Search Results (1,706)

To address the challenge of autonomous process adaptation in non-standard components with continuously varying groove angles, this study proposes an intelligent decision-making framework based on Response Surface Methodology (RSM) for oscillation welding. Instead of solely identifying a single optimal parameter set, RSM is employed as a knowledge-modeling tool to reveal adaptive relationships between groove geometry and key welding parameters. A Central Composite Design (CCD) is utilized to establish predictive models for weld geometry under varying conditions: wire feed rate (8–12 m/min), travel speed (5–9 mm/s), travel angle (70–110°), oscillation amplitude (2–6 mm), dwell time (0.2–0.6 s), and groove angle (80–100°). The significance and adequacy of the models are validated through analysis of variance (ANOVA), demonstrating high predictive accuracy with all coefficients of determination (R²) exceeding 0.82. Furthermore, defect-aware physical constraints derived from the formation mechanism of bottom humping are incorporated into the optimization process, specifically restricting the travel angle to a push angle of 70–85° to ensure feasible and reliable decision outputs. Based on the established response surfaces, geometry-dependent parameter selection rules are derived to simultaneously optimize root penetration (target 8.5–10.5 mm) and sidewall fusion (>2.5 mm) for groove angles ranging from 80° to 100°. Experimental validation confirms that the proposed decision-making strategy achieves stable bead formation and defect-free fusion, demonstrating high quantitative reliability with root penetration prediction errors below 7% and bead width errors below 13%. This work bridges the gap between geometric perception and process control, providing a practical pathway toward intelligent and adaptive robotic welding of non-standard components. Full article

Welding is a fundamental technique for joining materials in industrial applications and large-scale construction. Various methods are employed to ensure robust connections. Resistance spot welding is ideal for thin sheets due to its speed, low cost, short processing times, and easy integration into automation systems. Stainless steel is widely used in many food and beverage industries because of its durability and ability to withstand diverse conditions. However, despite the existence of modeling approaches, predictive models linking weld parameters to the simultaneous improvement of stiffness and tensile strength in different joint regions remain limited in published studies. Many studies treat the weld as a single homogeneous region or focus primarily on general indicators such as tensile strength or weld diameter. The spatial variation in properties between the weld region, the heat-affected region, and the base metal is often not modeled separately. This study examines the effect of welding current and welding time on the mechanical properties of weld beads. Scanning electron microscopy (SEM) was also used to characterize the weld microstructure. The combination of mechanical evaluation and microstructural analysis provides deeper insight into the relationship between welding parameters and weld quality. Among the conditions studied (6–8 kA, 60–120 ms), the optimal parameters (6 kA, 120 ms) produced the maximum hardness of 178.16 HV observed in the weld zone and a tensile strength of 12 kN. The experimental results demonstrated that welding parameters significantly influence weld bead quality, and the optimization study allowed us to identify the parameters that achieve the best possible mechanical properties and optimal operating conditions. The experimental results demonstrated that welding parameters significantly influence weld bead quality, and the optimization study using Response Surface Methodology (RSM) allowed us to identify the parameters that achieve the best possible mechanical properties and optimal operating conditions. Full article

Particle-reinforced aluminum matrix composite (AMC)/steel hybrid structures present considerable benefits for lightweight design and enhanced product performance. This article provides a systematic overview of research advances from 2003 to 2024 on laser welding of particle-reinforced AMCs to steel, with particular emphasis on the influence of laser welding parameters, shielding gas, and reinforcing particles on the mechanical properties of the welded joints. The mechanisms by which intermetallic compounds (IMCs) impair joint strength are thoroughly analyzed. Moreover, the effects of rare earth element additions on both mechanical properties and corrosion resistance of the joints are critically assessed, along with the coupling mechanism between rare earth elements and the reinforcement phase. Key insights from the literature reveal that regulating heat input can effectively suppress harmful interfacial reactions. Meanwhile, the synergistic incorporation of rare earth elements not only refines the grain structure and boosts mechanical strength, but also improves corrosion resistance through the formation of dense surface oxide films and grain boundary strengthening. This review underscores the innovative integration of interfacial reaction control with rare earth microalloying to achieve high-performance AMC/steel laser-welded joints—a distinct departure from prior studies that typically investigated these strategies separately. Full article

Friction stir welding (FSW) is a key technology for manufacturing T-shaped thin-walled structures and avoiding fusion welding defects. However, the quantitative relationship between its process parameters and the microstructure properties of the joint remains unclear. To address this, this study established regression models via response surface methodology (RSM) relating rotational speed (w), welding speed (v), and plunge depth (h) to the mechanical properties of T-joints. The optimal process parameters (400 rpm, 60 mm/min, 0.21 mm) were determined, under which the ultimate tensile strength (UTS) and weld nugget hardness (WNH) of the joint reached 74.1% (377 MPa) and 94.4% (153 Hv) of the base materials (BM) respectively, with v showing the most significant influence on joint mechanical properties. Microstructural observations revealed that from the BM to the stirring zone (SZ), the grains underwent a continuous evolution from coarsening, partial recrystallization to complete dynamic recrystallization (DRX). In the SZ, due to severe plastic deformation and high heat input, the continuous dynamic recrystallization (CDRX) was the dominant mechanism, and the grain was significantly refined. The heat input in the thermomechanical affected zone (TMAZ) is relatively low, mainly geometric dynamic recrystallization (GDRX). DRX-driven grain refinement was the primary strengthening factor in the joint, with hardness closely related to grain size. However, thermal cycling induced softening in the heat-affected zone (HAZ) and promoted the precipitation of brittle compounds such as Al₃Mg₂ and MgZn₂, which caused crack initiation exhibiting intergranular brittle fracture. Subsequently, under stress drive, it extends to SZ, mainly characterized by ductile fracture. Full article

This study investigates the transient wheel–rail contact mechanics of welded joints in heavy-haul rails via a validated 3D finite element model, and analyzes the stick-slip behavior, dynamic response and elastoplastic characteristics in the base material zone, heat-affected zone and weld bead zone. Results show a distinct contact state transition from stick-slip in the base material to predominant slip within the welded zones, indicating higher wear susceptibility. Dynamic response analysis reveals the highest and lowest contact-point acceleration amplitudes in the base material and heat-affected zone, respectively, due to material heterogeneity. Plastic deformation consistently initiates at the rail surface, where stress and strain concentrate, establishing it as the primary site for damage nucleation. A systematic parametric study shows that plastic deformation can be effectively mitigated by increasing the yield strength and elastic modulus of the welded joint material, or reducing the wheelset velocity, unsprung mass and wheel–rail friction coefficient. In contrast, adjusting the primary suspension and fastener parameters exerts a negligible influence on plastic deformation control. These findings provide a mechanistic basis for optimizing the performance and maintenance of welded joints in heavy-haul rail operations. This study reveals the coupling law of multiple mechanisms among contact behavior, dynamic response and material failure during the damage initiation process of rail welded joints from the mechanistic perspective, which provides a theoretical basis for the structural optimization, condition assessment and maintenance of rail welded joints in heavy-haul railways. Full article

The objective of this article is to optimise the deposition modes and the content of exothermic additions (EAs) in the core filler in Fe-C-Cr-Ti with Cu additions hardfacing. To achieve this, JMatPro Release 7.0, Sente Software Ltd., 2016 material characterisation software was used to simulate and calculate the equilibrium phase structure and composition of the Fe-C-Cr-Ti-Cu alloy during the welding thermal cycle. A synergistic approach combining the Taguchi–Analysis of Variance (ANOVA)–Factorial design (FD) method with the standard hybrid Taguchi–ANOVA–Principal Component Analysis (PCA)–Grey Relational Analysis (GRA) is used and justified to optimise factors and develop mathematical models for parameters in the L9 orthogonal experimental design. The study examines how the transfers of deoxidisers depend on the content of exothermic additions in the cored wire filler (EA) and the contact tip-to-work distance (CTWD), while the behaviour of carbide formers is influenced by wire feed speed (WFS) and present arc voltage at the power source (U_set). The research specifically investigates the Fe-C-Cr-Ti-Cu system and the role of copper in stabilising austenite. Findings show that high Cu concentrations (7 wt.%) enhance hardenability by 13%, effectively suppressing pearlite transformation and expanding the bainite region. The desired chemical composition of the deposited metal is determined by the distribution of selected factors, as measured by the transfer coefficients of each element. Full article

Shop-floor weld inspection may appear to be a solved problem until a camera is deployed near a galvanized-sheet MIG welding line. The seam reflects light, the texture changes from frame to frame, and the defects of interest are often small and visually subtle. Additionally, the hardware near the line is rarely a data-center GPU. With those constraints in mind, this paper presents YOLO-MIG, a compact detector built on YOLOv10n for weld-seam inspection in practical production conditions. We make three focused changes to the baseline: a C2f-EMSCP backbone block to better preserve weak defect cues with modest parameter growth, a BiFPN neck to keep small-target information alive during feature fusion, and a C2fCIB head to clean up predictions that otherwise get distracted by seam edges and illumination artifacts. On a workshop-collected dataset containing 326 original images, with the training subset expanded through augmentation to 2608 labeled samples in total, YOLO-MIG achieves 98.4% mAP@0.5 and 56.29% mAP@0.5:0.95 on the test set while remaining lightweight (1.83 M parameters, 3.87 MB FP16 weights). Compared with YOLOv10n, the proposed model improves mAP@0.5 by 9.36 points and mAP@0.5:0.95 by 4.89 points, while reducing parameters, GFLOPs, and model size by 43.4%, 19.9%, and 29.9%, respectively. The results suggest that YOLO-MIG is not only accurate but also realistic to deploy at the edge for intelligent weld quality control. Full article

Efficient operation of robotic manipulators in repetitive industrial tasks, such as welding and logistics sorting, requires careful coordination of obstacle representation and motion planning. Traditional methods, such as axis-aligned bounding boxes, generate overly conservative trajectories, while highly detailed models impose excessive computational burden, both increasing cumulative energy consumption in long-duration operations. This paper presents an adaptive sphere-based obstacle modeling framework integrated with energy-aware motion planning for repetitive manipulation tasks. The proposed method employs an improved Whale Optimization Algorithm with nonlinear parameter adjustment and elite guidance mechanisms to generate compact sphere representations through adaptive voxelization. Experimental validation using a 6-DOF UR5 manipulator demonstrates substantial performance improvements over conventional AABB models, achieving 31–66% energy reduction and 12.5–37% shorter configuration-space paths, with competitive modeling efficiency (2.63–3.34 s) compared to 11 metaheuristic algorithms. The framework provides a systematic methodology for integrating obstacle modeling with motion planning, particularly suitable for applications where cumulative energy savings are critical in repetitive operations. Full article

Conventional reinforced concrete exhibits weaknesses such as poor ductility, limited load-bearing capacity, and complex reinforcement detailing in beam-column joints. To expand its application scope, this study proposes two novel types of L-shaped and T-shaped concrete-filled special-shaped steel columns, with steel skeletons fabricated from either square steel tubes or H-shaped steel sections, based on built-up steel welding construction. A total of eight column compression tests were conducted under both axial and eccentric loading conditions. The main conclusions are as follows: All specimens exhibited failure modes characterized by external concrete cracking or spalling; axial compression specimens primarily developed vertical cracks, whereas eccentric compression specimens exhibited diagonal cracks. The use of normalized loads enabled comparison across columns with different numbers of limbs. The square steel tube columns demonstrated superior compressive performance compared to the H-shaped steel columns, with an average increase of 17.6%. Eccentric loading resulted in significant performance degradation across all four column types, with the T-shaped columns being particularly affected, exhibiting a 40.9% reduction in normalized load, while the L-shaped columns showed a relatively smaller reduction of 22.1%. Furthermore, a quadratic function fitting method was employed to analyze the stiffness degradation curves of all specimens, effectively capturing the stiffness degradation patterns with minimal dispersion and satisfactory fitting accuracy. Systematic parametric analysis revealed that the curve parameters are primarily governed by the initial stiffness and the displacement corresponding to complete stiffness degradation. Full article

Carbon fiber-reinforced thermoplastic (CFRTP) composites are now widely used in many fields. Ultrasonic welding (UW) is a key technology for joining these materials. The control mode of UW has a great effect on the quality of the welded joints. However, there is still not enough research comparing the different welding control modes. This paper investigates the effects of the time control, energy control, and displacement control modes on the ultrasonic welding quality of carbon fiber-reinforced polycarbonate (CF/PC). A flat PC film is used as the energy director (ED). The evaluation focuses on the lap-shear strength (LSS), macro- and micro-morphology, fracture surface characteristics and power–displacement curves of the welding process. Furthermore, significant differences are observed in the temperature field evolution and joint failure modes across the different control modes and process parameters. Results indicate that the displacement control mode achieves the highest joint quality and process stability, yielding a maximum LSS of 30.6 MPa. A correlation analysis reveals that the displacement–energy relationship exhibits the strongest coupling, and the Pearson correlation coefficient r is 0.896. Full article

This paper proposes a simplified mechanical model to estimate transverse shrinkage and angular distortion in multi-pass butt welding. The simplified mechanical model is first derived for an I-groove joint by representing the heated weld region with one-dimensional bar elements and by enforcing force equilibrium to obtain closed-form expressions for pass-by-pass deformation increments and cumulative deformation. For non-I-groove joints, the same simplified mechanical model is applied by updating the layer partition and geometric parameters for each pass based on the pass-wise high-temperature region; the inherent shrinkage of each pass is evaluated from the heat input and an equivalent heated-layer thickness. The simplified mechanical model is validated for V-groove multi-pass joints by comparison with thermo-elastic-plastic finite element (FE) analyses and available experimental data, and for X-groove multi-pass joints by comparison with thermo-elastic-plastic FE analyses. In addition, a parametric study on the V-groove angle (40°–70°) for SUS316L demonstrates that the model captures the increasing trend of final transverse shrinkage with groove angle without a pronounced degradation in prediction accuracy. The results show that the simplified mechanical model reproduces both deformation histories and final values with good accuracy while using only a small set of input parameters and negligible computational cost, making it useful for early-stage welding procedure planning and quick parameter studies. Full article

Weld root reinforcement is a critical geometric parameter governing stress concentration and structural performance in thin-walled stainless-steel piping systems designed to ASME B31.3. While current codes specify permissible dimensional limits, they do not explicitly quantify how incremental variations in root height influence stress distribution under realistic service loading conditions. This study integrates finite element analysis (FEA) with experimentally validated GTAW weld profiles to evaluate the structural influence of weld root height in 316L stainless-steel pipe joints. An experimentally manufactured 4 in schedule 10S joint with a measured root height of less than 1.5 mm was adopted as the baseline geometry. Additional models with reinforcement heights of 1.138, 2.0, 2.5, and 3.0 mm were evaluated under two representative load cases: (i) internal pressure combined with drag and axial thrust (LC-1), and (ii) internal pressure with thrust only (LC-2). The results demonstrate that reinforcement heights exceeding 2.0 mm increase von Mises, hoop, longitudinal, and radial stress gradients, with peak stresses shifting toward the weld toe under drag-inclusive loading. In contrast, reinforcement ≤2 mm provides smoother load transfer and reduced stiffness discontinuity across the weld interface. The combined numerical and experimental findings support a stress-informed upper limit of 2 mm for weld root reinforcement in thin-walled stainless-steel pipelines, offering a performance-based complement to existing dimensional acceptance criteria. Full article

In this study, the Nd:YAG laser process was employed with preselected welding parameters and varying initial welding temperatures (including room temperature, 10 °C, and 0 °C) for spot welding of (Zr₅₃Al₁₇Co₂₉)Nb₁ bulk metallic glass. Following welding, the microstructure—including the parent material, heat-affected zone (HAZ), and weld fusion zone (WFZ)—as well as the microhardness, thermal properties, and corrosion resistance of the welds, were systematically investigated. Owing to the low glass-forming ability of the alloy, a small amount of Zr₆CoAl₂ phase was observed within the amorphous matrix at the center of the bulk metallic glass cast plate. After the laser welding, sub-micron or nanoscale Zr(Al_xCo_1−x)₂ phases have formed in the HAZ of all welded samples, which significantly influenced the microhardness, thermal properties, and corrosion resistance in this region. As the initial welding temperature decreased, both the volume fraction and the density of the Zr(Al_xCo_1−x)₂ phase were reduced. Notably, for the weld performed at the lowest initial temperature of 0 °C, small crystalline phases were detected only at approximately 70 μm below the surface of the HAZ. To clarify the effect of IWTs on corrosion resistance, welded samples were immersed in 6 M HCl at 35 °C for 72–120 h. Surface morphologies after corrosion were examined by SEM in the PM, HAZ, and WFZ. No evident pitting was detected after 72 h of immersion. After 120 h, pitting corrosion was observed on the HAZ surfaces of welds subjected to RT and 10 °C IWTs, whereas no obvious pitting was found at an IWT of 0 °C. The pit size and density in the HAZ increased with increasing IWT. In contrast, no pitting was observed in the WFZ under any IWT condition. Full article

Wire and arc additive manufacturing is a promising technology for fabricating large and complex metallic components. Wire arc methods, like MIG and MAG, use an electric arc to melt and deposit metal wire layer-by-layer. The improvement of the surface depends on the multi-bead overlapping model. However, the high quality of multi-layer deposits is reduced by structural irregularities, such as geometric defects, poor fusion, and reduced mechanical properties of the weld bead. The analysis of a single weld bead that solidifies on a base material can be carried out to improve the geometry of the microstructure, to improve the mechanical properties, and to understand the relationship between welding parameters and the bead dimensions. In the present study, current metal welding technologies and strategies in wire-arc additive manufacturing were discussed, and different weld bead geometries using BÖHLER SG2 solid wire were realized, varying the robot’s trajectory length and welding speed. The computational models are proposed to create a dependence between the controllable welding input parameters and resulting geometrical weld bead outputs (width, height, length, and radius) for prediction and optimization. These models, using techniques such as support vector machines and artificial neural networks, can be a good tool for controlling quality by understanding these input–output relationships. However, the SVM has revealed a superior performance based on metrics for the nonlinear and intricate relationships between the geometrical weld beads and welding parameters. Full article

Accurate and real-time weld seam recognition is critical for automated welding systems in intelligent manufacturing. However, existing deep learning-based models often suffer from high computational complexity and limited real-time performance, which restrict their deployment in embedded and industrial environments. To address these challenges, this paper proposes a lightweight weld seam segmentation framework based on an optimized SGD-YOLO (Segmentation-guided Ghost Dynamic YOLO) architecture, aiming to achieve a favorable balance between accuracy and efficiency. By redesigning the network structure and enhancing feature extraction capability, the proposed model significantly reduces computational cost while maintaining high detection precision. Experiments demonstrate that the proposed method achieves a 36.5% reduction in floating-point operations and a 29.4% decrease in parameter size compared with conventional models, enabling stable real-time performance under industrial conditions. Furthermore, feature point extraction experiments show that the pixel localization error is controlled within 5 pixels and the mean depth error remains below 0.5 mm, indicating high robustness and measurement accuracy. These results confirm the effectiveness of the proposed framework in precise weld seam perception and geometric feature extraction. Overall, the proposed lightweight weld seam segmentation approach provides a practical and efficient solution for real-time welding automation, promoting the broader application of deep learning techniques in intelligent manufacturing and industrial robotics. Full article