably, several mutants reached bacterial population levels comparable to the wild-type isolate but produced little or no symptoms, indicating that bacterial multiplication and symptom development are not necessarily linked. This study provides the first comprehensive genome-wide functional screen of X. arboricola pv. pruni virulence and establishes a framework for dissecting infection mechanisms. The essential factors identified here, particularly XopN, XopX, and key metabolic pathways, represent promising targets for future anti-virulence strategies to manage bacterial spot disease. Characterizing the specific functions of each virulence factor identified in this study will be an important focus of future work. Full article

Omphalocele is a congenital abdominal wall defect whose underlying muscular and fascial structural characteristics remain incompletely understood. This study aimed to investigate the anatomical and ultrastructural features of the abdominal wall in patients with omphalocele to provide additional insight into tissue organization at the defect margins. We report a series of 11 term-born patients diagnosed with omphalocele between 2024 and 2025 who were admitted to a pediatric surgery department for operative management. Following informed consent from legal guardians, two small intraoperative biopsies were obtained during surgical repair from the rectus abdominis muscle and its anterior aponeurosis, sampled from the supraumbilical and subumbilical regions. Tissue specimens were fixed within 48 h and analyzed using conventional histopathology and scanning electron microscopy to assess potential structural and ultrastructural alterations. The combined microscopic approaches allowed for a detailed evaluation of muscle and aponeurotic architecture in different abdominal regions. These observations contribute to a more comprehensive understanding of abdominal wall tissue characteristics in omphalocele and may support improved interpretation of the structural changes associated with this congenital condition. Full article

Under random wave loading, the crack growth rate exhibits jump-like cycle-to-cycle variations, which limit the direct use of efficient integration schemes such as the Euler method. In addition, the crack growth life is highly sensitive to the initial crack size and aspect ratio, while the initial defects are often difficult to determine accurately in practice, leading to increased uncertainty in life assessment. To address these issues, a cycle-scaling-based crack size accumulation method for random loading is proposed. A predictor–corrector improved Euler method is then established, and a fourth-order Runge–Kutta scheme incorporating the cycle-scaling transformation is derived. Furthermore, based on spectral analysis theory, a mapping between the wave spectrum and the crack-tip stress intensity factor response spectrum is developed. A stress intensity factor range sequence is generated by concatenating short-term sea states, thereby providing a random loading input that preserves the required statistical characteristics. Finally, a 21,000-TEU container ship is analyzed as a case study to investigate crack growth evolution for different initial aspect ratios. The results show that the crack aspect ratio gradually converges to a particular trend during propagation. A convergent aspect ratio curve is fitted. And a unified life assessment curve is constructed. An equivalent transformation is used to map an arbitrary initial crack shape and size to an equivalent convergent aspect ratio crack. As a result, fatigue life can be rapidly estimated using a single “initial crack size–fatigue life” curve, providing support for crack growth life assessment and the definition of defect acceptance limits for ship hull structures. Full article

This study investigates ultrasonic wave propagation in carbon fiber reinforced polymer (CFRP) composites containing foreign object debris (FOD) by introducing a novel method to characterize the depth and size of FOD, from a single captured waveform generated by an out-of-focus spherically focused transducer. Current methods of inspection utilize a raster approach to both detect and quantify FOD, which is limited to identifying FOD smaller than 4 mm. The method introduced in the present paper allows for a single point scan to detect and quantify FOD, as small as 0.5 mm, with the highest error in the depth estimation being less than 8%. This paper presents experimental testing to inform a finite element analysis of a full waveform simulation of an immersion tank inspection environment and compares waveforms between testing and simulation. A transient pressure acoustic model is developed in the COMSOL Multiphysics environment to simulate wave propagations. Simulation results provide waveform reflection and transmission at material interfaces, which will occur when there is an acoustic mismatch between materials. The transmitted ultrasonic wave is partially reflected toward the transducer upon encountering material interfaces between the water, CFRP laminate, and the FOD. Simulation results show that the acoustic profile and pressure of the reflected wave captured by the transducer allows an accurate identification of FOD depth and size within the composite structure, suggesting an alternative method of inspection to quantify FOD characteristics faster than through conventional approaches. Results show an increase in captured signal pressure of over 125% between the 0.5 mm FOD and the 1 mm FOD located on the mid-plane of the laminate, and 500% between the same 0.5 mm FOD and 1 mm FOD placed near the front wall. These results suggest the potential sensitivity limits for physical component. This work demonstrates that small FOD, which are often difficult to resolve and quantify under conventional raster-based inspection, can be reliably identified by intentionally positioning the specimen within the defocused region of a spherically focused transducer. Results are presented to correlate the reflected acoustic pressure amplitude to defect depth, transducer–specimen distance, and FOD size, providing an approach to quantitatively discriminate small defects that would otherwise produce ambiguous signals. Full article

As substantial incorporation of variable renewable generation technologies, particularly wind and photovoltaic systems, becomes more common, the complexities of power supply and demand characteristics are increasing, making it essential to conduct a detailed power and energy balance analysis. Aiming at regional power systems with multi-source structures and internal transmission interface constraints, this paper proposes a power and energy balance analysis method that considers demand-side carbon emissions. First, a closed-loop mechanism of “carbon signal–load response–balance optimization” based on nodal carbon potential (NCP) is constructed. In this framework, NCP is utilized to generate carbon signals that guide the active response of flexible loads, which are subsequently integrated into the coordinated optimization of power and energy balance. Second, a power and energy balance optimization model adapted to multi-source structures is established, where transmission power limits between zones are directly embedded into the constraint system, overcoming the defects of traditional heuristic methods that require repeated iterations to correct interfaces. Finally, an improved hybrid solution strategy for large-scale balance analysis is designed, significantly reducing the variable scale through the aggregation of similar units within zones. Case studies show that this method can effectively guide the load to shift toward low-carbon periods and nodes, significantly reducing total system carbon emissions and improving renewable energy consumption while ensuring power and energy balance. Full article

We propose a design of a composite splitter–filter by replacing the traditional periodic arrays with Fibonacci rod chains along both sides of the output channel of a T-junction photonic crystal waveguide. This integrated structure concurrently realizes the dual functions of a power splitter and an optical filter. The coexistence and effectiveness of these two functions are verified through numerical simulations. Furthermore, the proposed device exhibits excellent robustness against three types of defects and enables strong tunability of its operating wavelength window. Owing to these superior characteristics, this hybrid photonic crystal–quasicrystal structure holds significant application potential in photonic integrated circuits and high-performance optical communication systems. Full article

Defect detection on Printed Circuit Boards (PCBs) constitutes a pivotal component of the quality control system in electronics manufacturing. However, owing to the intricate circuitry structures on PCB surfaces and the characteristics of defects—specifically their minute scale, irregular morphology, and susceptibility to background texture interference—existing generic deep learning models frequently fail to achieve an optimal equilibrium between detection accuracy and inference speed. To address these challenges, this study proposes MDEB-YOLO, a lightweight real-time detection network tailored for PCB micro-defects. First, to enhance the model’s perceptual capability regarding subtle geometric variations along conductive line edges, we designed the Efficient Multi-scale Deformable Attention (EMDA) module within the backbone network. By integrating parallel cross-spatial channel learning with deformable offset networks, this module achieves adaptive extraction of irregular concave–convex defect features while effectively suppressing background noise. Second, to mitigate feature loss of micro-defects during multi-scale transformations, a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) is proposed. Utilizing bidirectional weighted paths and residual attention mechanisms, this network facilitates the efficient fusion of multi-view features, significantly enhancing the representation of small targets. Finally, the detection head is reconstructed based on grouped convolution strategies to design the Lightweight Grouped Convolution Head (LGC-Head), which substantially reduces parameter volume and computational complexity while maintaining feature discriminability. The validation results on the PKU-Market-PCB dataset demonstrate that MDEB-YOLO achieves a mean Average Precision (mAP) of 95.9%, an inference speed of 80.6 FPS, and a parameter count of merely 7.11 M. Compared to baseline models, the mAP is improved by 1.5%, while inference speed and parameter efficiency are optimized by 26.5% and 24.5%, respectively; notably, detection accuracy for challenging mouse bite and spur defects increased by 3.7% and 4.0%, respectively. The experimental results confirm that the proposed method outperforms state-of-the-art approaches in both detection accuracy and real-time performance, possessing significant value for industrial applications. Full article

With the increasingly prominent issues of energy shortage and environmental pollution, the development of clean energy materials has become a core topic in the academic community. SnSe, as a material with moderate bandgap, a high light absorption coefficient, and environmental friendliness, has shown broad application prospects in the fields of photovoltaics and thermoelectrics. However, pure SnSe thin films have inherent defects, low carrier concentration, and high recombination rates, which limit their photoelectric conversion efficiency. This article provides a detailed overview of the characteristics of band engineering control technology, defect control technology, and carrier concentration control technology, as well as the improvements in the characteristics of SnSe thin films that they bring. This article systematically reviews the research progress on doping control technology for SnSe thin films characteristics in recent years and analyzes and discusses the differences in typical doping elements on SnSe thin films characteristics, such as optical bandgap and absorption coefficient, and applicable application scenarios, such as photovoltaics, near-infrared/infrared detection, and thermoelectric and flexible optoelectronic devices. Furthermore, the interaction between the doping mechanism of dopants and natural defects, as well as the influence of the structural parameters of doped films on doping efficiency, were analyzed, and a predictive design route for the doping mechanism of SnSe films was proposed. Finally, the influence of different atomic fractions on the characteristics of SnSe thin films was discussed. Low atomic fractions are beneficial for bandgap tuning and absorption enhancement; high atomic fractions can easily introduce phase separation and non-radiative recombination. It is suggested that future researchers can continue to focus on the precise control of atomic fractions, exploration of new element co-doping, and industrial large-scale production applications, providing theoretical guidance for the design and application of SnSe thin films in photothermal devices. Full article

As uniaxial compressive strength (UCS) of rock material is influenced by petrographic characteristics, special emphasis is given to the visual assessment and determination of rock properties. Visual determination of properties such as lithology, fabric, defects and porosity were conducted on six petrographically different limestone varieties from Istria, Croatia. In addition to the macroscopic and microscopical determination of petrographic characteristics, measurements of ultrasound propagation velocity through samples were also conducted. According to the gained results of visual macro and micro assessment and ultrasound propagation, velocity estimation of uniaxial compressive strength was performed. Using predicted values of UCS, classification of samples according to different classifications was conducted where four out of six varieties of limestone were classified correctly. Based on the achieved results, the method has proven successful for rocks, which have uniaxial compressive strength over 100 MPa. Full article

Aiming at the detection challenges caused by the diverse morphology of microcracks in plate heat exchanger sheets, this paper proposes a detection framework that integrates parameter-driven adaptive template generation, binary scale optimization, and feature value threshold segmentation using convolutional networks. First, based on the grayscale characteristics of microcracks, an adaptive template generation model driven by key parameters (width, height, and endpoint grayscale difference) is constructed, obtaining a unique solution by solving the boundary conditions of physical features. Second, to overcome the challenge of microcrack width continuity, a binary scale optimization strategy based on the critical decay ratio k* of the correlation coefficient is designed, enabling the coverage of continuous-width defects with a finite set of templates. Finally, enhanced features are fed into a convolutional network. Utilizing the bimodal characteristic of the feature value distribution, the region corresponding to the extreme values in the top 0.3% before the foreground peak is located using 3σ extreme value statistics, achieving adaptive segmentation to identify defect regions. Evaluation on the self-built microcrack dataset SUT-B1 yielded results of 83.59% recall, 80.55% precision, and an F1 score of 81.98%. This method outperforms small object detection networks, demonstrating its advantage in morphological adaptability for small-sized objects. It also surpasses receptive field optimization modules, proving the necessity of structural optimization. The proposed method demonstrates practicality and scalability in the field of industrial inspection. Full article