Search Results (1,743)

Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data and comparative studies for actual ship structure details. This study addresses this limitation by evaluating the fatigue strength of longi-web connections in hull structures using local stress approaches, including hot spot stress, effective notch stress, notch stress intensity factor, and structural stress methods. Finite element analyses were conducted, and the predicted fatigue lives and failure locations were compared with experimental results. Although there are some differences between each method, all methods are valid and reasonable for predicting the primary failure locations and evaluating fatigue life. These findings provide a basis for considering suitable fatigue assessment methods for welded ship structures with respect to joint geometry and failure mechanisms. Full article

The growing complexity of modern manufacturing has intensified the need for precise control of residual stresses to ensure structural reliability, dimensional stability, and material performance. This study conducts a bibliometric review using data from Scopus and Web of Science, covering publications from 2019 to 2024. Residual stress research in metal manufacturing has gained prominence, particularly in relation to welding, additive manufacturing, and machining—processes that induce significant stress gradients affecting mechanical behavior and service life. Emerging trends focus on simulation-based prediction methods, such as the finite element method, heat treatment optimization, and stress-induced defect prevention. Key thematic clusters include process-induced microstructural changes, mechanical property enhancement, and the integration of modeling with experimental validation. By analyzing the evolution of research output, global collaboration networks, and process-specific contributions, this review provides a comprehensive overview of current challenges and identifies strategic directions for future research in residual stress management in advanced metal manufacturing. Full article

The object of this study is welded joints of AISI 321 and Ti-6Al-4V, obtained by diffusion welding on developed conical surfaces. The problem of creating bimetallic joints of AISI 321 and Ti-6Al-4V with developed conical contact surfaces, using diffusion welding through an intermediate Electrolytic Tough Pitch Copper (Cu-ETP) copper layer, was solved. The joints were studied using micro-X-ray spectral analysis, microstructural analysis, and mechanical tests. High mutual diffusion of copper and titanium, along with increased concentrations of Cr and V in copper, was detected. The shear strength of the obtained welded joints is 250 MPa and 235 MPa at 30 min and 15 min, respectively, which is higher than the copper layer’s strength (180 MPa). The obtained results are explained by the dislocation diffusion mechanism in the volume of grains and beyond, due to thermal deformations during welding. Under operating conditions of internal pressure and cryogenic temperatures, the strength of the connection is ensured by the entire two-layer structure, and tightness is ensured by a vacuum-tight diffusion connection. The obtained strength of the connection (250 MPa) is sufficient under the specified operating conditions. Analysis of existing solutions in the literature review indicates that industrial application of technology for manufacturing bimetallic adapters from AISI 321 stainless steel and Ti-6Al-4V titanium alloy is limited to butt joints with small geometric dimensions. Studies of the transition zone structure and diffusion processes in bimetallic joints with developed conical contact surfaces enabled determination of factors affecting joint structure and diffusion coefficients. The obtained bimetallic adapters, made of Ti-6Al-4V titanium alloy and AISI 321 stainless steel, can be used to connect titanium high-pressure vessels with stainless steel pipelines. Full article

Aluminum matrix composites provide an ideal solution for lightweight brake disks, but conventional casting processes are prone to crack initiation due to inhomogeneous reinforcement dispersion, gas porosity, and inadequate toughness. To break the conventional trade-off between high wear resistance and low toughness of brake disks, this study fabricated a bimetallic structure of SiC_p/Al–Fe–V–Si aluminum matrix composite and cast ZL101 alloy using friction stir lap welding (FSLW). Then, the microstructural evolution, mechanical properties, and tribological behavior of the FSLW joints were studied by XRD, SEM, TEM, tensile testing, and tribological tests. The results showed that the FSLW process homogenized the distribution of SiC particle reinforcements in the SiC_p/Al–Fe–V–Si composites. The Al₁₂(Fe,V)₃Si heat-resistant phase was not decomposed or coarsened, and the mechanical properties were maintained. The FSLW process refined the grains of the ZL101 aluminum alloy through recrystallization and fragmented eutectic silicon, improving elongation to 22%. A metallurgical bond formed at the joint interface. Tensile fracture occurred within the ZL101 matrix, demonstrating that the interfacial bond strength exceeded the alloy’s load-bearing capacity. In addition, the composites exhibited significantly enhanced wear resistance after FSLW, with their wear rate reduced by approximately 40% compared to the as-received materials, which was attributed to the homogenized SiC particle distribution and the activation of an oxidative wear mechanism. Full article

The inferior electrical conductivity and elevated hardness of AgSnO₂ electrical contact materials have impeded their development. To investigate the effects of Co, Bi, and La doping on the stability and electrical properties of AgSnO₂, this study established interfacial models of doped AgSnO₂ based on first-principles calculations initiated from the atomic structures of constituent materials, subsequently computing electronic structure parameters. The results indicate that doping effectively enhances the interfacial stability and bonding strength of AgSnO₂ and thereby predicted improved electrical contact performance. Doped SnO₂ powders were prepared experimentally using the sol–gel method, and AgSnO₂ contacts were fabricated using high-energy ball milling and powder metallurgy. Testing of wettability and electrical contact properties revealed reductions in arc energy, arcing time, contact resistance, and welding force post-doping. Three-dimensional profilometry and scanning electron microscopy (SEM) were employed to characterize electrical contact surfaces, elucidating the arc erosion mechanism of AgSnO₂ contact materials. Among the doped variants, La-doped electrical contact materials exhibited optimal performance (the lowest interfacial energy was 1.383 eV/Ų and wetting angle was 75.6°). The mutual validation of experiments and simulations confirms the feasibility of the theoretical calculation method. This study provides a novel theoretical method for enhancing the performance of AgSnO₂ electrical contact materials. Full article

Spot-welding joints are widely used in modern industries, and their fatigue life is crucial for the safety and reliability of structures. This paper proposes a method for predicting the fatigue life of spot-welding joints by integrating traditional structural stress methods and machine learning algorithms. Systematic fatigue tests were conducted on Q&P980 steel spot-welding joints to investigate the influence of the galvanized layer on fatigue life. It was found that the galvanized layer significantly reduces the fatigue life of spot-welding joints. Further predictions of fatigue life using machine learning algorithms, including Random Forest, Artificial Neural Networks, and Gaussian Process Regression, demonstrated superior prediction accuracy and generalization ability compared to traditional structural stress methods. The Random Forest algorithm achieved an R² value of 0.93, with lower error than traditional methods. This study provides an effective tool for the fatigue life assessment of spot-welding joints and highlights the potential application of machine learning in this field. Full article

Fatigue damage in steel–concrete composite structures frequently initiates at welded joints due to stress concentrations and inherent defects. This study investigates the stress intensity factors (SIFs) associated with fatigue cracks in the welds of steel longitudinal beams, employing the FRANC3D–ABAQUS interactive technique. A finite element model was developed and validated against experimental data, followed by the insertion of cracks at both the weld root and weld toe. The influences of stud spacing, initial crack size, crack shape, and lack-of-penetration defects on Mode I SIFs were systematically analyzed. Results show that both weld root and weld toe cracks are predominantly Mode I in nature, with the toe cracks exhibiting higher SIF values. Increasing the stud spacing, crack depth, or crack aspect ratio significantly raises the SIFs. Lack of penetration defects further amplifies the SIFs, especially at the weld root. Based on the computed SIFs, fatigue life predictions were conducted using a crack propagation approach. These findings highlight the critical roles of crack geometry and welding quality in fatigue performance, providing a numerical foundation for optimizing welded joint design in composite structures. Full article

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article

High-density polyethylene (HDPE) pipes are widely used in urban natural gas pipeline systems due to their excellent mechanical and chemical properties. However, welding joints are critical weak points in these pipelines, and defects, such as cold welding—caused by reduced temperature or/and insufficient pressure—pose significant safety risks. Traditional non-destructive testing (NDT) methods face challenges in detecting cold welding defects due to the polymer’s complex structure and characteristics. This study presents a microwave-based NDT system for detecting cold welding defects in thermal fusion welds of HDPE pipes. The system uses a focusing antenna with a resonant cavity, connected to a vector network analyzer (VNA), to measure changes in microwave parameters caused by cold welding defects in thermal fusion welds. Experiments conducted on HDPE pipes welded at different temperatures demonstrated the system’s effectiveness in identifying areas with a lack of fusion. Mechanical and microstructural analyses, including tensile tests and scanning electron microscopy (SEM), confirmed that cold welding defects lead to reduced mechanical properties and lower material density. The proposed microwave NDT method offers a sensitive, efficient approach for detecting cold welds in HDPE pipelines, enhancing pipeline integrity and safety. Full article

Shoulder-assisted heating friction plug welding (SAH-FPW) experiments were conducted to repair keyhole-like volumetric defects in 6082-T6 aluminum alloy, employing a novel concave backing hole technique on a flat backing plate. This approach yielded well-formed plug welded joints without significant macroscopic defects. Notably, the joints exhibited no thinning on the top surface while forming a reinforcing boss structure within the concave backing hole on the backside, resulting in a slight increase in the overall load-bearing thickness. The introduction of the concave backing hole led to distinct microstructural zones compared to joints welded without it. The resulting joint microstructure comprised five regions: the nugget zone, a recrystallized zone, a shoulder-affected zone, the thermo-mechanically affected zone, and the heat-affected zone. Significantly, this process eliminated the poorly consolidated ‘filling zone’ often associated with conventional plug repairs. The microhardness across the joints was generally slightly higher than that of the base metal (BM), with the concave backing hole technique having minimal influence on overall hardness values or their distribution. However, under identical welding parameters, joints produced using the concave backing hole consistently demonstrated higher tensile strength than those without. The joints displayed pronounced ductile fracture characteristics. A maximum ultimate tensile strength of 278.10 MPa, equivalent to 89.71% of the BM strength, was achieved with an elongation at fracture of 9.02%. Analysis of the grain structure revealed that adjacent grain misorientation angle distributions deviated from a random distribution, indicating dynamic recrystallization. The nugget zone (NZ) possessed a higher fraction of high-angle grain boundaries (HAGBs) compared to the RZ and TMAZ. These findings indicate that during the SAH-FPW process, the use of a concave backing hole ultimately enhances structural integrity and mechanical performance. Full article

Non-uniform temperature fields are developed during the welding of studs in steel–concrete composite bridges. Due to uneven thermal expansion and reversible solid-state phase transformations between ferrite/martensite and austenite structures within the materials, residual stresses are induced, which ultimately degrades the mechanical performance of the structure. For a better understanding of the influence on steel–concrete composite bridges’ structural behavior by residual stress, accurate simulation of the spatio-temporal temperature distribution during stud welding under practical engineering conditions is critical. This study introduces a precise simulation method for temperature evolution during stud welding, in which the Gaussian heat source model was applied. The simulated results were validated by real welding temperature fields measured by the infrared thermography technique. The maximum error between the measured and simulated peak temperatures was 5%, demonstrating good agreement between the measured and simulated temperature distributions. Sensitivity analyses on input current and plate thickness were conducted. The results showed a positive correlation between peak temperature and input current. With lower input current, flatter temperature gradients were observed in both the transverse and thickness directions of the steel plate. Additionally, plate thickness exhibited minimal influence on radial peak temperature, with a maximum observed difference of 130 °C. However, its effect on peak temperature in the thickness direction was significant, yielding a maximum difference of approximately 1000 °C. The thermal influence of group studs was also investigated in this study. The results demonstrated that welding a new stud adjacent to existing ones introduced only minor disturbances to the established temperature field. The maximum peak temperature difference before and after welding was approximately 100 °C. Full article

E36 steel, widely used in shipbuilding and offshore structures, offers moderate strength and excellent low-temperature toughness. However, its welded joints are highly susceptible to fatigue failure. Cracks typically initiate at weld toes or within the heat-affected zone (HAZ), severely limiting the fatigue life of fabricated components. Traditional life prediction methods are complex, inefficient, and lack accuracy. This study proposes a machine learning (ML) framework for efficient fatigue life prediction of E36 welded joints. Welded specimens using SQJ501 filler wire on prepared E36 steel established a dataset from 23 original fatigue test data points. The dataset was expanded via Z-parameter model fitting, with data scarcity addressed using SMOTE. Pearson correlation analysis validated data relationships. After grid-optimized training on the augmented data, models were evaluated on the original dataset. Results demonstrate that the machine learning models significantly outperformed the Z-parameter formula (R² = 0.643, MAPE = 16.15%). The artificial neural network (R² = 0.972, MAPE = 4.45%) delivered the best overall performance, while the random forest model exhibited high consistency between validation (R² = 0.888, MAPE = 6.34%) and testing sets (R² = 0.897), with its error being significantly lower than that of support vector regression. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article

When processing widely used materials in welded structures such as steels, a surface operation such as plasma cutting applied in the automated Computer Numerical Control (CNC) version can provide technical and economic benefits to the cut components, but the impact on health and environment must be addressed accordingly. In this paper, a plate with a base material made of S355J2 + AR structural steel is cut in 10 pieces with plasma in a water-bed designed and manufactured by the authors in order to mitigate such risks. The surfaces cut in the water-bed are compared to surfaces cut in open air by macroscopic analyses of the edge cut, by microscopic analyses of the cut parts—base material, heat-affected zone, and cut area—and by hardness determinations. The results reveal improvements as a result of plasma cutting in the water-bed: slag reduction, preservation of granulation, transformations in the austenitic temperature zone, and hardness in the heat-affected zone. Compared to a classical cutting procedure such as oxygen flame cutting, the proposed procedure offers a clean alternative and also requires low maintenance. Full article

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article