Welding in the World最新文献

In this study, the correlation between dynamic resistance during the first 10 ms of welding time and the electrode surface condition in resistance spot welding of 5182 aluminum alloy has been investigated. The electrode surface rapidly degrades due to contamination and morphological changes, adversely affecting the weld spot surface. The accumulation of Cu-Al intermetallic phases on the electrode surface alters its roughness, leading to variations in dynamic resistance. By analyzing this correlation, optimal electrode milling intervals were identified to extend electrode life. This work focused on detecting crater formation on the electrode surface through dynamic resistance monitoring. The results indicate that resistance measurements provide a reliable approach for evaluating electrode wear, optimizing maintenance schedules, and reducing material removal during milling.

Directed energy deposition-gas metal arc (DED-GMA) process has recently gained considerable attention due to its inherent capability to produce large metallic components, with moderate complexity, at substantially high deposition rate compared to other additive manufacturing techniques. The effect of wire feed rate, energy input per unit length and orientation on the tensile and low cycle fatigue behaviour of multi-layer builds of low-carbon steel ER70S-6 is systematically studied in the present work. In addition, a detailed microstructural characterization is also carried out for better understanding of the microstructural evolution during deposition and its influence on the mechanical behaviour of the build. In general, insignificant variation of the tensile properties of DED-GMA specimens at different orientations signifies an overall isotropic behaviour. The vertically oriented samples, printed at highest energy input, show superior fatigue life. The number of cycles to failure, for the vertically oriented samples, at highest wire feed rate of 10 m/min and deposition travel speed of 1 m/min, are found to be around 718, 450 and 366 at strain amplitudes of ± 0.6, ± 0.8 and ± 1.0%, respectively. It is envisaged that the control of energy input by adjusting wire feed rate and deposition travel speed is crucial to improve the tensile and fatigue properties of the build.

In the underlying study, a method has been proposed to automatically extract finite element (FE) peak stresses of welded components to alleviate human errors and increase the calculation accuracy. The approach is based on the K-means and DBSCAN (density-based spatial clustering of applications with noise) methods as the unsupervised machine learning approaches. Data points, in this case, nodal coordinates and their corresponding stress magnitudes, are grouped within different clusters. The peak stress in each dense region (cluster) is then highlighted and reported automatically. Parametric and comparative studies have also been carried out in order to detect optimised parameters of the K-means and DBSCAN algorithms. The methodology will ultimately be used for more reliable stress analysis in fatigue assessment of welded structures.

The present study focuses on friction welded joints between Al6063-T6 and SS304L pipes with different compositions that were examined to understand how rotational speed affected the evolution of the interfacial microstructure. A comprehensive study was done on the various microstructures, grain boundaries, kernel angle misorientation, and joint strength. Under the influence of rotational speed, there was a change in the metal flow, microstructures, and grain morphology at the welded area. However, the microstructure of Al6063-T6 altered from elongated to equiaxed grains due to dynamic recrystallization, and the grain size reformed from 2.10 to 2.63 μm, while SS grain size from 1.68 to 2.42 μm obtained with variation of the rotational speed. The distribution of grain misorientation angle at the interface was varied from 31 to 45° with the different rotational speeds. Al6063-T6 exhibited significantly greater misorientations and wider variances in their scattering compared to SS304L. The ongoing dynamic recrystallization was observed for the grain refinement on the Al6063-T6 side of the interface, whereas dynamic recovery was noted on the SS304L side. The texture intensity of Al6063-T6 and SS304L varies owing to the rotation speed. The various rotational speed plays a dominant role in the tensile strength and metallurgical bonding between Al6063-T6 and SS304L that causes a higher amount of elements to participate at the faying zone which leads to creating intermetallic compounds such as AlFe, FeAl₃, and AlFe₃ whereas the maximum strength of 214 MPa was received, which corresponds to 82% joint efficiency.

Electric and magnetic field (EMF) phenomena arise where applying manual arc welding equipment. Consequently, using such systems may cause adverse effects to welding personnel. Models available quantitatively to assess EMF impacts in welding consistently show underestimation of exposure, mainly due to simplified boundary conditions implemented to facilitate modelling application. For arc welding, this paper introduces a novel approach, namely the implementation of Induction Factors based on anatomical body models in realistic welding postures and welding current parameters to improve the EMF assessment quality. Moreover, it is shown in how far especially advanced MIG/MAG and TIG welding variants, for example also involving additional hardware, may cause exposure values close to the limits defined in currently existing standards. Results, both found in practical process application and numerical simulation, are presented and discussed. Employing the developed calculation approach is capable of compensating for inaccuracies yet identified with models still recommended by regulatory or professional bodies. Users are provided with comprehensive information to help practically evaluate EMF exposure.

Friction surfacing (FS) is a solid-state deposition process in which layers are deposited on a substrate surface by frictional heat and severe plastic deformation of a consumable stud material below its melting temperature. Bonding occurs due to accelerated diffusion. The deposition of several layers on top of each other is referred to as multi-layer FS (MLFS), a promising candidate for additive manufacturing (AM) as it offers advantages over fusion-based AM. In this study, the MLFS process for the precipitation-hardenable alloy AA2024 is investigated regarding the influence of environmental process conditions, i.e., preheating of the substrate like other AM processes as well as underwater and room temperature experiments. The influence of ambient conditions on the process behavior, the layer geometries, the microstructure, and the mechanical properties is shown. Preheating the substrate leads to an overall higher process temperature (424.1 °C), resulting in thinner and wider layers, larger grains, an overaged microstructure, and a smooth hardness transition in the MLFS stacks from top (140 HV0.1) to bottom (95 HV0.1). The lower the process temperatures, e.g., for underwater FS (326.5 °C), the thicker and less wide the layers and the smaller the grains. The hardness shows a periodic pattern at the layer interface, which is more pronounced at lower process temperatures, i.e., the hardness values range from 100 HV0.1 to 150 HV0.1.

Friction stir welding (FSW) can be challenging for joining thick heat-treatable aluminum alloy plates due to limitations with the single-pass method and heat distribution. To address these challenges, the double-sided FSW (DS-FSW) method was developed. It allows for welding the unwelded side of the plate which cannot be reached by the pin’s height at the first pass. DS-FSW has been proven to overcome many challenges and has been found to be a great solution for FSW in welding thicker plates. During DS-FSW implementation, some drawbacks related to machine parameters were discovered but were addressed through tool development. However, the time efficiency issue was only solved when two identical tools were used. This approach, known as simultaneous DS-FSW (SDS-FSW), significantly reduced processing time. It also led to improved microstructures and mechanical properties based on survey results. The dual-tool simultaneity provided flexibility for dwelling into the workpiece in different ways, such as parallel side-by-side, tandem in-line, and staggered transverse, which were mostly used for lap welding in single-pass FSW and showed remarkable performance. Regardless of the dwelling method used, the impact of process parameters in FSW must always be taken into consideration, emphasizing the importance of the tool profile and machine-related parameters being in optimal working order.

Two carbon low alloy steels 42CrMo and 36Mn2V were successfully jointed using continuous drive friction welding. The effects of forging pressure and post-weld heat treatment on microstructure and mechanical properties of joints were investigated in detail. Results reveal that with increasing the forging pressure, the tensile and yield strength increase firstly and then decrease. The as-welded joint with the highest yield strength (708 MPa), largest elongation (14.2%), and high impact toughness (57.24 J) were obtained with the 35 MPa forging pressure. After post-weld heat treatment, the joint yield strength, elongation, and impact toughness were increased to 798 MPa, 18.1%, and 71.02 J, respectively. The microhardness measurement results indicate that the as-welded joints show higher Vicker hardness than the two base metals. After post-weld heat treatment, the microhardness was decreased owing to martensite elimination. The above findings provide a basis for the implementation of friction welding of dissimilar steels used for drills in the coal-mining industry.

An attempt has been made to join aluminium alloy (AA) 2219 and titanium alloy Ti-6Al-4V by solid-state diffusion bonding (SSDB), as this process ensures no macroscopic deformation during the joining. The quality of SSDB joints formed at the bonding temperature in the range of 500–540 °C is evaluated using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD). The bonding strength of the joints is evaluated by shear test, and hardness is tested using the Vickers microhardness method. It is observed that the hardness and shear strength values are increased with an increase in bonding temperature owing to the formation of intermetallic compounds at the joint interface like Al₃Ti, Al₂Ti and AlTi. Maximum shear strength of 82 MPa and hardness of 286 HV_0.05 are observed for the specimen bonded at 540 °C.

Addressing issues such as low efficiency in feature extraction, suboptimal feature quality, and low identification accuracy, this paper proposes an innovative Time Series Identification Method (TSIM) of weld joint penetration states based on multi-source data fusion. Firstly, a convolutional block, based on the architecture of convolutional neural networks, is designed. This forms part of an image representation network composed of three such blocks, incorporating a channel attention mechanism for high-quality image representation. Additionally, welding current and voltage data are integrated to create a comprehensive multi-source dataset. Secondly, an innovative atrous convolutional block is introduced, incorporating Efficient Channel Attention Networks (ECANet) to enhance the processing of multi-source data. Leveraging Temporal Convolutional Networks (TCN), an Efficient TCN (ETCN) with an advanced attention mechanism, is proposed. It is designed to extract global spatial features from the time series multi-source data, while concurrently feeding these data into a Transformer encoder to complete the extraction of time series features. Ultimately, a novel network utilizing a cross-attention mechanism is developed to amalgamate the time series and spatial features of the multi-source data, facilitating the prediction of Back-Side Bead Width (BSBW) and identification of the subsequent joint penetration state. Experimental findings indicate that irrespective of variations in welding current, the proposed algorithm achieves an RMSE of 0.17 mm. This represents a reduction in root mean squard error(RMSE), mean absolute error(MAE), and relative absolute error(RAE) values, along with an increase in R-square(R(^{2})) value, surpassing the performance of existing methods such as CNN, ResNet, CNN-LSTM, and AE-GRU.