Search Results (756)

In industrial inspection, X-ray detection methods are the mainstream approach for non-destructive testing (NDT) of weld defects. In response to the issues of insufficient detection accuracy and slow detection speed in existing X-ray weld defect detection (WDD) methods, a lightweight X-ray WDD model, AFD-YOLOv10, based on an improved YOLOv10n, is proposed. First, by introducing variable kernel convolution (AKConv) to replace traditional convolution in the backbone network, the model better adapts to the multi-scale variations in weld defects while maintaining its lightweight nature. Second, a lightweight C2f-Faster module is incorporated into both the backbone and neck networks to achieve a more symmetrical and efficient feature flow, reducing the model’s computational complexity and achieving lightweight design. Finally, dynamic upsampling (DySample) is added to the neck network to enhance the model’s detection accuracy for targets of different scales. This combination of innovations strikes an effective symmetry between model complexity, inference speed, and detection performance. Experimental results show that the improved AFD-YOLOv10 model achieves accuracies, recall rates, and mean average precision values of 90.7%, 88.8%, and 93.8%, respectively, on five typical X-ray weld defects, representing improvements of 4.9%, 4.1%, and 5.3% over the YOLOv10n baseline model, with a 10.1% reduction in model parameters and a 13.3% increase in detection speed. Compared with other existing mainstream detection methods, the AFD-YOLOv10 model not only improves the accuracy of X-ray WDD but also achieves model lightweighting, demonstrating overall detection performance superior to other mainstream algorithms, thus meeting the industrial production requirements for X-ray WDD. Additionally, generalization experiments conducted using a public dataset of surface defects in steel validate the good generalization performance of the AFD-YOLOv10 model. Full article

Printed circuit board (PCB) assembly, on the basis of surface mount electronic component welding, is one of the most important electronic assembly processes, and its defect detection is also an important part of industrial generation. The traditional two-stage target detection algorithm model has a large number of parameters and the runtime is too long. The single-stage target detection algorithm has a faster running time, but the detection accuracy needs to be improved. To solve this problem, we innovated and modified the YOLO11n model. Firstly, we used the Retention Block (RetBlock) to improve the C3K2 module in the backbone, creating the RetC3K2 module, which makes up for the limitation of the original module’s limited, purely convolutional local receptive field. Secondly, the neck structure of the original model network is fused with a Multi-Branch Auxiliary Feature Pyramid Network (MAFPN) structure and turned into a multi-branch auxiliary neck network, which enhances the model’s ability to fuse multiple scaled characteristics and conveys diverse information about the gradient for the output layer. The improved YOLO11n model improves its mAP50 by 0.023 (2.5%) and mAP75 by 0.026 (2.8%) in comparison with the primitive model network, and detection precision is significantly improved, proving the superiority of our proposed approach. Full article

TC4 titanium alloy and 5083 aluminum alloy with different groove shapes were joined by laser-MIG hybrid welding–brazing using ER4043 filler wire. The effects of groove shape on the weld formation, intermetallic compounds and tensile property of the Ti/Al butt joints were investigated. The welds without obvious defects could be obtained with grooves of I-shape and V-shape on Ti side, while welds quality with grooves of V-shape on Al side and V-shape on both sides were slightly worse. The interfacial intermetallic compounds (IMCs) on the brazing interface were homogeneous in the joints with groove of V-shape on Ti side, and V-shape on both sides, which had similar thickness and were both composed of TiAl₃. Unlike the IMCs mainly composed of TiAl₃ at the I-shape groove interface, TiAl₃, TiAl, and Ti₃Al constituted the IMCs at the V-shape on Al side interface. The average tensile strength of Ti/Al joints with groove of I-shape was the highest at 238 MPa, and was lowest at 140 MPa with groove of V-shape on Al side. The tensile samples mainly fractured at IMCs interface and the fractured surfaces all exhibited mixed brittle–ductile fracture mode. Based on the above research results, I-shape groove was recommended for laser-arc hybrid welding–brazing of 4 mm thick Ti/Al dissimilar butt joints. Full article

This study investigates the effect of welding speed on the microstructure and mechanical properties of pulsed laser lap-welded 0.2 mm 316L stainless steel sheets, commonly used in fuel cell bipolar plates. Welding speeds ranging from 6 to 26 mm/s were tested while other laser parameters remained constant. Results show that increasing welding speed reduces heat input, overlap factor, and weld dimensions. A transition from full to partial penetration occurs beyond 6 mm/s, with no visible heat-affected zone. The weld microstructure features columnar ferrite near fusion boundaries and globular ferrite in the center. Tensile–shear tests reveal that welds maintain higher strength than the base metal up to 22 mm/s, with all fractures occurring in the base material. An optimal speed range of 10–14 mm/s ensures defect-free joints with improved mechanical performance. These findings provide practical guidance for thin-gauge stainless steel welding in fuel cell applications. Full article

Automated and robotic welding have become standard practices in manufacturing, requiring precise control to maintain weld quality without relying on skilled welders. In Tungsten Inert Gas (TIG) welding, monitoring the weld pool is crucial for ensuring the necessary weld penetration, which is vital for maintaining weld integrity. Real-time observation is essential to prevent defects and improve weld quality. Various sensing technologies have been developed to address this need, with vision-based systems showing particular effectiveness in enhancing welding quality and productivity within the framework of Industry 4.0. This review looks at the latest technologies for monitoring weld pools and bead shapes. It covers methods like using Complementary Metal-Oxide Semiconductors (CMOS) to take clear images of the melt pool for better process identification, Active Appearance Model (AAM) to capture 3D images of the weld pool for accurate penetration measurement, and Charge-Coupled Devices (CCD) and Laser-Induced Breakdown Spectroscopy (LIBS) to analyze plasma spectra and create material composition graphs. Full article

Defects formed during the thermite welding process of two sections of rails require the welded joints to be inspected for quality, and the most used non-destructive method for inspection is radiography testing. However, the conventional defect investigation process from the obtained radiography images is costly, lengthy, and subjective as it is conducted manually by trained experts. Additionally, it has been shown that most rail breaks occur due to a crack initiated from the weld joint defect that was either misclassified or undetected. To improve the condition monitoring of rails, the railway industry requires an automated defect investigation system capable of detecting and classifying defects automatically. Therefore, this work proposes a method based on image processing and machine learning techniques for the automated investigation of defects. Histogram Equalization methods are first applied to improve image quality. Then, the extraction of the weld joint from the image background is achieved using the Chan–Vese Active Contour Model. A comparative investigation is carried out between Deep Convolution Neural Networks, Local Binary Pattern extractors, and Bag of Visual Words methods (with the Speeded-Up Robust Features extractor) for extracting features in weld joint images. Classification of features extracted by local feature extractors is achieved using Support Vector Machines, K-Nearest Neighbor, and Naive Bayes classifiers. The highest classification accuracy of 95% is achieved by the Deep Convolution Neural Network model. A Graphical User Interface is provided for the onsite investigation of defects. Full article

Spatters in the powder-based metal additive manufacturing processes influence deposition quality, part surface quality, and internal defects. We developed a novel video analysis method to monitor and count the melted spatter particles of biomedical-grade Co-Cr-Mo-4Ti powder particles in depositing layers using a micro-plasma transferred arc additive manufacturing (M-PTAAM) process. We captured the spatters using a weld-monitoring camera and building datasets of videos and monitored different combinations of M-PTAAM process parameters. We captured videos of the melted spatter particles and counted the melted spatter particles in real time using a Kalman filter. The weld-monitoring camera captured the melted spatter particles and the fumes generated by the evaporated spatter particles. The video processing algorithm was developed in this study to accurately capture melted spatter particles. In images without fumes, nearly all melted spatter particles were successfully detected. Even in images with the presence of fumes, the algorithm maintained a detection accuracy of 90%. The real-time melted spatter count particle exhibited a powder feed rate changing from 30 to 35 g/min and then to 50 g/min. The melted spatter particle count was lowest at a powder feed rate of 30 g/min and increased with the increasing powder feed rate. Full article

The demand for electric mobility has driven the development of advanced laser welding technologies such as dual beam welding and beam shaping. Nevertheless, some intrinsic characteristics present challenges to exploring all its benefits. In this sense, this study investigates the effect of the laser welding parameters employed on the weld quality in busbar–battery interconnects. Dual beam and beam shaping strategies were applied in Al-Al (AA1050 H24) and Al-Cu (AA1050 H24 and C11000) overlap joint configurations adopting statistical methods. For Al-Al joints, welding speed was the most significant parameter influencing interface width, whereas in Al-Cu joints, core power was the only significant parameter affecting both interface width and penetration in the studied configuration. Common defects, such as porosity and cracks, were observed in both material combinations. In Al-Al joints, higher welding speeds resulted in up to a 16% (65.6 HV) increase in hardness, while, in Al-Cu joints, the peak value reached around 900 HV in the interface zone due to the formation of intermetallic compounds (IMCs). In addition, IMCs with complex structures and significant compositional variations, including Cu₉Al₄ and CuAl₂ were identified. Full article

Surface roughness plays a vital role in determining surface integrity and function. Surface irregularities or reduced quality near the surface can contribute to material failure. Surface roughness is considered a crucial factor in estimating the fatigue life of structures welded by FSW. This study attempts to provide a deeper understanding of the nature of the surface formation and roughness of aluminum joints during FSW processes. In order to form more efficient joints, the frictional temperature generated was monitored until reaching 450 °C, where the transverse movement of the tool and the joint welding began. Hardness and tensile tests showed that the formed joints were good, which paved the way for more reliable surface roughness measurements. The surface roughness of the weld joint was measured along the weld line at three symmetrical levels using welding parameters that included a rotational speed of 1250 rpm, a welding speed of 71 mm/min, and a tilt angle of 1.5°. The average hardness in the stir zone was measured at 64 HV, compared to 50 HV in the base material, indicating a strengthening effect induced by the welding process. In terms of tensile strength, the FSW joint exhibited a maximum force of 2.759 kN. Average roughness (Rz), arithmetic center roughness (Ra), and maximum peak-to-valley height (Rt) were measured. The results showed that along the weld line and at all levels, the roughness coefficients (Rz, Ra, and Rt) gradually increased from the beginning of the weld line to its end. The roughness Rz varies from start to finish, ranging between 9.84 μm and 16.87 μm on the RS and 8.77 μm and 13.98 μm on the AS, leveling off slightly toward the end as the heat input stabilizes. The obtained surface roughness and mechanical properties can give an in-depth understanding of the joint surface forming and increase the ability to overcome cracks and defects. Consequently, this approach, using adaptive thresholding image processing coupled with grayscale histogram analysis, yielded significant understanding of the FSW joint’s surface texture. Full article

Friction Stir Welding (FSW) is a solid-state joining technique that has gained widespread adoption, particularly for aluminum alloys, due to its ability to produce high-quality welds without melting base materials. This comprehensive review focuses on the influence of process parameters on weld characteristics and performance. Compared to conventional fusion welding methods, FSW offers notable advantages, including superior mechanical properties, fewer defects, enhanced corrosion resistance, and lower environmental impact. The review also addresses key challenges such as tool wear, precise process control, and complications arising from welding dissimilar alloys. By synthesizing recent developments and case studies, this work outlines current limitations and proposes future directions for optimizing the FSW process to expand its applicability in critical engineering sectors. Full article

To address the issue of the low precision in detecting defects in aluminum alloy weld seam digital radiography (DR) images using the current target detection algorithms, a modified algorithm named YOLOv8-ELA based on YOLOv8 is proposed. The model integrates a novel HS-FPN feature fusion module, which optimizes the parameter efficiency and enhances the detection performance. For better identification of small defect features, the CA attention mechanism within HS-FPN is substituted with the ELA attention mechanism. Additionally, the first output layer is enhanced with a SimAM attention mechanism to improve the small target recognition. The experimental findings indicate that, at a 0.5 threshold, the YOLOv8-ELA model achieves mean average precision (mAP@0.5) values of 93.3%, 96.4%, and 96.5% for detecting pores, inclusions, and incomplete welds, respectively. These values surpass those of the original YOLOv8 model by 1.4, 2.3, and 0.1 percentage points. Overall, the model attains an average mAP of 95.4%, marking a 1.3% improvement over its predecessor, confirming its superior defect detection capabilities. Full article

This study evaluates and compares the effectiveness of friction stir welding on aluminium and copper alloys with the aim of improving the strength of the resulting joints. The rotary friction stir welding method was chosen for its ability to produce high-quality joints with minimal deformation. This study explores various welding parameters, such as rotating speed, welding speed, and tool design, and their impact on the mechanical properties of the joint, including tensile strength, hardness, and microstructure of the weld region. The findings show that the optimum parameters for aluminium and copper alloys differ significantly: the tensile strength of aluminium is around 240 MPa, while copper joints require careful adjustment to avoid defects, reaching around 220 MPa. Aluminium showed improved joint strength with higher rotating speed and welding speed parameters, while copper required more precise parameter adjustments to prevent cracking and other defects. The results of this study provide practical guidance for selecting appropriate rotary friction welding parameters to optimize joint strength in aluminium and copper alloys, which can enhance the application of these materials in industry. Full article

High-speed friction stir welding (HSFSW) has emerged as a promising technique for improving the manufacturing efficiency of aluminum alloy structures by enabling faster welding while maintaining the quality of welded joints. This study investigates the mechanical properties and microstructural characteristics of AA 7020-T651 aluminum alloy joints welded using a novel multi-pin tool at high feed rates ranging from 2500 to 6000 mm/min under a constant rotational speed of 4000 rpm. Defect-free welds were successfully fabricated, as confirmed by metallographic analysis and micro-computed tomography (µ-CT). The multi-pin tool facilitated consistent material flow and heat distribution, which contributed to reliable joint formation across all feed rates. At the highest feed rate, the tensile strength reached 76% of the base material. A consistent softening in the nugget zone (NZ) was observed, and electron backscatter diffraction (EBSD) analysis showed a more than 70% grain size reduction in this zone, averaging ~3 µm, due to dynamic recrystallization. These findings underscore the suitability of HSFSW with multi-pin tools for high-speed industrial applications, offering enhanced productivity without compromising structural integrity. Full article

Real-time detection of welding defects in resistance spot welding is a complex challenge. Dynamic resistance (DR) reflects nugget growth and varies with defect types, serving as a key indicator. This study presents an online quality evaluation and defect classification method for Q235 low-carbon steel welding. Welding current and voltage were collected in real-time, and DR signals were processed employing a second-order Butterworth low-pass filter featuring zero-phase processing to enhance accuracy. Empirical mode decomposition (EMD) decomposed these signals into intrinsic mode functions (IMFs) and residuals, which were classified by a support vector machine (SVM). Experiments showed the EMD-SVM method outperforms traditional approaches, including backpropagation (BP) neural networks, SVM, wavelet packet decomposition (WPD)-BP, WPD-SVM, and EMD-BP, in identifying four welding states: normal, spatter, false, and edge welding. This method provides an efficient, robust solution for online defect detection in resistance spot welding. Full article

A total of 0.3%C-Cr-Mo-V steel, a high-strength alloy steel widely used in rocket motor housings, suspension systems in high-performance vehicles, etc., is noted due to its high strength-to-weight ratio. However, its high carbon equivalent (CE > 1%) makes it challenging to weld, as it is prone to brittle martensitic formation, which increases the risk of cracking and embrittlement. The present paper focuses on enhancing the microstructure and mechanical properties of 0.3% C-Cr-Mo-V steel by gas tungsten arc welded (GTAW) joints, utilizing post-weld heat treatment and cooling strategies (PWHTCS). A systematic experimental approach was employed to ensure a defect-free weld through dye penetrant testing (DPT) and X-ray radiography techniques. Subsequently, test specimens were extracted from the welded sections and subjected to PWHT protocols, including hardening, tempering, and rapid quenching using air and oil cooling (AC and OC, respectively) mediums. Results show that OC has enhanced tensile strength and hardness while simultaneously maintaining and improving ductility, ensuring a well-balanced combination of strength and toughness. Fractography analysis revealed ductile fracture in AC samples, whereas OC weldments exhibited a mixed ductile–brittle fracture mode. Thus, the findings demonstrate the critical role of PWHTCS, with OC, as an effective method for achieving enhanced mechanical performance and microstructural stability in high-integrity applications. Full article