Search Results (891)

The growing need for lightweight, multifunctional, and high-performance structures in the automotive, aerospace, electronics, and medical industries has driven the development of advanced joining technologies for polymers and metal-polymer combinations. Among these, ultrasonic welding (USW) and friction stir spot welding (FSSW) have emerged as promising solid-state techniques capable of producing reliable joints with minimal thermal degradation and enhanced interfacial bonding. This review focuses on recent developments in USW and FSSW of thermoplastics, fiber-reinforced composites, and hybrid metal–polymer systems, with a particular emphasis on process mechanics, microstructural evolution, and joint performance. The mechanisms of heat generation, material flow behavior, and consolidation are discussed in relation to key process parameters, including applied pressure, rotational speed, vibration amplitude, plunge depth, and dwell time. Microstructural transformations such as polymer chain orientation, recrystallization, interfacial diffusion, and defect formation are analyzed to establish process–structure–property relationships. Mechanical performance metrics, including lap shear strength, fatigue resistance, impact behavior, and environmental durability, are critically compared across different materials and welding methods. Furthermore, recent advances in numerical and thermo-mechanical modeling, in situ process monitoring, and data-driven optimization are discussed to highlight pathways toward predictive and scalable manufacturing. Current industrial applications and existing limitations such as challenges in automation, thickness constraints, and hybrid material compatibility are also evaluated. Finally, key research gaps and future directions are identified to improve joint reliability, sustainability, and broader industrial adoption of advanced solid-state welding technologies. Full article

As a solid-state welding technique, friction stir welding (FSW) has many advantages over conventional fusion welding. Its applications in the manufacturing and joining of parts in aerospace, automotive, and shipbuilding have significantly increased. Friction heat generation is the fundamental driver of the FSW process. It governs material flow, microstructural evolution, mechanical properties, and residual stresses. Understanding the effect of heat generated on the joint quality is essential for process parameter optimization, ensuring defect-free welds and high-quality joints. Thus, evaluating the thermal history of the FSW process is a key requirement for effective analysis. This comprehensive review critically discusses research studies published over the past three decades (1991–2025) that have examined different approaches to predict and measure heat generation in FSW. A total of 136 highly relevant articles were selected from the Scopus database and systematically analyzed. The effects of various welding parameters on heat generation, microstructural evolution, and joints’ mechanical properties have been reported. Different heat generation prediction and measurement techniques, such as analytical models, finite element models (FEM), and experimental methods have been discussed in terms of their feasibility, accuracy, advantages, disadvantages, and cost. The evolution, state of the art of analytical models and FEM over the last three decades are analyzed and future research directions are outlined. Finally, the correlation between process parameters, heat generated, microstructural development, and mechanical performance of the welded joints for various workpiece materials is investigated. This review provides a critical and comparative perspective that highlights the strengths and limitations of each method, offering practical guidance for researchers and industry practitioners. Full article

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

Ensuring the reliability of solder joints is essential for stable operation in electric vehicle chargers, particularly for components assembled using surface-mount technology. Therefore, we developed a defect inspection system for welding joint defects using a Faster Region-based Convolutional Neural Network model to classify results as insufficient defect, shifting defect, and normal (pin-qualified) on voltage control IC pins. The model was trained on 72,000 pin samples and achieved a training accuracy of 99.93%. Evaluation of 65,700 pin samples resulted in an accuracy of 98.89%. The experimental results demonstrate that the system provides stable recognition of reflective solder joints, reliably identifies critical pin-level defects, and is suitable for deployment in practical inspection environments. 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

To address the challenge of detecting internal defects in medium-thick titanium alloy laser welds, a combined simulation and experimental study on ultrasonic testing was conducted. A finite element model employing a 5 MHz shear wave angle transducer for inspecting titanium alloy welds was established. An ultrasonic testing system was developed, incorporating a DPR300 pulser-receiver (JSR Ultrasonics, Pittsford, NY, USA) and an MSO5204 oscilloscope (RIGOL, Suzhou, China), and was calibrated using standard reference blocks. The inspection results for four prefabricated internal defects at various depths demonstrated that all defects were effectively detected, with the minimum detectable equivalent defect size reaching 1 mm. The measured signal-to-noise ratio (SNR) averaged 17.6 dB, validating the high sensitivity of the proposed system. The mean absolute error for defect localization was 0.438 mm, achieving a positioning accuracy better than 0.5 mm. This study indicates that the pro-posed method enables effective detection and accurate localization of internal defects in titanium alloy laser welds, providing critical technical support for laser welding quality assessment. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article

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

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

The fabrication of NiTiZr alloys by solid-state routes remains challenging due to limited atomic diffusion and the high reactivity of Ti and Zr. Mechanical alloying offers a potential pathway for synthesising such systems; however, complete alloy formation is not always achieved under practical milling conditions. Researchers have infrequently explored the mechanical alloying of NiTiZr, and this study systematically investigates the effect of milling time on microstructural evolution rather than claiming complete alloy synthesis. A high-energy planetary ball mill was used to mechanically process elemental powders of Ni, Ti, and Zr for 5–28 h. The examination revealed that longer milling times resulted in progressive crystallite refinement and increased lattice strain, while particle morphology evolved from irregular to more globular shapes due to repeated fracture and cold welding. After 28 h of milling, limited reacted regions containing Ni, Ti, and Zr were observed (~4.6% area fraction), while most of the powder remained heterogeneous and polyphasic, with no evidence of complete Ni₅₀Ti₃₀Zr₂₀ alloy formation. X-ray diffraction showed significant peak broadening without systematic 2θ peak shifts, indicating severe plastic deformation and crystallite refinement rather than definitive solid-solution formation of the allot. Differential scanning calorimetry revealed exothermic thermal events between 300 °C and 470 °C, which are attributed to defect recovery and thermally activated structural rearrangements rather than confirmed martensitic or crystallisation transformations. These results demonstrate that high-energy ball milling alone is effective for particle size reduction and defect generation but insufficient for producing a fully homogeneous Ni₅₀Ti₃₀Zr₂₀ alloy within 28 h. Additional activation energy, such as post-milling heat treatment or extended processing, is required to promote complete alloying in this system. Full article

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. 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

Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data analyses is still necessary to fully realize a monitoring system. This work proposes a two-stage deep learning (DL) approach for automated ultrasonic data analysis for RSW processing monitoring. The first stage conducts semantic segmentation on ultrasonic M-scan welding process signatures, yielding masks for identified molten pool and stack regions from which weld penetration measurements can be directly extracted, as well as expulsion occurrences throughout welding. From input images and segmentation outputs, the second stage directly estimates resultant weld nugget diameters using an additional neural network. Both stages leveraged architectures based on TransUNet, mixing elements of both convolutional neural networks (CNN) and vision transformers, and the effect of cross-attention for stack-up sheet thickness data fusion was investigated via an ablation study. Additionally, in the diameter estimation stage, the ablation study included alternative feature extraction architectures in the network and investigated the provision of M-scans to the model alongside segmentation masks. In both cases, cross-attention was determined to improve performance, and in the case of diameter estimation, providing M-scans as input was found to be beneficial in general. With cross-attention, the segmentation approach yielded a mean intersection over union (IoU) of 0.942 on molten pool, stack, and expulsion regions in the M-scans with 13.4 ms inference time. With cross-attention, diameter estimates yielded a mean absolute error of 0.432 mm with 4.3 ms inference time, representing a significant improvement over algorithmic approaches based on ultrasonic time of flight. Additionally, the approach attained >90% probability of detection (POD) at 0.830 mm below the acceptable diameter threshold and <10% probability of false alarm (PFA) at 0.828 mm above the threshold. These results demonstrate a novel production-ready application of DL in ultrasonic nondestructive evaluation (NDE) and pave the way for zero-defect RSW manufacturing. Full article

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

Fatigue failure constitutes an issue that cannot be ignored when designing welded steel structures due to the initiation of cracks at weld toes and defects under cyclic loading conditions. Traditional methods, such as the notch stress approach, estimate fatigue life by modeling local stress distributions using idealized weld geometries. While these methods are widely accepted in design codes, they can be limited by complexity and reduced accuracy in real-world applications. This study explores the use of artificial neural networks (ANNs) to enhance fatigue life prediction through data-driven modeling. The proposed method involves training an ANN using synthetic data generated through finite element simulations of S355 steel weldments under various loading histories, rates, and frequencies. The objective is to capture the influence of local geometric and stress features without relying solely on assumptions used in conventional approaches. The FEM-based training data incorporate both classical experimental findings and validated modeling practices. While performance evaluation of the ANN model is reserved for future work, this study lays the groundwork for replacing or supplementing the notch stress approach with a more adaptable and efficient predictive tool. The integration of machine learning into fatigue assessment has the potential to improve reliability, reduce computational burden, and support more informed maintenance and design decisions. Full article