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Resistance spot welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as both nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). Starting from the thermal signal phase gradient and amplitude gradient maps, a convolutional neural network (CNN) estimates the size of nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study suggests adopting a new methodology for automatic RSW quality control based on thermal signal analysis.

During the spot welding process, it is always possible to be made defects, and the most important reasons for defects can be due to incorrect settings of current, force, and time. In other words, at different time intervals depending on the working conditions of the welding machine (i.e., both manual and robotic machines), such as the erosion of the electrode head, it is necessary to optimize the welding parameters and apply the essential changes in the next settings. The presence of various defects reduces the quality and strength of spot welds and eventually structures, which is one of the main concerns in automotive, aerospace, and marine industries. Therefore, in the present research, the authors have tried to investigate the effects of two important strength defects, including undersized and stick, on the spot-welded connection strength under static and cyclic loading. To this end, different samples of three-sheet spot-welded joint with defects of undersized and stick and free-defect were prepared based on the results of previous studies and using available relationships for the nugget diameter in terms of welding parameters, i.e., force, quadrilateral times (squeeze, up slope, welding time, and hold), and current of spot welding process. Next, metallographic, tensile, and axial fatigue tests were performed to obtain the nugget diameter, static strength of the connection, and fatigue behavior of the connection in different classifications of the samples, respectively. Furthermore, numerical analysis including the simulation of the welding process and the simulation of tensile and fatigue tests was performed in the finite element software. Numerical and experimental results have an acceptable correlation. Finally, both methods show that the nugget diameter is an influencing parameter on the tensile strength and fatigue life of spot welds, which reduces the fatigue life by 60% when the nugget diameter is reduced by 44%. Moreover, the stick defect also decreases the fatigue life of the spot-welded connection, while its effect is different depending on the geometry of welding core, including penetration height and nugget diameter.

The primary disadvantage of friction stir spot welding is the keyhole reminds after the welding process which removing of this keyhole is considered in this study. In addition, alumina nanopowder is incorporated into the weld stir zone as a technique to increase the joint strength. Flat friction stir spot welds with and without alumina nanopowder are examined, and the influences of the main process parameters on the joint strength and weld microstructure were investigated. It was found that the most effective parameters on the joint strength were the normal plunge depth, dwell time, and tool rotational speed, respectively. Tool rotational speed of 1120 rpm, 5 s dwell time, and normal plunge depth of 2.75 were found as the optimum process parameters. In this case, the strength of the friction stir spot weld increased 23% due to adding alumina nanopowder. The hardness of the stir zone was also increased, somewhat.

Resistance spot welding is used as a reliable joining process in many engineering applications because of its effectiveness, automation capability, and low cost. The spot-welded sheet metals, on the other hand, are prone to mechanical fatigue failure, especially under cyclic loadings. Therefore, understanding and elucidating the fatigue phenomenon of the spot-welded joints are crucial in terms of estimating and preventing undesired failure conditions. In the design phase, there exist a considerable amount of challenges to overcome; one of the most important challenges is to select optimum working conditions. Hence, in this study, the fatigue phenomenon of the spot-welded sheet metals is investigated experimentally, by taking electrode force into consideration. For this purpose, spot-welded modified tensile shear (MTS) test specimens were utilized. A series of fatigue life tests were conducted to examine the influence of electrode force on fatigue life. The results obtained through an optical microscope were presented and interpreted. Experimental data showed that the number of cycles to failure changes depending on the spot-generating schemes in terms of electrode force and welding schedules. Through the investigation of an optical micrograph of partially failed spot-welded MTS specimens for different groups of spot welds created under different electrode force effects, it is seen that the fatigue failure is dominated by the through-thickness cracking. Comparing both crack formation and also fatigue lives of different groups of spot-welded MTS specimens, it is shown that the electrode force and accordingly thermal interaction play an important role in the fatigue strength of the spot-welded specimens.

Steel T-profiles with the spot-welding manufacturing process are extensively used in various sectors such as construction, automotive, renewable energy, etc., due to their versatility and reliability. These profiles are exposed to various loading modes during their service life, which include axial, bending, shear, torsional, or combinations thereof. This paper investigates the fatigue performance of a spot-welded T-profile assembly subjected to torsional cyclic loading. The extended finite element method (XFEM) analysis was performed to simulate the intricate behavior of spot welds under the loading, elucidating critical areas prone to fatigue initiation and propagation especially around the spot welds. The simulation results were compared with previously obtained experimental results. Both results are consistent. The effects of various parameters, including the spot-weld diameters, the amount of torque applied, thickness of the profile parts, and the presence of base part, on the fatigue performance of the assembly were studied critically.

Spot welds are widely used to join metal stamping parts in the automotive industry. The number and layout of spot welds have direct impacts on both the mechanical performances of the vehicle and the manufacturing cost. The traditional methods including the 0–1 programming method, the size optimization method, the direct optimization method, and the single-step topology optimization (SSTO) method all have major drawbacks and are not suitable or efficient for spot weld layout design of an automotive structure. In this work, we propose a new method called multi-step varying-domain topology optimization (MVTO) method for spot weld design of automotive structures to balance structural performance and manufacturing cost. Based on a multi-step topology optimization framework with adaptive and varying design domains, the MVTO method can find a better layout design of the spot welds in a more efficient way compared to the existing methods. Limited engineering experiences are required and adverse human factors are eliminated in the spot weld design process with MVTO. To assist the application of MVTO method and to realize design automation, a new modeling approach is also developed to connect the continuous connection elements (CCEs) of the spot welds to the shell elements of the welded structure. Two case studies involving a simple single-hat beam and an automotive B-pillar show that the proposed MVTO method is reliable and effective for spot weld design of automotive structures. It has also been demonstrated that the MVTO method is superior to the direct optimization method and the SSTO method in terms of spot weld number reduction and welded structure performances, thus has a great potential for spot weld design of complicated automotive structures.

An automobile’s structure takes about 3000 to 6000 spot welds, and resistance spot welding is the most widely used because of its simplicity. Because it is a process that greatly influences the overall production cost, the automotive industry tends to decrease spot welds present in vehicle structure. With this, the majority of automobiles are unable to achieve satisfactory results in safety tests, such as crash tests. In addition to affecting safety in impact cases, the reduction in the number of spot welds implies a reduction in torsional and longitudinal vehicle stiffness, making it unstable and noisy. In view of the need for more studies on the effects of this reduction, this study uses numerical simulation and topology optimization to help assess the most appropriate spot weld distribution in a weldment, to maintain the torsional stiffness within acceptable limits. Results showed that structures obtained by topology optimization can have better stiffness and compliance compared to non-optimized structures.

The root cause of post-weld baking on the mechanical performance of Al-steel dissimilar resistance spot welds (RSWs) has been determined by machine learning (ML) and finite element modeling (FEM) in this study. A deep neural network (DNN) model was constructed to associate the spot weld performance with the joint attributes, stacking materials, and other conditions, using a comprehensive experimental dataset. The DNN model positively identified that the post-weld baking reduces the joint performance, and the extent of degradation depends on the thickness of stacking materials. A three-dimensional finite element (FE) model was then used to investigate the root cause and the mechanism of the baking effect. It revealed that the formation of high thermal stresses during baking, from the mismatch of thermal expansion between steel and Al alloy, causes damage and cracking of the brittle intermetallic compound (IMC) formed at the interface of the weld nugget during welding. This in turn reduces the joint performance by promoting undesirable interfacial fracture when the welds were subjected to externally applied loads. The FEM model further revealed that increase in structural stiffness, because of increase in steel sheet thickness, reduces the thermal stresses at the interface caused by the thermal expansion mismatch and consequently lessens the detrimental effect of post-weld baking on the joint performance.

This study delves into the exploration of the fatigue performance of a structure that has been spot-welded and is being loaded with torsional fatigue. The extended finite element method (XFEM) was applied to simulate the intricate interaction of spot welds in response to cyclic loading. The developed model was validated through experiments. The influences of different parameters, such as the number of spot welds used to join the adherends, the diameters of the spot welds, and the load ratio applied, on the fatigue performance of the box were investigated. The first two parameters studied had a significant influence on the extent of the fatigue failure-affected spot welds, where the crack propagation rate can be decreased by more than 700%.

Spot welds are commonly used to join steel sheets in automotive structures. The number and layout of these spot welds are vital for the performance of the structure. However, reducing the number of spot welds will cut both production time and cost. This article presents three different methods of reducing the number of spot welds in automotive structures: ranking-based selection, topology optimization and size optimization of a parameterized model. The methods are compared in a simple example and it is found that the latter two methods have the best potential of reducing the number of spot welds. Topology optimization requires less preparation and computational effort as compared to size optimization of a parameterized model. However, the method is primarily suitable for studies where load cases involving linear systems are judged to be most important. Otherwise, size optimization of a parameterized model is probably a better choice. The topology optimization approach is successfully demonstrated in a full-scale industrial application example and confirms that the method is useful within contemporary product development.