Search Results (699)

Solid-state welding processes, particularly friction-stir welding (FSW), offer significant advantages for joining different aluminum alloys due to their good mechanical performance, energy efficiency, and cost-effectiveness. The FSW of the AA5052-H32 and AA6261-T6 alloys has not been previously reported. In this study, the effects of the main FSW process parameters on the mechanical behavior of different AA5052/AA6261 alloy joints were systematically investigated. A full factorial experimental design was applied, considering the tool rotation speed (900–1800 rpm) and the welding speed (56–252 mm/min) as control factors, along with their ratio (Rs/Ws). The results of the tensile tests reveal that the joint strength is strongly affected by the interaction between the rotation and welding speeds, with the Rs/Ws ratio is identified as a key parameter governing material flow, plastic deformation, and defect formation. The maximum tensile strength, approximately 198 MPa, corresponding to a mechanical efficiency of 84.4%, was achieved at 1800 rpm and 7 rev/mm, a condition that favored effective material mixing and a defect-free interfacial bond (≈162–186 MPa). The microhardness profiles showed a minimum of approximately 40–50 HV within the TMAZ, on the advancing side. In general, clear quantitative relationships were established between the process parameters and the mechanical properties, which allowed for the identification of optimal operating conditions to produce high-quality FSW joints between the dissimilar materials AA5052/AA6261. Full article

Friction stir welding (FSW) of dissimilar aluminium alloys often results in non-uniform microstructure and hardness distributions due to asymmetric temperature fields and material flow. The objective of this study is to establish a quantitative relationship between thermal history, microstructural evolution, and hardness distribution in dissimilar AA5052-H32/AA6061-T6 FSW joints by combining experimental characterisation with validated thermal modelling. AA5052-H32 and AA6061-T6 plates were welded under five different parameter sets. A thermal finite element model was developed in COMSOL Multiphysics to simulate temperature evolution during welding and was validated using embedded thermocouple measurements, with predicted peak temperatures ranging from 455 °C to 641 °C. Optical microscopy, scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) were employed to characterise grain structure and dynamic recrystallisation (DRX) behaviour, while Vickers microhardness mapping was used to evaluate the local mechanical response. The results show that DRX occurred in the nugget zone (NZ), leading to significant grain refinement, with a minimum grain diameter of 6.07 µm, representing an approximately eightfold reduction compared with the base material AA5052-H32. In contrast, the thermo-mechanically affected zone (TMAZ) experienced limited recrystallisation due to insufficient plastic deformation and temperature. The lowest hardness was observed in the TMAZ on the AA5052-H32 side, with the hardness reduction of 22% primarily caused by work hardening loss. Hardness was also reduced by 34% on the AA6061-T6 side due to decreased precipitation strengthening caused by high temperatures. This combined experimental–numerical study provides a systematic thermal–microstructure–hardness framework for understanding and predicting local property variations in dissimilar FSW joints. Full article

Friction stir welding (FSW) is a critical solid-state joining process for lightweight and high-performance metallic structures, particularly in aerospace and automotive manufacturing, yet conventional implementations remain largely dependent on offline parameter optimization and open-loop control. The purpose of this review is to examine how Industry 4.0 technologies enable the transition of FSW from a parameter-driven process into an intelligent, adaptive, and increasingly autonomous manufacturing capability. A structured review methodology was employed, including systematic literature selection and synthesis of recent research on smart sensing, industrial internet of things (IIoT), data analytics, machine learning, digital twins, automation, robotics, and human–machine interaction in FSW. The review reveals that Industry 4.0 integration enables real-time process monitoring, predictive quality assurance, closed-loop control, and virtual process optimization, resulting in improved weld quality, reliability, productivity, and scalability. Significant benefits are observed for safety-critical aerospace components and high-throughput automotive production, where adaptability and consistency are essential. However, persistent challenges remain in data standardization, model generalization, real-time digital twin integration, interoperability, cybersecurity, and workforce readiness. This review concludes that addressing these challenges through interdisciplinary research, standardization efforts, and human-centered system design is essential for enabling adaptive and data-driven FSW systems. The findings position intelligent FSW as a foundational technology for smart, resilient, and sustainable metal manufacturing in the Industry 4.0 era. Full article

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal and harmonic analyses, confirming a strong longitudinal resonance near 27.9 kHz with a tip amplitude of about 46 µm. A 27-run factorial experiment varied tool rotation (600–900 rpm), welding speed (45–55 mm/min), and plunge depth (0.10–0.25 mm). Welded joints were assessed using tensile strength and Vickers hardness. Four predictive models, support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANNs), and multiple linear regression (MLR) were trained and compared under five-fold cross-validation. The best joint quality was obtained at 900 rpm, 55 mm/min, and a 0.25 mm plunge depth, yielding a tensile strength of 188.7 MPa and a hardness of 102 HV. Overall, MLR provided the strongest predictive performance while remaining interpretable (UTS R² = 0.81, RMSE = 11.84 MPa; hardness R² = 0.67, RMSE = 2.36 HV), matching the ANN for UTS prediction and outperforming the ANN, GPR, and SVR for hardness. A coupling physics-based ultrasonic design with an interpretable predictive model offers a practical route to reduce trial and error, improve parameter selection, and accelerate the process development for ultrasonic vibration-assisted FSW of aluminium alloys; however, modest models can outperform complex ones when the dataset is limited. Full article

The complex flow behavior of the metal around the stirring tool during welding directly determines the microstructural evolution, defect formation, and mechanical properties of the welded joint, and thus becomes the core physical process affecting welding quality and process stability. In this study, to characterize the three-dimensional material flow behavior of AZ31 magnesium (Mg) alloy during friction stir welding (FSW), conventional metallographic sectioning was adopted as the primary observation method, and copper foil was used as the marker material. The flow trajectories of the materials after welding were investigated via three configurations of the marker material. The results indicate that three typical characteristic zones exist along the vertical direction, which are the shoulder-affected zone (SAZ), the pin-affected zone (PAZ), and the swirl zone from top to bottom. Specifically, the material in the SAZ is dominated by laminar flow; the PAZ exhibits complex mixed-flow characteristics; while the swirl zone shows an obvious rotational flow pattern. Based on the principles of material mechanics and fluid mechanics, a force-flow coupled “simple flow model around a rotating cylinder” was proposed, which defines three flow modes corresponding to the different characteristic zones within the weld. 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

Friction stir-welding (FSW) is widely recognized as a modern solid-state technology used to join dissimilar materials by solid-state mechanical mixing. Such mechanical mixing can be exploited to fabricate in situ composite structures through solid-state deformation mechanisms. The present investigation highlights the microstructural evolution and mechanical properties of an in situ composite structure fabricated by FSW of aluminum (Al) to titanium (Ti) incorporating a thin Nickel (Ni) interlayer. A 0.1 mm thick Ni foil was placed across the full butt interface between 4 mm thick Al and Ti plates before friction stir-welding. Properties of the composite were investigated in detail, and the results revealed that fragmented Ti and Ni particles of different sizes were consolidated in the weld nugget. Al, on the other hand, exhibited substantial microstructural refinement and developed an equiaxed microstructure with random grain orientation, mixed grain boundaries and low micro-strain accumulation in the weld nugget. At the processing temperature, Al reacted with both Ti and Ni to form multiple intermetallic compounds. Tensile testing indicated that the tensile properties of the weld were close to those of the base aluminum. This retention of mechanical properties in spite of recrystallization is attributed to the following mechanisms: (1) Ti and Ni undergo severe deformation, forming fine particles with varying sizes and shapes; (2) at particle interfaces, diffusion and chemical reactions produce interlayers and intermetallic compounds; (3) these particles are consolidated within dynamically recrystallized Al, imparting composite characteristics to the weld nugget; and (4) the particles containing intermetallic compounds act as dispersoids in the Al matrix. Quantitatively, the weld retained 98% (104.2 ± 3.3 MPa) UTS and 90% (17.1 ± 1.2) ductility of base aluminum, demonstrating the effectiveness of the Ni interlayer approach in controlling brittle intermetallic formation. Full article

This paper examines the use of high-rigidity Tau-robots in friction stir welding, where process loads are very high. The rigidity of Tau-robots increases at the expense of the workspace. Therefore, the right configuration of the Tau-robot is sought to reconcile rigidity and workspace requirements. This is studied by use of kinematics, followed by static and modal analysis. In particular, by extending an existing kinematic model employing free vectors, the robot workspace was derived in non-dimensional parametric form and was then maximized through evolutionary optimization. However, finite element static and modal analysis that were carried out subsequently may prove, as in a case demonstrated here, that the optimized configuration may not withstand high loads, typically axial forces of 15 kN and torques of 80 Nm, and it may also be susceptible to forced vibrations in the typical spindle rotation range up to 3000 rpm. As a rectification measure, it was shown how a modified configuration by placing robot kinematic chain bases further apart and shortening robot links achieves higher rigidity, axial displacement being reduced by one or two orders of magnitude to below 1 mm and increases critical modal frequency 3 to 5 times depending on the workspace position, of course sacrificing part of the workspace, i.e., reducing it 3-fold to enclose welding lines in a rectangle of dimensions 700 × 800 mm. In the quest for the appropriate robot configuration desired dimensions of parts to be welded and available standard components are briefly considered, too. Full article

Welding copper (Cu) and aluminum (Al) is highly demanded for lightweight and cost-effective manufacturing. However, it faces significant challenges. First, substantial differences in physical properties may lead to high residual stresses and distortion. Second, brittle intermetallic compounds (IMCs) readily form at the interface, severely compromising the joint’s mechanical properties and electrical conductivity. Third, the native oxide film on Al impedes effective wetting and bonding. Therefore, effective control over the interfacial microstructure of the welded joint is essential. This review provides a critical analysis and comparison of several typical welding techniques, including laser welding (LW), friction stir welding (FSW), ultrasonic welding (UW), brazing and soldering, and welding–brazing. These analyses focus on their process characteristics, joint microstructures, and corresponding formation mechanisms. Furthermore, this review synthesizes key strategies for enhancing joint quality, including process parameter optimization, introduction of functional interlayers, and external assistance, aimed at optimizing joint microstructure and minimizing defects. Based on the analysis, this work provides comparative insights into process selection and microstructure control, and highlights future directions: advancing novel methods such as magnetic pulse welding and transient liquid phase bonding; developing intelligent real-time process control to suppress brittle IMCs and associated defects; promoting sustainable practices and establishing standardized performance evaluation; and systematically investigating long-term reliability to support the industrial application of robust Cu/Al joints. Full article

As a solid-state joining technology, friction stir welding (FSW) exhibits significant advantages for joining aluminium alloys, including low heat input and minimal formation of intermetallic compounds, thereby enhancing joint quality and mitigating deformation. This study investigates the single-sided and double-sided FSW processes of 3 mm thick 7075-T6 aluminium alloy sheets, focusing on characterising the microstructure and mechanical properties of the joints. Experimental results show that at a rotational speed of 1500 rpm and a welding speed of 80 mm/min, the double-sided co-directional FSW joint achieves a tensile strength of 388 MPa and an elongation of 7.09%, significantly outperforming those of the other two welding paths. In the weld nugget zone (WNZ), continuous dynamic recrystallization (CDRX) occurs, generating uniformly refined equiaxed grains (average size: 1.10 μm) and facilitating the transformation of low-angle grain boundaries (LAGBs) to high-angle grain boundaries (HAGBs). Meanwhile, the strong rotated cube texture is remarkably weakened and replaced by random recrystallized brass textures with the lowest kernel average misorientation (KAM) value in the WNZ. In contrast, the thermo-mechanically affected zone (TMAZ) accumulates a high density of LAGBs due to welding-induced plastic deformation. Microhardness testing reveals a typical “W”-shaped distribution: WNZ hardness is relatively high but slightly lower than that of the base metal (BM), and the minimum hardness of the advancing side (AS) of the heat-affected zone (HAZ) is higher than that of the retreating side (RS). This study confirms that double-sided co-directional FSW crucially regulates microstructural evolution and improves the mechanical properties of 7075-T6 aluminium alloy joints, providing a viable process optimisation strategy for high-quality welding of thin-gauge sheets. Full article

Additive manufacturing (AM) is a significant contributor to Industry 4.0. However, one considerable challenge is usually encountered by AM due to the bed size limitations of 3D printers, which prevent them from being adopted. An appropriate post-joining technique should be employed to address this issue properly. This study investigates the influence of key friction stir butt welding (FSBW) factors (FSBWFs), such as tool rotational speed (TRS), tool traverse speed (TTS), and pin profile (PP), on the weldability of 3D-printed PLA–Chromium (PC) composites (3PPCC). A filament containing 10% by weight of chromium reinforced in PLA was used to prepare samples. The material extrusion additive manufacturing process (MEX) was employed to prepare the 3D-printed PCC. A Taguchi-based design of experiments (DOE) (L9 orthogonal array) was employed to systematically assess weld hardness (WH), weld temperature (WT), weld strength (WS), and weld efficiency. As far as the 3D-printed samples were concerned, two distinct infill patterns (linear and tri-hexagonal) were also examined to evaluate their effect on joint performance; however, all other 3D printing factors were kept constant. Experimentally validated findings revealed that weld efficiency varied significantly with PP and infill pattern, with the square PP and tri-hexagonal infill pattern yielding the highest weld efficiency, i.e., 108%, with the corresponding highest WS of 30 MPa. The conical PP resulted in reduced WS. Hardness analysis demonstrated that tri-hexagonal infill patterns exhibited superior hardness retention, i.e., 46.1%, as compared to that of linear infill patterns, i.e., 34%. The highest WTs observed with conical PP were 132 °C and 118 °C for both linear and tri-hexagonal infill patterns, which were far below the melting point of PLA. The lowest WT was evaluated to be 65 °C with a tri-hexagonal infill, which is approximately equal to the glass transition temperature of PLA. Microscopic analysis using a coordinate measuring machine (CMM) indicated that optimal weld zones featured minimal void formation, directly contributing to improved weld performance. In addition, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were also performed on four deliberately selected samples to examine the microstructural features and elemental distribution in the weld zones, providing deeper insight into the correlation between morphology, chemical composition, and weld performance. Full article

Fatigue crack propagation in friction stir welded joints significantly affects aircraft structural integrity. This study investigates the influence of welding speed, rotational speed, specimen thickness, loading frequency, and stress ratio on the fatigue crack growth rate. Four classical machine learning models with different structures—Deep Back-Propagation Network, Random Forest, Support Vector Regression, and K-Nearest Neighbors—were employed to predict fatigue crack growth behavior. The results show that all models achieve strong predictive performance. For FSWed joints, Deep BP and KNN exhibit comparable performance (R² > 0.98) on the training data, indicating similar learning capabilities with sufficient data coverage. Notably, KNN achieves the fastest training time (<0.3 s), while all models require less than 5 s of computation time. These results demonstrate that machine learning-based models provide an efficient and reliable alternative for rapid fatigue crack growth evaluation, supporting damage-tolerant design and structural integrity assessment in aircraft engineering. Full article

Friction stir welding (FSW) of high-strength aluminum alloys, including EN AW 7020-T651, encounters significant challenges under weld line gap conditions, leading to compromised joint integrity. This study develops a predictive, data-driven framework to assess and optimize the gap bridgeability performance of FSW joints with weld line gaps ranging from 0 to 4 mm in 2 mm thick plates. A structured experimental matrix was implemented, systematically varying rotational speed, welding speed, axial force, and tool shoulder diameter. To promote stable material flow and consistent weld quality under varying gap conditions, a multi-pin tool was employed throughout the welding trials. This configuration supported defect-free weld formation across a broad process window and contributed to improved weld soundness under gap conditions. Weld quality was evaluated using a comprehensive, multi-criteria approach that required (i) defect-free joints verified by visual and cross-sectional (metallographic) inspection, (ii) an ultimate tensile strength of at least 230 MPa, and (iii) a novel metric termed weak area percentage (WAP). Derived from micro-hardness mapping, WAP quantified the proportion of the heat-affected zone (HAZ) exhibiting hardness below 96 HV, providing a more robust and spatially sensitive measure of mechanical integrity than conventional average hardness values. Two machine learning models, Logistic Regression and Random Forest, were trained to classify weld acceptability. The Random Forest model demonstrated superior performance, achieving 92.5% classification accuracy and an F1-score of 0.90. Feature importance analysis identified the interaction terms “welding speed × gap size” and “rotational speed × gap size” as the most influential predictors of weld quality. Full article

This study investigated the dynamic behavior of AA7075 plates joined by Friction Stir Welding (FSW), focusing on the influence of key process parameters, rotation, and traverse speeds, on the resulting dynamic characteristics. Experimental Modal Analysis (EMA) was performed under free boundary conditions to determine resonance frequencies, mode shapes, and damping ratios, revealing that an increase in traverse speed consistently led to a decrease in natural frequencies across most modes, thereby indicating reduced joint stiffness attributed to insufficient heat input. Furthermore, localized weld defects caused significant damping variations, particularly in low-order modes. To complement the experimental findings and enable simultaneous, multi-output prediction of these coupled dynamic parameters, a Co-Active Neuro-Fuzzy Inference System (CANFIS) model was developed. The CANFIS architecture utilized spindle speed and feed rate as inputs to predict natural frequency and damping ratio for multiple vibration modes as tightly coupled outputs. The trained model demonstrated strong agreement and high predictive accuracy against the EMA experimental data, with convergence analysis confirming its stable learning and excellent generalization capability. The successful integration of EMA and CANFIS establishes a robust hybrid framework for both physical interpretation and intelligent, coupled prediction of the dynamic behavior of FSW-welded AA7075 plates. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article