Search Results (590)

This study investigates the use of YOLOv8 deep learning models to segment and classify thermal images acquired from the rear of the weld pool during the Gas Tungsten Arc Welding (GTAW) process. Thermal data were acquired using a two-color pyrometer under three welding current levels (160 A, 180 A, and 200 A). Models of sizes from nano to extra-large were trained on 66 annotated frames and evaluated with and without data augmentation. The results demonstrate that the YOLOv8m model achieved the best classification performance, with a precision of 83.25% and an inference time of 21.4 ms per frame by using GPU, offering the optimal balance between accuracy and speed. Segmentation accuracy also remained high across all current levels. The YOLOv8n model was the fastest (15.9 ms/frame) but less accurate (75.33%). Classification was most reliable at 160 A, where the thermal field was more stable. The arc reflection class was consistently identified with near-perfect precision, demonstrating the model’s robustness against non-relevant thermal artifacts. These findings confirm the feasibility of using lightweight, dual-task neural networks for reliable weld pool analysis, even with limited training data. Full article

This paper, based on the Johnson–Cook damage model, investigates the damage mechanism of high-strength steel tailor welded blanks (TWBs) (Usibor1500P and Ductibor500) during the forming process. Initially, specimens with varying notch sizes were designed and fabricated to perform uniaxial tensile tests to determine their mechanical properties. Then, the deformation process of the notched specimens was simulated using finite element software, revealing the distribution and variation of stress triaxiality at the fracture surface. By combining both experimental and simulation data, the parameters of the Johnson–Cook (J–C) damage model were calibrated, and the effects of temperature, strain rate, and stress triaxiality on material fracture behavior were further analyzed. Based on finite element analysis, the relevant coefficients for stress triaxiality, strain rate, and temperature were systematically calibrated, successfully establishing a J–C fracture criterion for TWB welds, Usibor1500P, and Ductibor500 high-strength steels. Finally, the calibrated damage model was further validated through the Nakajima-type bulge test, and the simulated Forming Limit Diagram (FLD) closely matched the experimental data. The results show that the analysis based on the J–C damage model can effectively predict the fracture behavior of tailor welded blanks (TWB) during the forming process. This study provides reliable numerical predictions for the damage behavior of high-strength steel laser-customized welded sheets and offers a theoretical basis for engineering design and material performance optimization. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article

Over the past 20 years, laser beam–electric arc hybrid welding has gained popularity, enabling high quality and efficiency standards needed for steel welds in structures subjected to severe working conditions. This process enables single-pass welding of thick components, overcoming issues concerning the individual use of traditional processes based on an electric arc or laser beam. Therefore, thorough knowledge of both processes is necessary to combine them optimally in terms of efficiency, reduced presence of defects, corrosion resistance, and mechanical and metallurgical features of the welds. This article aims to review the technical and metallurgical aspects of hybrid welding reported in the scientific literature mainly of the last decade, outlining possible choices for system configuration, the inter-distance between the two heat sources, as well as the key process parameters, considering their effects on the weld characteristics and also taking into account the consequences for solidification modes and weld composition. Finally, a specific section has been reserved for hybrid welding of clad steel plates. Full article

This paper presents optimization of peak temperatures achieved during friction stir welding (FSW) of Al-6061-T6 alloys. This research work employed a novel approach by investigating the effect of FSW welding process parameters on peak temperatures through the implementation of finite element analysis (FEA), the Taguchi method, analysis of variance (ANOVA), and machine learning (ML) algorithms. COMSOL 6.0 Multiphysics was used to perform FEA to predict peak temperatures, incorporating seven distinctive welding parameters: tool material, pin diameter, shoulder diameter, tool rotational speed, welding speed, axial force, and coefficient of friction. The influence of these parameters was investigated using an L32 Taguchi array and analysis of variance (ANOVA), revealing that axial force and tool rotational speed were the most significant parameters affecting peak temperatures. Some simulations showed temperatures exceeding the material’s melting point, indicating the need for improved thermal control. This was achieved by using three machine learning (ML) algorithms, i.e., Logistic Regression, k-Nearest Neighbors (k-NN), and Naive Bayes. A dataset of 324 data points was prepared using a factorial design to implement these algorithms. These algorithms predicted the welding conditions where the temperature exceeded the melting temperature of Al-6061-T6. It was found that the Logistic Regression classifier demonstrated the highest performance, achieving an accuracy of 98.14% as compared to Naive Bayes and k-NN classifiers. These findings contribute to sustainable welding practices by minimizing excessive heat generation, preserving material properties, and enhancing weld quality. Full article

This study develops an optimized femtosecond laser welding process for joining quartz glass and TC4 titanium alloy (Ti-6Al-4V) under non-optical contact conditions, specifically addressing the manufacturing needs of specialized photoelectric effect research containers. The joint primarily consists of parallel laser-welded zones (WZ) interspersed with base material. The defocus distance of the femtosecond laser predominantly influences the depth and phase composition of the WZ, while the weld spacing influences the crack distribution in the joint region. The maximum shear strength of 14.4 MPa was achieved at a defocusing distance of +0.1 mm (below the interface) and a weld spacing of 40 μm. The XRD stress measurements indicate that the defocusing distance mainly affects the stress along the direction of laser impact (DLI), whereas the weld spacing primarily influences the stress along the direction of spacing (DS). GPA results demonstrate that when the spacing is less than 30 μm, the non-uniform shrinkage inside the WZ induces tensile stress in the joint, leading to significant fluctuations in DS residual stress and consequently affecting the joint’s shear strength. This study investigates the effects of process parameters on the mechanical properties of dissimilar joints and, for the first time, analyzes the relationship between joint residual strain and femtosecond laser weld spacing, providing valuable insights for optimizing femtosecond laser welding processes. Full article

This study joins a 20 mm thick 5000-series aluminum alloy using hot-wire insertion combined with narrow-gap laser welding to evaluate the feasibility and welding characteristics of this technique. The findings indicate that weld formation is primarily influenced by the laser energy density and material deposition rate. A strategy for improving weld beads is introduced incorporating a reoriented laser spot during the final pass on narrow-gap joints. This approach improves penetration and produces defect-free joints. The optimal processing conditions result in complete joint formation with four welding passes. Microstructural analysis reveals that the aluminum matrix morphology evolves according to the local thermal history during welding. Measurements show that the weld region is slightly harder than the base metal, whereas slightly lower hardness is observed at the fusion line and inter-pass boundaries, which correlates with the microstructure result. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

Taking the TPO waterproofing membrane as an example, this paper studies the influence of temperature, speed and welding pressure on the welding quality of a TPO waterproofing membrane lap area through a peel test and a water impermeability test, determines the optimal construction process, and observes and compares the permeable path through laser confocal microscope. Finally, it is applied to the actual effect test in the project. The results show that the welding pressure test tool for the lap area of the waterproofing membrane is designed to meet the welding work test requirements of various lap areas of the waterproofing membrane. The peel strength increases first and then decreases with the increase in welding temperature, and the optimal construction temperature is 400 °C. The optimal construction speed is 4 m/min; at 400 °C welding temperature, the peel strength increases first and then decreases slightly with the increase in welding pressure. The optimal construction pressure is 14.97 N; under the condition of 0.2 MPa, 30 min to 0.6 MPa, 120 min, the water impermeability test of the overlapping area was qualified. In this paper, the optimal construction technology of a TPO waterproofing membrane is determined, which provides guidance for its application and promotion in engineering. Full article

Aluminum alloy, due to its low melting point and high thermal conductivity, deforms and contracts significantly during welding. To mitigate this and achieve full penetration in a single pass, this study uses GTAW (Gas Tungsten Arc Welding) additive manufacturing and optimizes welding groove parameters via the Box-Behnken Response Surface Methodology. The focus is on improving tensile strength and penetration depth by analyzing the effects of groove angle, root face width, and root gap. The results show that groove angle most significantly affects tensile strength and penetration depth. Hardness profiles exhibit a W-shape, with base material hardness decreasing and weld zone hardness increasing as groove angle rises. Root face width reduces hardness fluctuation in the weld zone, and an appropriate root gap compensates for thermal expansion, enhancing joint performance. The interaction between root face width and root gap most impacts tensile strength, while groove angle and root face width interaction most affects penetration depth. The optimal welding parameters for 7xxx aluminum alloy GTAW are a groove angle of 70.8°, root face width of 1.38 mm, and root gap of 0 mm. This results in a tensile strength of 297.95 MPa and penetration depth of 5 mm, a 90.38% increase in tensile strength compared to the RSM experimental worst group. Microstructural analysis reveals the presence of β-Mg₂Si and η-MgZn₂ strengthening phases, which contribute to the material’s enhanced mechanical properties. Fracture surface examination exhibits characteristic ductile fracture features, including dimples and shear lips, confirming the material’s high ductility. The coexistence of these strengthening phases and ductile fracture behavior indicates excellent overall mechanical performance, balancing strength and plasticity. Full article

The research presented herein investigates the impact of axial force and feed rate in the Friction Stir Welding (FSW) of aluminum alloy 6061-T6 in a GROB G552 horizontal 5-axis CNC machine with adaptive torque control enabled. The purpose of this study is to further advance the performance and characteristics of FSW aluminum alloys in 5-axis CNCs, particularly in conjunction with adaptive torque control. The Taguchi and ANOVA methods were utilized to define parameter tables and analyze the resulting data. Optical microscopy and tensile tests were performed on the welded samples to evaluate weld quality. The results from this study provide clear evidence that axial force has a significant effect on tensile strength in FSW AA6061-T6. The maximum UTS found in this study, welded with an axial force of 9.4 kN, retained 69% tensile strength of the base material. Conversely, a decrease in strength and an increase in void formation was found at higher feed rates with this force. Ideal welds, with minimal defects across all feed rates, were performed with an axial force of 8.3 kN. A feed rate of 300 mm/min at this force resulted in a 67% base metal strength. These findings contribute to improving joint strength and application efficiency in FSW AA6061-T6 performed in a horizontal 5-axis CNC machine where adaptive torque control is enabled. Full article

Alloy 625 is a widely utilized nickel-based superalloy known for its excellent mechanical strength and corrosion resistance in aggressive environments. However, its high niobium (Nb) content can lead to the formation of detrimental phases, such as Laves and MC carbides, during welding processes, compromising the mechanical integrity and long-term performance of the weld overlay. This review systematically examines recent research findings on the implications of reducing Nb content in Alloy 625 weld overlays, particularly with respect to microstructure evolution, mechanical behavior, and corrosion performance. Key advancements, including the understanding of segregation behavior, solidification paths, and secondary phase formation, are presented based on recent studies. This paper aims to provide a discussion on the trade-offs and future directions for optimizing Alloy 625 weld overlay claddings through Nb content modification. Full article

Arc stud welding differs from conventional arc welding techniques and is widely used for joining structural steel, stainless steel, aluminum, and copper alloys in various configurations. Achieving a reliable stud weld requires appropriate welding parameters and a suitable process selection, considering factors such as stud diameter, base material, and surface condition. This study experimentally compares three arc stud welding techniques—arc welding with a ceramic ferrule (ARC CF), arc welding with shielding gas (ARC SG), and arc welding assisted by a radially symmetric magnetic field (ARC SRM)—applied to structural steel (1.0038) and stainless steel (1.4301). Macrostructural analysis, Vickers hardness testing (HV10), visual inspection, non-destructive testing, and bend tests were performed to evaluate weld quality. Results show that ARC CF achieved the highest penetration and hardness but produced more spatter. ARC SG provided moderate penetration but was more prone to cold welds, while ARC SRM resulted in the cleanest collars with minimal spatter but shallower penetration. All welds met ISO 5817:2014 Quality Level C, confirming acceptable structural integrity. These findings support informed selection and optimization of stud welding techniques for diverse engineering applications. Full article

Laser weld quality remains a critical priority across nearly all industries. However, identifying optimal laser parameter sets continues to be highly challenging, often relying on costly, time-consuming trial-and-error experiments. This difficulty is largely attributed to the severe fluctuations and instabilities inherent in laser welding, particularly keyhole instabilities. This study examines the impact of laser power modulation parameters, which, when properly applied, have been found effective in controlling and minimizing process instabilities. The investigated parameters include different pulse shapes (sinusoidal and cosinusoidal) and their associated characteristics, namely frequency (100–800 Hz) and amplitude (1000–4000 W). The impact of these modulation parameters on keyhole mode laser spot welding performance in aluminum is investigated. Using a Taguchi experimental design, a series of tests were developed, focusing on eight key welding responses, including keyhole dimensions, mean temperature, and the variability of instability-inducing forces and related factors affecting process stability. Grey relational analysis (GRA) combined with analysis of variance (ANOVA) is applied to identify the optimal combinations of laser parameters. The results indicate that low amplitude (1000 W), low to intermediate frequencies (100–400 Hz), and cosinusoidal waveforms significantly enhance weld quality by improving process stability and balancing penetration depth. Among the factors, amplitude has the greatest impact, accounting for over 50% of the performance variation, followed by frequency and pulse shape. The findings provide clear guidance for optimizing laser welding parameters to achieve stable, high-quality aluminum welds. Full article

In deformation measurements, processing overexposed images poses challenges due to the welding process or metal reflection. To track the deformation surface, an Adaptive Brightness Global Digital Image Correlation method is proposed. First, the effective range is determined based on the extent of image overexposure. Second, an improved Dark Channel Prior method is employed to adjust the brightness of overexposed images. Third, by calculating the parameter results of Finite Element Partitioning, Adaptive Brightness Global Digital Image Correlation can be utilized to conduct deformation measurements. The proposed method can adjust both the image brightness and Finite Element Partitioning for Global Digital Image Correlation. The experimental results demonstrate that the improved dark channel method modifies the image brightness without altering its brightness distribution. The modified image can significantly increase the Mean Intensity Gradient within different partitions. This method overcomes the difficulty in measuring the weld deformation during the welding process and can achieve dynamic deformation measurement using overexposed images. Finally, the evolution processes of unstable deformation and angular deformation in the whole welding field are obtained, which can assist in optimizing the welding process. Full article