Search Results (29)

The use of weld bond (WB) joints in automotive manufacturing is gaining popularity for joining similar and dissimilar materials. This study investigated the effect of Sikaflex-252 (Sika Australia Pty Ltd, Perth, Australia) adhesive in DP600 similar steel joints and DP600 and AISI 316 stainless steel dissimilar steel joints. An increase in welding current from 7 kA to 10 kA increased the weld diameter and tensile shear strength in the RSW joints and the WB joints. WB joints had bigger weld diameters of 5.39 mm and 4.84 mm, higher tensile shear strengths of 12.3 kN and 6.85 kN, and higher energy absorption before failure of 32.6 J and 24.6 J at 10 kA compared to joints at 7 kA for similar and dissimilar joints, respectively. The use of adhesive increased heat generation at 10 kA welding current, due to the increase in dynamic resistance. At 7 kA welding current, the adhesive could not produce sufficient heat for spot weld development. The use of adhesive narrowed the weldability lobe in dissimilar RSW and WB joints and showed changes in failure mode. In similar RSW joints and WB joints, weldability lobe changes were not observed, and RSW and WB joints had the same fracture mode for the same welding current. WB welds have reduced stress distribution across the weld nugget compared to RSW welds because of the bigger weld diameter of 5.39 mm and lesser sheet bending of 1.13 mm. WB joint failure comprises the adhesive failure at the start and later the spot weld failure, while RSW joint failure is purely due to spot weld failure. Full article

Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. The methodology connects the welding input and during-process parameters with the output visual quality information. A manual resistance spot welding machine was used to monitor and record the process input and output parameters to generate the dataset for training. The welding current, welding time, and electrode pressure data were correlated with the welding spot nugget’s quality, mechanical characteristics, and thermal and visible images. Six machine learning models were trained on visible and thermographic images to classify the weld’s quality and connect the quality characteristics (pull force and welding diameter) and the manufacturing process parameters with the visible and thermographic images of the weld. Finally, a cross-validation method validated the robustness of these models. The results indicate that the welding time and the angle between electrodes are highly influential parameters on the mechanical strength of the joint. Additionally, models using visible images of the welding spot exhibited superior performance compared to thermal images. Full article

In response to the growing demand for fuel economy and the imperative to reduce greenhouse gas emissions, the automotive industry has embraced structural lightweighting through multi-material solutions. This poses challenges in joining dissimilar lightweight metals, such as aluminum alloys to steels. The effects of the diameter of a weld nugget have been well documented, particularly in relation to its effects on the tensile strength, tensile fracture modes and fatigue behavior. For tensile shear specimens, various methods have been developed over the years to predict fracture modes by deriving the critical nugget diameter. However, these methods have proved inadequate for coach peel specimens, where a noteworthy observation is the occurrence of pull-out fracture modes with smaller weld nugget diameters than the critical diameter. In the present study, aluminum alloy sheets and steel sheets were resistance spot welded, achieving a deliberately reduced weld nugget diameter to induce an interfacial fracture mode in the tensile testing of coach peel specimens. Intriguingly, it was noted that fatigue fracture modes in the same coach peel specimens transitioned from pull-out to interfacial with decreasing applied loads, challenging conventional expectations. Furthermore, finite element analysis was performed, and the findings indicated that the fracture modes of the coach peel specimens were influenced not only by the diameter of the weld nugget but also by local stress states, specifically the stress triaxiality at the tips of the spot weld notches. Full article

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model’s quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions. Full article

The welding quality during welding body-in-white (BIW) determines the safety of automobiles. Due to the limitations of testing cost and cycle time, the prediction of welding quality has become an essential safety issue in the process of automobile production. Conventional prediction methods mainly consider the welding process parameters and ignore the material parameters, causing their results to be unrealistic. Upon identifying significant correlations between vehicle body materials, we utilize principal component analysis (PCA) to perform dimensionality reduction and extract the underlying principal components. Thereafter, we employ a greedy feature selection strategy to identify the most salient features. In this study, a welding quality prediction model integrating process parameters and material characteristics is proposed, following which the influence of material properties is analyzed. The model is verified based on actual production data, and the results show that the accuracy of the model is improved through integrating the production process characteristics and material characteristics. Moreover, the overfitting phenomenon can be effectively avoided in the prediction process. Full article

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R²). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1–1 mm, 3.298 kN and 4.1 mm for the case of 0.5–0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5–1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1–1 mm case was the welding current; for the 0.5–0.5 mm case, it was pulse welding; and for the 0.5–1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg–Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures. Full article

Spot welded joints play a crucial role in the construction of modern automobiles, serving as a vital method for enhancing the structural integrity, strength, and durability of the vehicle body. Taking into account spot welding process in automotive bodies, numerous defects can arise, such as insufficient weld nugget diameter. It may have evident influence on vehicle operation or even contribute to accidents on the road. Hence, there is a need for non-invasive methods that allow to assess the quality of the spot welds without compromising their structural integrity and characteristics. Thus, this study describes a novel method for assessing spot welded joints using ultrasound technology. The usage of ultrasonic surface waves is the main component of the proposed advancement. The study employed ultrasonic transducers operating at a frequency of 10 MHz and a specially designed setup for testing various spot welded samples. The parameters of the spot welding procedure and the size of the weld nugget caused differences in the ultrasonic surface waveforms that were recorded during experiments. One of the indicators of weld quality was the amplitude of the ultrasonic pulse. For low quality spot welds, the amplitude amounted to around 25% of the maximum value when using single-sided transducers. Conversely, for high-quality welds an amplitude of 90% was achieved. Depending on the size of the weld nugget, a larger or smaller amount of wave energy is transferred, which results in a smaller or larger amplitude of the ultrasonic pulse. Comparable results were obtained when employing transducers on both sides of the tested joint, as an amplitude ranging from 13% for inferior welds to 97% for superior ones was observed. This research confirmed the feasibility of employing surface waves to assess the diameter of the weld nugget accurately. Full article

Due to their effective and affordable joining capabilities, resistance spot-welded (RSW) structures are widely used in many industries, including the automotive, aerospace, and manufacturing sectors. Because spot-welded structures are frequently subjected to cyclic stress conditions while in service, fatigue failure is a serious concern. It is essential to comprehend and predict their fatigue behavior in order to guarantee the dependability and durability of the relevant engineering products. The analysis of fatigue failure in spot-welded structures is the main topic of this paper, along with the prediction of fatigue life (N_f) and identification of failure mechanisms. Also, the effects of parameters such as the amount of cyclic load applied, the load ratio, and size of the spot-welding on the N_f were investigated. To achieve this, the fatigue performance of spot-welded joints was simulated using the extended finite element method (XFEM). The XFEM method is particularly suited for capturing intricate crack patterns in spot-welded structures because it allows for the modeling of crack propagation without the need for remeshing. It was observed that when the cycling load was decreased by 20%, N_f increased by around 250%. On the other hand, the fatigue life of the structure, and, hence, the crack propagation rate, was significantly affected by the load ratio and diameter of the spot-welding. This paper presents the details of the novel approach to studying spot-weld fatigue characterization using XFEMs to simulate crack propagation. Full article

For the friction stir welding (FSW) of AZ91 magnesium alloy, low tool rotational speeds and increased tool linear speeds (ratio 3.2) along with a larger diameter shoulder and pin are utilized. The research focused on the influence of welding forces and the characterization of the welds by light microscopy, scanning electron microscopy with an electron backscatter diffraction system (SEM-EBSD), hardness distribution across the joint cross-section, joint tensile strength, and SEM examination of fractured specimens after tensile tests. The micromechanical static tensile tests performed are unique and reveal the material strength distribution within the joint. A numerical model of the temperature distribution and material flow during joining is also presented. The work demonstrates that a good-quality joint can be obtained. A fine microstructure is formed at the weld face, containing larger precipitates of the intermetallic phase, while the weld nugget comprises larger grains. The numerical simulation correlates well with experimental measurements. On the advancing side, the hardness (approx. 60 HV0.1) and strength (approx. 150 MPa) of the weld are lower, which is also related to the lower plasticity of this region of the joint. The strength (approx. 300 MPa) in some micro-areas is significantly higher than that of the overall joint (204 MPa). This is primarily attributable to the macroscopic sample also containing material in the as-cast state, i.e., unwrought. The microprobe therefore includes less potential crack nucleation mechanisms, such as microsegregations and microshrinkage. Full article

The manufacture of complicated automobile components that are joined by resistance spot welding requires considerable cost and time. The use of additive manufacturing technology to manufacture automobile components helps reduce the overall time consumption and yields high accuracy. In this study, the weldability of conventional (C) 316L stainless steel and additive manufactured (AM) 316L stainless steel was evaluated and analyzed. After deriving the lobe diagram for both the materials, the monitoring data, nugget diameter, tensile shear strength, and hardness were analyzed. The findings of the study have opened up a massive potential for use in resistance spot welding technology for additive manufactured materials’ industries in the forthcoming years. When AM 316L stainless steel was welded in the constant current control mode, a nugget diameter of up to 4.7 mm, which is below the international standard, could be secured. Through the constant power control mode, however, the nugget diameter could be improved to a sufficient level of 5.8 mm. Full article

Vortex- friction stir welding (VFSW) utilizes a stir bar made of an identical material to the workpiece to rub the workpiece’s top surface, which avoids the keyhole defect in conventional friction stir welding. It presents great potential in the repair field of aluminum alloys. In this study, the effect of stir bar diameter, rotation speed, and welding speed on the weld formation was investigated in the VFSW of 6061-T6 aluminum alloy. The weld macrostructure, penetration, and mechanical properties were characterized. The results show that a large diameter of the stir bar can enhance the vortex material flow, increase the heat input, and eliminate the incomplete-penetration defect. The increase in rotation speed within limits can enhance the weld penetration and the mechanical properties of the weld nugget zone (WNZ). However, too high a rotation speed reduces the weld penetration and weakens the mechanical properties of the WNZ. The increase in welding speed reduces the weld penetration but enhances the mechanical properties of the heat affected zone. The incomplete-penetration defect significantly weakens the ductility of the VFSW joint. It can be eliminated by enlarging the stir bar diameter and choosing a moderate rotation speed and a lower welding speed. Full article

Electrode degradation in the continuous resistance spot-welding process of baked hardening (BH) 220 steel was evaluated by an electrode life test, and weldability tests were conducted by geometry feature measurement, mechanical property analysis, and electrode diameter measurement with 88 or 176 weld intervals. The analysis of weld geometry shows that the heat-affected zone (HAZ) width, nugget diameter, and nugget area tend to decrease rapidly, while the nugget height tends to increase with the weld repetitions until the welding heat input becomes too small to form an effective nugget. The maximum displacement and failure energy of the welded joints show a decreasing trend during the welding electrode failure process, while the peak load increases slightly until the 88th weld and then decreases. The cavities and pores in the nugget mainly appear after the 176th spot weld. The electrode diameter increases during welding. The reason for the increase in electrode diameter may be that the contact area between the electrode and the BH 220 steel sheets becomes smaller in the welding process, which causes the continuous sticking phenomenon between the electrode and the BH 220 steel sheets. In the absence of alloying, the edge of the electrode is geometrically deformed, while Cu–Zn–Fe alloying occurs in the area in contact with the BH 220 steel sheet. Full article

In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs. Full article

Resistance spot-welding of dissimilar metals, medium manganese TRIP steel 7Mn and DP590, is carried out. The effects of single-pulse welding parameters and a double-pulse-tempering current on the quality characteristic parameters and mechanical properties of 7Mn/DP590 spot-welded joints are studied. The welding process parameters are optimized using the control variable method. The results show that the optimal process parameters under a single pulse are as follows: electrode pressure: 4.5 kN, welding current: 9 kA and welding time: 300 ms. The failure mode of the welding joint is partial pull-out failure (PF-TT). The welding parameters have great influence on the nugget diameter and thickness reduction. Expulsion, crack and shrinkage are displayed in the joint under high electrode pressure. Softening occurs in the heat-affected zone due to a strong halo effect in the single-pulse weld. The tempering zone on the DP590 side is 202.49 HV, which is the lowest hardness point, while the hardness of the nugget zone is 450 HV. The addition of the tempering current homogenizes the microstructure with different failure paths and eliminates the stress. The tensile shear force of the joint increases by 17.13%. The 7Mn Steel/DP590 resistance spot-welding joint is from the fusion line to the center of the nugget, and the microstructure is composed of plane crystal, cellular crystal, dendritic crystal and columnar crystal, in turn. The nugget zone is composed of lath martensite and a small amount of residual austenite. Fine quasi-spherical and lamellar interbedded cementites are formed in the tempering zone of the DP590-side heat-affected zone. Full article

Resistance spot welding as the basic method of joining car body elements has been used in the automotive industry for many years. For these connections, it is required to obtain the appropriate diameter of the weld nugget, which results in a high strength and durability of the connection during vehicle operation. The article presents the methodology of testing spot-welded joints using both destructive methods: shearing test of the spot weld and the ultrasonic method. The main goals of the performed tests are (1) to determine the correlation between the mechanical strength of a joint, measured in kN, and the selected parameters of the ultrasonic longitudinal wave with a frequency of 20 MHz propagating in the area of the spot weld and (2) to build and verify the predictive models of the weld nugget quality. The correlation of these parameters allows assessing the strength of the connection with the use of a non-destructive test method. On the basis of the performed analyses, it was determined that there is a strongly positive correlation between the number of reverse echoes and the force necessary to destroy the spot weld (0.41) and the diameter of the weld nugget (0.50). A strong negative correlation was also obtained between the number of echoes and the strength (−0.69) and diameter of the weld nugget (−0.72). Full article