This study reports the fabrication and characterization of aluminium 5052 (Al-5052) alloy foams using TiH2 as a foaming agent processed through friction stir processing (FSP). A multi-pass FSP strategy was utilized to ensure uniform incorporation of TiH2 particles. Results reflected that TiH2 particles were evenly dispersed throughout the Al-5052 matrix using multi-pass FSP, then activated to promote foaming under specific thermal conditions. Phase analysis via XRD confirmed the coexistence of aluminium and titanium phases, while microstructural examinations revealed homogeneous particle dispersion and significant grain refinement. Mechanical characterization illustrated the improvement in tensile strength (from 263.2 MPa to 318.3 MPa) and microhardness (from 85 HV to 103 HV). Foaming heat treatment at 725 °C and 750 °C led to the formation of uniform pore structures, with average pore diameters of 256 μm and 219 μm, respectively. A foam density of 1,266 kg/m³ and porosity of 53.28% were achieved, validating the effectiveness of TiH2-induced gas expansion. These findings establish Al5052/TiH2 foams produced by FSP as a promising pathway for lightweight, high-performance materials suitable for aerospace, automotive, and thermal management applications.

Friction stir consolidation (FSC) is a relatively new solid-state recycling process developed to obtain small billets from metal chips in a unique working step. One of the main unresolved issues in solid-state recycling processes (e.g., Friction Stir Extrusion, Continuous Friction Extrusion, etc.) is the understanding of the effects of the oxide layers initially present on the chips on the final recycled products. In this paper, FSC was conducted on AA 6082 aluminum chips with different rotational speeds of 1000, 1500, 2000, and 2500 rpm. Microstructural evolution was examined using scanning electron microscopy (SEM) integrated with electron backscattered diffraction (EBSD). Backscattered electron (BSE) images showed that fine equiaxed grain structures were developed through the microstructure of the processed samples.

Intermetallic compounds were broken down by the stirring action of the rotating tool, leading to the formation of micro- and nano-sized second-phase particles. The presence of loose high-angle grain boundaries connected to low-angle grain boundaries confirmed the occurrence of continuous dynamic recrystallization (CDRX). Texture analysis revealed the development of simple shear texture components of face-centered cubic (FCC) structured materials with the Oblique Cube component. Hardness measurements revealed that the hardness of the microstructure increased with a greater proportion of low-angle grain boundaries (LAGBs) and higher texture intensity.

Weld quality in friction stir welding (FSW) is difficult to maintain because rapid changes in heat input and material flow can generate transient surface defects during welding. These defects cannot be detected in real time using conventional inspection approaches, resulting in increased inspection time and higher production cost. Real-time visual monitoring is therefore required to support stable and efficient production. This study investigates whether modern convolutional neural network (CNN) models can provide reliable, in-situ segmentation of FSW surface defects together with accurate geometric measurements during welding. A multi-class dataset of weld-surface video frames was created and annotated for flash, burrs, voids, galling, tool interaction, and weld-zone regions. Several CNN-based segmentation models were evaluated, and a lightweight architecture suitable for real-time deployment was selected and integrated with a high-dynamic-range industrial camera on the FSW setup. The system performs continuous segmentation and extracts weld width and defect area from live video at approximately 25 frames per second. Quantitative validation against optical-microscope measurements demonstrated near microscope-level accuracy, with sub-millimetre weld-width deviations and defect-area errors below 6 %. These results demonstrate that real-time visual segmentation can provide reliable weld-quality monitoring in FSW, support early defect detection, and establish a practical foundation for future automated process-control strategies in manufacturing environments. 

This study explores the feasibility of stationary shoulder friction stir welding (SSFSW) for joining dissimilar extruded aluminum alloys, AA6082-T6 and AA6005A-T6, targeted for structural applications in the transportation industry. The key objective is to achieve higher welding speeds, up to 1.5 m/min, which is significantly higher than those typically reported for SSFSW in lap joint configurations. This increased welding speed is expected to reduce heat input, narrow the heat-affected zone (HAZ), and improve the microstructural uniformity and mechanical performance of the weld. Microstructural characterization via optical microscopy, scanning electron microscopy, and electron backscattered diffraction revealed extensive dynamic recrystallization within the stir zone, resulting in a refined average grain size of approximately 2.3 µm. Hardness mapping across the weld cross-section showed a significant reduction in hardness within the stir zone and HAZ, with the minimum hardness dropping to 65 HV from the base material hardness of 110–115 HV. High-speed SSFSW at 1.5 m/min produced sound joints with good lap interface bonding, despite the presence of a small root void. Lap shear tensile testing showed an average ultimate load of 3.8 kN, with all samples failing from the advancing side hook defect due to stress concentration. Fractographic analysis confirmed ductile failure modes with dimples in the fracture surface. These results suggest that high-speed SSFSW (1.5 m/min) is a promising technique for joining dissimilar aluminum alloys in lap joint configurations, offering potential advantages in microstructural refinement and mechanical performance compared to conventional methods. 

Wire Arc Additive Manufacturing (WAAM) has emerged as a promising metal additive manufacturing technique due to its high deposition rate, cost-effectiveness, and ability to build large-scale components. However, challenges such as porosity, poor mechanical properties, limited microstructural control, and residual stress hinder its full potential. Incorporating nanoparticles into the WAAM process has recently gained significant attention as a strategy to enhance material performance. This review provides a detailed and systematic analysis of the various types of nanoparticles used in WAAM, their methods of incorporation, effects on microstructure, mechanical performance, and functional properties of the built components. This review provides the first comprehensive classification and quantitative analysis of nanoparticle incorporation strategies in WAAM, systematically categorising 72 research articles across four distinct deposition strategies, including feedstock modification, interlayer application, direct melt pool injection, and ultrasonic dispersion. This work presents a comparative framework analysing the relative efficacy of different nanoparticle types (carbides, nitrides, and oxides) across multiple alloy systems, revealing that TiC emerges as the most extensively studied reinforcement. The review establishes that nanoparticle addition demonstrates positive influence on yield strength and ultimate tensile strength up to optimal concentrations, beyond which agglomeration-induced property deterioration occurs. Furthermore, the review identifies future perspectives for the optimized integration of nanoparticles in WAAM for high-performance manufacturing, design of multifunctional and hybrid reinforcement strategies, and adoption of AI-driven predictive modeling. The review discusses the industrial adoption barriers of the process. This systematic framework provides practical guidance for nanoparticle selection and process optimization, accelerating the industrial deployment of nanoparticle-reinforced WAAM technology.

Enhancing joint strength in aluminium alloys remains a critical challenge for industrial applications. Friction stir welding (FSW) is a solid-state welding process that offers superior weld quality compared to fusion methods. However, further advancements are needed, particularly for high-performance alloys like third-generation Al-Li. To address this, ultrasonic vibration-assisted FSW (UVaFSW) has been explored as a potential enhancement. This study compares the mechanical and microstructural properties of AA2060-T8-E30 joints produced by FSW and UVaFSW.

Key process parameters, including tool traverse speed and ultrasonic vibration amplitude (7.5 µm and 22.5 µm), were varied to assess their influence on weld quality. Mechanical performance was evaluated through tensile testing and Vickers microhardness, while microstructural characteristics were examined using optical microscopy and SEM. The results demonstrated that UVaFSW significantly improved material flow, reduced asymmetry in the thermo-mechanically affected zone (TMAZ), and refined the grain structure. Consequently, the ultimate tensile strength increased by 16.6 % and 31.8 % at 7.5 µm and 22.5 µm amplitudes, respectively, and elongation reached 11 %, nearly three times that of FSW.

Furthermore, UVaFSW produced finer grains and more uniform precipitate distribution. Therefore, UVaFSW emerges as a promising technique for enhancing weld quality in advanced Al-Li alloys for demanding engineering applications.

FSTs are advanced solid-state processing methods that address the growing industrial demand for lightweight components with enhanced mechanical properties. These techniques, including friction stir welding and friction stir processing, are distinguished by their capability to refine microstructures and improve the quality and longevity of welds and surfaces, making them integral to modern manufacturing. Recent advancements in machine learning (ML) have facilitated the integration of data-driven approaches into FST applications, demonstrating significant potential for optimising performance. This review explores the use of ML in FSTs, highlighting how various ML models improve the prediction of mechanical properties and the optimisation of processing parameters. Findings indicate that ML provides higher accuracy in predictions for FST applications than traditional statistical methods, while hybrid ML techniques further enhance outcomes by refining process control. The review further highlights existing knowledge gaps and proposes directions for future research to enhance ML integration in FSTs. This comprehensive synthesis is drawn from academic literature primarily sourced from the Scopus and Web of Science databases, supplemented by insights from recent books published in the past 15 years.

The wire arc additive manufacturing has gained widespread adoption in recent years for fabricating large-scale components. However, a coarse columnar grain structure, resulting from directional heat dissipation, leads to reduced mechanical properties. The present study proposed a multi-objective optimization with the addition of an inoculant to enhance the mechanical properties of WAAM.

The study aimed to optimize the GMAW-WAAM process variables travel speed (TS), wire feed rate (WFR), and voltage (V) for the bead geometry characteristics bead width (BW), bead height (BH), and bead penetration (BP) on an SS316L substrate. Teacher learning based optimization (TLBO) was used to attain optimal combinations. The two multi-layer structures were then fabricated by considering optimized parameters. The silicon carbide (SiC) nanoparticles were introduced into the molten pool in the fabrication of one wall, and the other wall was fabricated without using SiC.

The microstructural evolution was analyzed using optical microscopy and scanning electron microscopy. The SiC-inoculated samples exhibited more refined grains, resulting from heterogeneous nucleation facilitated by the SiC particles within the molten pool. Mechanical characterization, including tensile testing and microhardness, was performed on both SiC-inoculated and non-inoculated samples.

The results revealed enhanced YS and UTS in the SiC-inoculated specimens by 25.6 % and 8.25 % respectively, primarily due to the refined grain structure.

The present study investigates ultrasonic metal welding to manufacture 10 mm2 copper (Cu) wire joints with different core diameters. The primary purpose of this study is to explore the influence of wire core diameter on the performance of ultrasonic welded joints. Wire core diameter is positively correlated with the peeling resistance of the joint. Superior mechanical properties of the joint are achieved with an increased diameter of the wire core. The peeling strength of the welded joint of two wires with a wire core diameter of 0.25 mm reaches 306.8 N. Examining the welding temperature and assessing the joint’s porosity reveals a significant impact of temperature on porosity. However, relying solely on porosity as a criterion for judging the overall forming quality of joints may be insufficient. Scanning electron microscope and energy-dispersive X-ray elemental analysis revealed that certain wires underwent plastic deformation at elevated temperatures without attaining atomic bonding. Additionally, the welded joint exhibits a compact structure externally and a more relaxed structure internally. The upper side of the joint in contact with the briquette and the lower side in contact with the welding head exhibit minimal gaps, while numerous gaps are evident in the middle of the joint. Furthermore, upon examining the fracture morphology, two distinct failure modes are identified at the joint surface of the conductor. The first involves the fracture of the wire core with a completely separated joint surface, resulting in poor mechanical properties of the joint. The second mode entails the ductile fracture of the wire core at the joint surface, indicating good mechanical properties of the joint. © IMechE 2024.

Friction stir-based techniques (FSTs), originating from friction stir welding (FSW), represent a solid-state processing method catering to the demands of various industrial sectors for lightweight components with exceptional properties. These techniques have gained much more attraction by providing an opportunity to tailor the microstructure and enhance the performance and quality of produced welds and surfaces. While significant attention has historically been directed towards the FSW process, this review delves into the working principles of FSTs, exploring their influence on mechanical properties and microstructural characteristics of various materials. Additionally, emphasis is placed on elucidating the advancement of hybrid FSW processes for both similar and dissimilar metal components, aimed at enhancing welding quality through meticulous control of grain textures, structures, precipitation, and phase transformations. Finally, the review identifies current knowledge gaps and suggests future research directions. This review paper synthesises academic literature sourced from the Web of Science (WoS) and Scopus databases, supplemented by additional sources such as books from the last 15 years.