Welding in the World最新文献

Excessive intermixing of the materials and thick intermetallic compounds (IMCs) could markedly impair the mechanical properties of Al-Cu dissimilar joints. In this study, a dynamic-stationary shoulder friction stir lap welding (DSSFSLW) was applied to Al-Cu lap welding, thereby reducing material mixing and the thickness of IMCs. Compared with traditional friction stir lap welding (FSLW), the shear strengths of DSSFSLW joints were higher than those of FSLW in the same welding parameters. The maximum shear strength could reach to 3.34 kN, which was 41.5% higher than that of FSLW joints. Failure was initiated on the Al side, immediately beside the SZ, and manifested as a cross-shaped crack. Furthermore, the formation mechanism of IMCs on the Al-Cu interface was elucidated. Quantitative analysis revealed that the thickness of IMCs on the side of SZ in the DSSFSLW joint was only 4μm, which was significantly lower than that of FSLW. Meanwhile, thermodynamic and kinetic studies indicated that the IMCs layer could be changed from Al₂Cu-AlCu-Al₄Cu₉ in the FSLW to Al₂Cu-Al₄Cu₉ by DSSFSLW. This study provides new insights for high-strength of Al-Cu lap joints.

This study proposes an integrated approach for optimizing the friction stir welding (FSW) process by combining sensor-based data acquisition, machine learning (ML), and digital twin (DT) technologies. Real-time sensor data including rotational speed, welding speed, axial force, torque, and temperature were collected during FSW operations. These parameters were correlated with weld quality indicators, such as surface appearance, internal defects, and tensile strength. A dataset of 132 weld samples was used to train supervised and unsupervised ML models, achieving a defect classification accuracy of 95%. In parallel, a COMSOL-based digital twin was developed to simulate thermo-mechanical aspects of the welding process. The model incorporated temperature-dependent material properties, frictional heat generation, and plastic deformation behavior to predict stress, strain, and temperature distributions. Model predictions were validated against experimental sensor data, confirming accuracy in peak temperature and torque estimation. The integrated ML-DT system functioned as a decision-support tool, enabling real-time process monitoring, virtual experimentation, and predictive defect detection. When implemented in an industrial environment, the system dynamically adapted welding parameters to maintain optimal conditions. This approach enhances process stability, reduces material waste, and improves weld integrity, offering a scalable solution for intelligent manufacturing and Industry 4.0 applications.

Gray cast iron components in nuclear power plants suffer from surface degradation under harsh conditions. This study investigates the additive repair of defective gray cast iron using double-sided laser cladding, focusing on the effects of laser power and scanning rate on the repair layer’s morphology, microstructure, and mechanical properties. Results indicate that the synergistic effect of these parameters significantly regulates cladding quality. Increasing power or decreasing scanning speed enlarges the cladding dimensions and heat-affected zone (HAZ), while insufficient heat input causes poor bonding. The microstructure exhibits a gradient distribution: the fusion zone (FZ) has fine grains and higher hardness (~ 400 HV) than the substrate (~ 200 HV), while the partial melting zone (PMZ) and HAZ undergo non-equilibrium phase transitions. Under optimal parameters (2400 W, 800 mm/min), the repair layer achieves a smooth surface, narrow HAZ (19 mm), and improved ultimate tensile strength (~ 250 MPa), meeting the FC200 standard. This work provides a process optimization basis for laser cladding repair to extend the service life of critical nuclear components.

The relationship between local weld geometry and fatigue life has been extensively studied over the past decades, driven by the need to enhance structural integrity and optimize costs throughout a structure’s service life. While numerous studies have explored the influence of weld geometry on fatigue strength, the comparative effect of different welding methods under comparable weld geometry quality remains largely unexplored. Furthermore, the influence of local geometric variations for each welding method has not been systematically evaluated. This study explores a large dataset of laser-scanned butt welds, analyzing key geometric parameters. The dataset is categorized by welding method (laser-hybrid welding, submerged arc welding, and flux core arc welding), and statistical distributions are examined to assess variations in weld geometry and compliance with ISO 5817 quality groups. The characteristic fatigue life for each quality group is estimated. The correlation between geometric factor and fatigue life is evaluated through the residual analysis of stress-life curve linear fitting. According to the findings, different geometry features dominate depending on the welding method. The fracture location is strongly influenced by angular misalignment, while fatigue strength is better explained by quantile-based analysis of local geometry. These results provide a basis for future predictive modeling and quality assessment in welded structures.

Liquefied hydrogen storage tanks are poised to play a pivotal role in the realization of a carbon–neutral society. While stainless steel is considered the material of choice for the first 50,000-m³ tank, there is a potential for carbon steel to be employed as the inner tank material in the future to increase capacity. The welding materials combined with the carbon steel are expected to be Hastelloy and Inconel alloys, which are currently used in LNG (liquefied natural gas) tank construction. However, since some reports have shown that the effect of Ni on hydrogen embrittlement properties deteriorates significantly on the high-Ni side, we conducted evaluation tests that took into account the operating environment. The SSRT (slow strain rate testing) revealed that the GTAW (gas tungsten arc welding) weld metal exhibited severe hydrogen embrittlement, whereas no significant crack propagation was observed in the constant-load CT (compact tension) tests. We analyzed the discrepancies between the two experimental methods and concluded that the differences are largely related to pre-straining and hydrogen diffusion and accumulation. From a FFS (fitness for service) perspective, the results of the CT tests are considered more representative of real-world conditions, indicating that this material combination is suitable for use in liquid hydrogen storage tanks.

Ceramic phase modification is an effective method to improve the performance of wire arc additive manufacturing (WAAM) aluminum alloy components. In this paper, a preparation method of ceramic aluminum alloy flux-cored wire was developed. An advanced apparatus was developed for the preparation of flux-cored wires. The preparation system comprises three integral units: a wire forming module, a drawing module, and a coiling module. The wire forming module features a configuration of three forming rollers and three closed dies, while the drawing and reduction module incorporates 12 sets of wire drawing dies with varying diameters, with each stage maintaining a controlled compression ratio of 20%. The fluidization properties of the core powder mixture were systematically examined, revealing that optimal powder flow characteristics were achieved when the constituent particles were sized as follows: aluminum particles at 300 μm, copper particles at 250 μm, and silicon particles at 200 μm. The pre-heat treatment parameters for the aluminum strip substrate were optimized, with the process conditions established as follows: heating temperature of 230 °C, soaking duration of 120 min, and air cooling as the cooling method. Through a sequential series of 12 drawing and reduction operations, a 1.2 mm diameter Al-Cu-NiO aluminum alloy flux-cored wire was successfully fabricated. During the WAAM process employing the developed Al-Cu-NiO flux-cored wire, the process exhibited stable arc combustion, consistent droplet transfer, and minimal spatter. The developed flux-cored wire was successfully utilized to fabricate the aircraft skin, demonstrating high formability and suitability for such applications.

To fulfill the requirements for dependable low-temperature soldering of high-temperature lead-free materials and to inhibit the transformation of Cu₆Sn₅ to Cu₃Sn during transient liquid phase diffusion bonding (TLPB) and aging processes, along with minimizing joint porosity, Cu particles were initially coated with a Ni layer through electroless plating, followed by Sn layer deposition via stannous sulfamate electroplating to achieve Cu@Ni@Sn core-shell structured powder. The study explored the oxidation resistance of Cu@Ni@Sn powder and examined the formation rate and activation energy of Cu₃Sn in both Cu@Sn and Cu@Ni@Sn TLPB joints, clarifying the Ni coating’s role in restricting element migration and phase transformation. The Cu@Ni@Sn TLPB joint displayed a three-dimensional network of intermetallic compounds enveloping Cu particles. This joint demonstrated thermal endurance of at least 400 °C, room temperature shear strength exceeding 80 MPa, and shear strength of no less than 28 MPa after aging at 250 °C for 336 h, representing a 20% improvement compared to the Cu@Sn system. Joint porosity was lowered from 10% in the Cu@Sn system to 5% in the Cu@Ni@Sn system, surpassing both high-lead and nano-sintered Ag systems. The findings illustrate that the introduction of a Ni coating successfully restricted Cu diffusion, suppressed Cu₆Sn₅ phase transformation, decreased void formation, and improved the high-temperature oxidation resistance of Cu@Ni@Sn powder. After undergoing reflow at 250 °C, the Cu@Ni@Sn joint exhibited thermal conductivity of 156 W/m·K and electrical resistivity of 4.2 μΩ·cm, surpassing those observed in the Cu@Sn system, Cu₆Sn₅, and Cu₃Sn. These results imply that Cu@Ni@Sn TLPB joints fulfill the criteria for high-performance interconnections, presenting a viable replacement for high-lead and sintered nano-Ag soldering materials, making them a favorable candidate for high-temperature, high-reliability interconnect applications in power modules.

FeNi36 is employed in aerospace, remote sensing, and mapping applications due to its low coefficient of thermal expansion, making it suitable for high-precision micro-parts. Customizing large FeNi36 parts through wire arc additive manufacturing has emerged as a new research direction. However, the high temperature gradient and cooling rate during the wire arc additive manufacturing process result in a coarse and uneven microstructure, leading to significant anisotropy in the mechanical properties and thermal expansion coefficient of FeNi36, which does not meet high-precision performance requirements. This study presents the development of a macro–micro volume fluid phase field model to simulate heat transfer and dendrite growth during wire arc additive manufacturing FeNi36 thin-walled specimens. The simulation results closely match the experimental data. By comparing the simulation results with the experimental data, the microstructure of wire arc additive manufacturing FeNi36 was optimized, and more suitable process parameters were identified. The mechanical property anisotropy of FeNi36 specimens prepared with these parameters is minimal. The tensile strength of specimens in various directions and positions ranges from 545 to 575 MPa, with elongation between 26.8 and 30.2%. Furthermore, the thermal expansion coefficient curves of the samples in different directions are nearly identical, all being below 2.0 × 10⁻⁶ K⁻¹, which satisfies the commercial FeNi36 requirements.

The temperature field of austenitic stainless steel narrow gap girth weld and nickel-based alloy over-layer is carried out respectively. The microstructure solidification, microsegregation, and microhardness are analyzed by SEM, EDS, and OM. The average microhardness of the austenitic stainless steel at RW, FW, and CW is 207 HV, 208 HV, and 207 HV respectively, the average microhardness at the center of RW, FW, and CW is 191 HV, 190 HV, and 194 HV respectively. Austenitic stainless steel, its girth weld CW and nickel-based alloy over-layer 1 (welding speed v: 0.54 mm/s) boundary position FTZ, is prone to ITZ, martensite zone, hot crack, and decarburization layer, and it is easy to form crack source in ITZ tip area. The nickel-based alloy over-layer 1 is mainly composed of the γ phase, and its grain morphology is various. The columnar crystals and equiaxed crystals in different regions are distributed irregularly, crystal cracks, and hot cracks are prone to occur. At the position of over-layer 2 (welding speed v: 0.54 mm/s) in nickel-based alloy over-layer, it is competitive growth of dendritic crystal zone and cellular crystal zone, epitaxial growth of equiaxed crystal zone and cellular crystal zone. Contact growth of cellular crystal zone and cellular crystal zone is occurred.

The primary objectives of this study are to propose a modified approach to monitor the strength of weld joints in the pulsed metal inert gas welding (P-MIG) process and give a novel contribution to available literature on model selection criteria. The focus is on achieving high ultimate tensile strength (UTS) by considering various welding parameters such as pulse voltage, background voltage, pulse duration, pulse frequency, wire feed rate, welding speed, and root mean square values of welding current and voltage. The study also examines the effects of these parameters on UTS by developing a mathematical model using a hybrid approach that combines artificial neural networks and regression, referred to as the multiple nonlinear neuro-regression method. Forty-seven different models, including linear, trigonometric, logarithmic, quadratic, and rational forms, are proposed to mathematically define the UTS behavior of mild steel plate. A stability analysis assessed the model’s ability to account for the welding process parameters during the modeling phase. This approach has not previously been employed as a criterion for model selection in prior modeling studies. Furthermore, the study includes modified versions of different optimization methods; differential evaluation, random search, simulated annealing, and the Nelder-Mead algorithm simultaneously. The results indicated that the different algorithms converged on the same design, which corresponds to the UTS of 499 MPa. This represents a 7% increment compared to the value reported in the referenced study. Besides, four algorithms presented seven distinct alternative designs. The proposed neuro-regression methodology is expected to be highly effective in accurately defining complex engineering phenomena.