Search Results (34)

Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data analyses is still necessary to fully realize a monitoring system. This work proposes a two-stage deep learning (DL) approach for automated ultrasonic data analysis for RSW processing monitoring. The first stage conducts semantic segmentation on ultrasonic M-scan welding process signatures, yielding masks for identified molten pool and stack regions from which weld penetration measurements can be directly extracted, as well as expulsion occurrences throughout welding. From input images and segmentation outputs, the second stage directly estimates resultant weld nugget diameters using an additional neural network. Both stages leveraged architectures based on TransUNet, mixing elements of both convolutional neural networks (CNN) and vision transformers, and the effect of cross-attention for stack-up sheet thickness data fusion was investigated via an ablation study. Additionally, in the diameter estimation stage, the ablation study included alternative feature extraction architectures in the network and investigated the provision of M-scans to the model alongside segmentation masks. In both cases, cross-attention was determined to improve performance, and in the case of diameter estimation, providing M-scans as input was found to be beneficial in general. With cross-attention, the segmentation approach yielded a mean intersection over union (IoU) of 0.942 on molten pool, stack, and expulsion regions in the M-scans with 13.4 ms inference time. With cross-attention, diameter estimates yielded a mean absolute error of 0.432 mm with 4.3 ms inference time, representing a significant improvement over algorithmic approaches based on ultrasonic time of flight. Additionally, the approach attained >90% probability of detection (POD) at 0.830 mm below the acceptable diameter threshold and <10% probability of false alarm (PFA) at 0.828 mm above the threshold. These results demonstrate a novel production-ready application of DL in ultrasonic nondestructive evaluation (NDE) and pave the way for zero-defect RSW manufacturing. Full article

Loquat fruit is valued for its pleasant taste and favorable ripening period. However, its delicate texture and high perishability make it highly vulnerable to damage during packaging, so the fruit is usually packed by hand. Developing a fruit-sizing machine could increase commercial market opportunities. Automated mass detection reduces manual sorting errors and labor requirements. Overall, it enhances grading accuracy, speed, and uniformity in loquat processing. It also helps distinguish between ripe, underripe, and overripe fruits through subtle mass differences. Mass modeling has proven to be an effective baseline approach for the development and optimization of grading machines, and its efficiency has been demonstrated across different fruit types. Here, we present a comparative analysis of various models for mass modeling of six international and Italian loquat varieties (“Algerie,” “Peluche,” “Golden Nugget,” “Virticchiara,” “Nespolone di Trabia,” and “Claudia”) cultivated in southern Italy. On fifty fruits per variety, singular mass and spatial diameters [longitudinal (DL), maximum transverse (DT1), and minimum transverse (DT2) were measured. Linear and non-linear regression analyses, including quadratic, polynomial, and cubic models, were applied to both the complete dataset and individual varieties. A set of predictors was used, including DL (length), DT1 (width), and DT2 (thickness), ellipsoid and oblate spheroid volume. Model performance was evaluated based on higher R² values, and lower RMSE and MBE values. The best general model was obtained using an ellipsoidal volume (R² = 0.97, RMSE = 2.76). Both linear and cubic models demonstrated high suitability across all varieties, with ellipsoidal volume emerging as the most effective predictor. Conversely, (DL) based models were the least suitable, yielding the lowest (R² = 0.41) values in “Virticchiara.” The developed general and specific-variety models and equations provide a solid foundation for establishing high-performance systems for mass and size estimation, which can be effectively integrated into a fruit sizer machine. Full article

H1000 stainless steel is defined as a nickel-saving austenitic stainless steel, characterized by high strength and high elongation. DP590 steel is widely used in the manufacturing of vehicle bodies. DP590 dual-phase steel is classified as a high-strength low-alloy steel, known for its high strength and good formability. To address issues such as nugget deviation, inhomogeneous mixing of the internal nugget microstructure, and interfacial fracture during tensile-shear testing in resistance spot-welded joints of these dissimilar materials, a unilateral magnetic-assisted resistance spot-welding process was proposed. The influence of the external magnetic field on various properties of the joint was systematically investigated. The results indicate that the application of an external magnetic field significantly enhances the strength of H1000/DP590 dissimilar spot-welded joints, with joint strength increasing by approximately 14% and energy absorption capacity improving by about 30%. These improvements are attributed to the electromagnetic stirring effect induced by the magnetic field, through which the effective nugget diameter was enlarged, the microstructure was homogenized, and the macroscopic morphology of the nugget was modified. As a result, the bonding area between the nugget and the base metal is expanded, and the fracture mode of the joint is shifted from interfacial failure to partial button failure, thereby enhancing the mechanical properties of the joint. Full article

Resistance spot welding is a process used to join overlapping metals using pressure and electric current, commonly applied in the automotive industry for joining car bodies. This study aimed to understand the mechanical performance of spot welds under dynamic impact conditions. Various welding schedules were tested to observe the effects of different welding currents and times on the impact energy absorbed by spot welds. The results showed that the impact energy absorbed ranged from 26 J to 98 J, with higher welding currents and times generally increasing the impact energy due to more heat input. However, excessive welding parameters led to decreased impact energy. Statistical analysis and modeling revealed that optimal impact energy is achieved with a welding current of 5 kA and welding time of 6.728 cycles. Full article

The design of spacecraft protection against orbital debris (OD) is generally based on experiments and models involving spherical projectiles. However, observations of collision fragments from ground-based satellite impact experiments have shown that orbital debris is non-spherical in shape. To accommodate non-spherical projectiles in spacecraft protection measures, a relationship between spherical projectiles and their threat-equivalent non-spherical counterparts was established. Cylindrical projectiles featuring adjustable Length-to-Diameter (L/D) ratios were employed to simulate the projectile shape effect on the bumper performance under hypervelocity impact. The L/D ratio spanned a range from L/D = 1/3, representing a “flake” shape, through L/D = 1 for a “nugget” configuration and extended up to L/D = 5/3, representing a “straight rod” configuration. The numerical analysis utilized the smoothed-particle hydrodynamics technique, demonstrating that projectile geometry significantly influenced the threat posed by projectile fragments to the objects behind the bumper. The established projectile threat relationship can be applied to assess the ability of the existing OD bumpers to withstand non-spherical projectiles by representing them with an equivalent sphere. Utilizing this approach can contribute to decreasing uncertainty and enhancing the protection of spacecraft when encountering irregularly shaped OD particles. Full article

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