Spot Welding Quality Research Articles

Investigation on the Effect of Projection Types on Stud Weld Strength by Taguchi Method

In this study, the effect of different weld projections on the mechanical properties of stud welds was analyzed using the Taguchi experiment planning method, using a projection welding machine with a capacity of 100 kVA with MFDC transformer. The samples were subjected to the tensile rupture force test with a destructive test device. The aim of this study was to ensure product safety by capturing the spot-welding quality, which is determined by the specifications of the automotive main industry, using a bolt with a lower weight. In this study, 1.5 mm WSS-M1A365-A11 uncoated steel sheet and 8.8 quality M6x1.00x25 triple projection uncoated welding bolts and 8.8 quality M6x1.00x18.5 ring shaped weld bolts were used as stud welding bolts. Different welding times, different welding currents and different welding clamping forces were used on the WSS-M1A365-A11 steel sheet samples in the stud welding operation with bolts. Taguchi analysis showed that ring shaped stud weld bolts required welding parameters of 32 ms welding time, 27 kA welding current and 500 daN welding force, while triple projection stud welding was found to perform the best optimum weld with 23 ms welding time, 18 kA welding current and 350 daN force.

Optimization and Analysis of Refill Friction Stir Spot Welding (RFSSW) Parameters of Dissimilar Aluminum Alloy Joints by FE and ANN Methods.

The quality of the refill friction stir spot welding (RFSSW) process is heavily dependent on the selected welding parameters that influence the resultant joint characteristics. Thermomechanical phenomena integral to the process were investigated using finite element (FE) analysis on two dissimilar materials. This FE analysis was subsequently validated through controlled experiments to ensure reliability. An artificial neural network (ANN) was employed to create a neural model based on an experimental setup involving 120 different sets of welding parameters. The parameters adjusted in the experimental plan included pin penetration depth, rotational speed, retention time, and positioning relative to material hardness. To assess the neural model's accuracy, outputs such as maximum temperature and normal stress at the end of the welding process were analyzed and validated by six data sets selected for their uniform distribution across the training domain.

A research on resistance spot welding quality judgment of stainless steel sheets based on revised quantum genetic algorithm and hidden markov model

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA (“coding length disaster” and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective.

An offset-transformer hierarchical model for point cloud-based resistance spot welding quality classification

Resistance spot welding (RSW) is a widely used welding technology in automotive manufacturing, and weld nugget quality is closely related to the quality of the vehicle body. Offline random checks are largely relied on the quality inspection of weld nuggets, but they have low efficiency and high cost. To address this issue, this paper proposes a deep learning model for RSW weld nugget classification, named the offset-transformer hierarchical model (OFTFHC), which is based on the point cloud data of its appearance shape. OFTFHC uses a hierarchical network structure to gradually expand the receptive field. A local feature module is introduced to extract local features from the point cloud, effectively enabling the recognition of the fine structural features of the resistance spot weld point cloud. A residual ratio module, which is based on MLP_MA and uses max and average functions for feature enhancement, is designed to adapt to the complex spatial structure of the point cloud. The offset-transformer structure is used to learn global context features, thereby enhancing the global feature extraction capability. Through classification experiments on RSW weld nuggets across 5 categories with a total of 1050 samples, OFTFHC achieved an average accuracy of 80.6 %, outperforming existing models. This demonstrates the effectiveness and superiority of the method, making it highly suitable for weld nugget quality control in automotive automation production lines.

An adaptive multivariate functional EWMA control chart

In many modern industrial scenarios, measurements of the quality characteristics of interest are often required to be represented as functional data or profiles. This motivates the growing interest in extending traditional univariate statistical process monitoring (SPM) schemes to the functional data setting. This article proposes a new SPM scheme, which is referred to as adaptive multivariate functional EWMA (AMFEWMA), to extend the well-known exponentially weighted moving average (EWMA) control chart from the univariate scalar to the multivariate functional setting. The proposed method distinguishes itself by adaptively selecting the weighting parameter in the calculation of the EWMA statistic to enhance the sensitivity of the AMFEWMA control chart across a spectrum of potential out-of-control scenarios. Such adaptability is essential in industrial processes, where multivariate functional quality characteristics are also subject to varying degrees of change. The favorable performance of the AMFEWMA control chart over existing methods is assessed via an extensive Monte Carlo simulation. Its practical applicability is demonstrated through a case study in monitoring the quality of a resistance spot welding (RSW) process in the automotive industry through online observations of dynamic resistance curves, which are associated with multiple spot welds on the same car body and are recognized as highly representative of the RSW process quality. The proposed method is implemented in the R package funcharts, available online on CRAN.

Expulsion prevention in resistance spot welding of dissimilar joints with ultra-high strength steel: An analysis of the mechanism and effect of preheating current

The widespread adoption of ultra-high strength steels, due to their high bulk resistivity, intensifies expulsion issues in resistance spot welding (RSW), deteriorating both the spot weld and surface quality. This study presents a novel approach to prevent expulsion by employing a preheating current. Through characteristic analysis of joint formation under critical welding current, the importance of plastic material encapsulation around the weld nugget (plastic shell) at high temperatures in preventing expulsion is highlighted. To evaluate the effect of preheating on the plastic shell and understand its mechanism in expulsion prevention, a two-dimensional welding simulation model for dissimilar ultra-high strength steel joints was established. The results showed that optimal preheating enhances the thickness of the plastic shell, improving its ability to encapsulate the weld nugget during the primary welding phase, thereby diminishing expulsion risks. Experimental validation confirmed that by employing the optimal preheating current, the maximum nugget diameter was enhanced to 9.42 mm, marking an increase of 13.4 % and extending the weldable current range by 27.5 %. Under quasi-static cross-tensile loading, joints with preheating demonstrated a 7.9 % enhancement in maximum load-bearing capacity compared to joints without preheating, showing a reproducible and complete pull-out failure mode within the heat-affected zone. This study offers a prevention method based on underlying mechanisms, providing a new perspective for future research on welding parameter optimization with the aim of expulsion prevention.

Effect of Welding Time Variation in Resistance Spot Welding on Mechanical Properties of Dissimilar Joints on Mild Steel and AISI 304 Stainless Steel

Resistance spot welding plays an important role in the automotive industry for working on car bodies or frames. Welding with two different steel materials is used in automotive structures. However, the low strength of the joints of different steel materials is a major problem, limiting the scope of its application. The quality of the welding connection Resistance spot welding is influenced by several factors, namely, time, welding current, and electrode pressure applied during welding and post-welding treatment. In this research, mild steel plate with AISI 304 stainless steel is welded by Resistance spot welding with different variations of welding time, material hardness and tensile testing that will be investigated. The results showed that the highest Rockwell Hardness time was obtained at a welding time variation of 8 seconds with a current of 6000 Ampere was 112.2 so the higher the welding time, the higher the hardness obtained. welding 8 seconds with a welding current of 6000 Ampere obtained a maximum tensile strength of 20.4 kgf/mm2 so the greater the welding current, the tensile strength will increase.

An intelligent inspection method for body-in-white weld quality based on vibration excitation response signals

To enhance the assembly quality in Body-in-White (BIW) assembly, this paper proposes an intelligent detection method for the nugget quality of Resistance Spot Weld (RSW) based on weld joint vibration excitation response signals. The method proposes a novel deep learning model, the Local-Global Hierarchical Graph Neural Network (LGHGNN). LGHGNN can automatically construct graph structures and, by introducing a newly designed upgrade pooling operation, extends the traditional flat structure of graph networks into a hierarchical structure within three-dimensional space. Therefore, LGHGNN achieves layered interaction of local-global information, enabling the model to focus on local details while gaining a broader learning perspective. Additionally, this paper proposes a strategy for multi-label unsupervised anomaly detection that involves layered interaction and collaborative decision-making for local and global graphs. The effectiveness of LGHGNN is demonstrated through its application in the BIW right front door assembly, achieving a remarkable 97.5% average accuracy in multi-region parallel anomaly detection.

Development of a fuzzy logic model for predicting the quality of micro friction stir spot welding (µFSSW) using particle swarm optimization

Micro Friction Stir Spot Welding (µFSSW) is crucial in microelectronics and precision manufacturing. It requires a comprehensive understanding of the complex connections between various parameters to achieve the highest quality welds. This study aims to improve the prediction of µFSSW weld quality by incorporating advanced optimization techniques. Fuzzy Logic Optimization is used to model uncertainties, and Particle Swarm Optimization (PSO) is employed to fine-tune parameters for improved accuracy. The fuzzy logic system utilizes Gaussian functions as membership functions, organized with nine rule bases. The results clearly demonstrate that the fuzzy logic model greatly enhances accuracy when combined with Particle Swarm Optimization. The refined model improves precision for pin diameter, shoulder diameter, Thermo-Mechanically Affected Zone (TMAZ) area, and cross-tensile strength. The PSO-optimized model shows lower accuracy in predicting plunge depth and shear tensile strength. The ongoing decline in Root Mean Square Error (RMSE) values highlights the complexity of the results. The optimization significantly improves the model’s ability to predict specific weld quality metrics, as demonstrated by the pin diameter’s reduced RMSE value of 0.07. The collective results showcase an optimized Fuzzy Logic System (FLS) model adept at accurately predicting µFSSW weld quality, demonstrating adaptability across diverse conditions. The discernible increase in accuracy, reaching up to 76 % following the optimization of the fuzzy logic model with PSO, serves as a testament to the efficacy of the employed methodologies in advancing the precision and reliability of µFSSW weld quality predictions

Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds

The quality of resistance spot welding (RSW) joints is strongly affected by the condition of the electrodes. This work develops a machine learning-based tool to automatically assess the influence of electrode wear on the quality of RSW welds. Two different experimental campaigns were performed to evaluate the effect of electrode wear on the mechanical strength of spot welds. The resulting failure load of the joints has been used to define the weld quality classes of the machine learning tool, while data from electrode displacement and electrode force sensors, embedded in the welding machine, have been processed to identify the predictors of the tool. Some machine learning algorithms have been tested. The most performing algorithm, i.e., the neural network, achieved an accuracy of 90%. This work provides important theoretical and practical contributions. First, the decreasing thermal expansion of the weld nugget as the electrode degradation advances results in a strong correlation between the difference of the maximum displacement value and the last value recorded during the welding and the relative failure load. Then, this work offers a practical decision support tool for manufacturers. In fact, the automatic detection of low-quality welds allows to reduce or eliminate unnecessary redundant welds, which are performed to compensate for the uncertainty of electrode wear. This leads to savings in time, energy, and resources for manufacturers. Finally, general recommendations for the timing of redressing or replacing the electrode are provided in the manuscript based on the company willingness to accept some non-compliant welds or not.

On-line measurement of nugget diameter in automatic resistance spot welding based on embedded ultrasound probe

The inspection of resistance spot welding (RSW) quality is crucial in the sheet metal joining industries. This paper proposes an enhanced approach for the automated real-time measurement of weld nugget diameter in welded joints, utilizing an ultrasonic probe integrated within the electrode. The ultrasonic signals received by the probe are sequentially arranged to generate consecutive ultrasonic B-scan images, which effectively capture the entire weld nugget growth and solidification process. The corresponding maximum time of flight (TOF) for the liquid nugget is extracted from the B-scan image. In the condition of qualified welding quality, a mathematic equation between maximum TOF and weld nugget diameter is established, while the opposite is alarmed. The on-line quality inspection of RSW can autonomously determine the qualification of welding quality, as well as quantitatively evaluate the weld nugget diameter in qualified weld joints. The research is applicable to various processing and manufacturing fields.

Implementation of Zero Defect Manufacturing using quality prediction: a spot welding case study from Bosch

Product quality is a vital aspect in the operation of manufacturing systems, manufacturers need to implement at least one quality assurance method to assure the desired quality. The most recent approach for quality assurance is named Zero Defect Manufacturing (ZDM). The scope of the current paper is the implementation of ZDM approach in the automotive industry and specifically for the spot-welding process. Using a machine learning method that is utilizing linear regression and LSTM and consuming data from the production such as sensors and other engineering data to predict the quality of future spot welds. Using this quality prediction there is a root cause analysis to identify why the spot weld will fail and the appropriate prevention actions are proposed. The next step is the training and validation of the machine learning model and the calculation of the accuracy of the model. Once the accuracy of the model is validated a series of simulations, using a dynamic scheduling tool, are performed in order to calculate a series of KPIs to evaluate the impact of the proposed method to the production.

Frequency-based analysis of active laser thermography for spot weld quality assessment

Frequency-based analysis of active laser thermography for spot weld quality assessment

Online evaluation method of resistance spot welding quality based on locally linear embedding algorithm

To address issues of low efficiency, poor feedback timeliness, and unsuitability for fast-paced, high-volume manufacturing of the traditional quality inspection methods of resistance spot welding, an online evaluation method of resistance spot welding quality based on a locally linear embedding algorithm is studied for mild steel resistance spot welding to achieve cost reduction and efficiency improvement. During welding tests, voltage and current were simultaneously collected to calculate the welding power signal. We study the variation pattern of the dynamic power curve. The dynamic power signal was subjected to locally linear embedding and manual feature extraction. The collected features were then used as input to build random forest models and CatBoost models for the online weld quality evaluation, respectively. The results show that the classification models with the feature volumes constructed by locally linear embedding as input have higher assessment accuracy than manually extracted features. The locally linear embedding method can effectively eliminate the subjective influence brought by manual extraction and has better reliability. The CatBoost model based on the locally linear embedding method using the welding power signal can quickly and effectively achieve online quality assessment of mild steel spot welding, providing a further breakthrough in spot welding quality evaluation technology.

A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies

Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.

Effect of electrode misalignment on the quality of resistance spot welds

The quality of resistance spot welds is critical to the structural integrity of components manufactured using the process. This paper analyses the effect of electrode misalignment, referred to as electrode tilt, on the quality of resistance spot welds and their relationship. The weld quality is represented by the diameter of the weld nugget as is standard in industry. Principal Component Analysis (PCA) was used to reduce the dimensionality of the dynamic resistance signal and extract features to be used in a linear regression model for the prediction of nugget diameter. This paper found that increased misalignment of the electrodes reduced the peak resistance of the welding process resulting in smaller nugget diameters and therefore a lower weld quality.

Online evaluation of resistance spot welding quality and defect classification

Resistance spot welding (RSW) is widely used in industrial product manufacturing, and weld quality is critical to ensure the safety and reliability of mechanical structures. With product quality improvement, the online monitoring of weld quality has become an urgent issue. Therefore, a method for online evaluation of RSW quality and automatic classification of welding defects based on mild steel is proposed. By acquiring the welding current and electrode voltage signals of the welding process in real time, a finite impulse response low-pass filter based on the Blackman window function is designed to perform zero-phase filtering of the dynamic resistance (DR). The influence of three welding parameters on the nugget diameter and shear strength of spot welding of DC01 mild steel was studied through the control variable method to determine the optimum welding parameters. Since the main defects of the RSW process are classified as expulsion defects and incomplete fusion defects, minor adjustments were made to the welding parameters to produce expulsion and incomplete fusion defects. Features in the time domain are extracted from the DR curves signals of normal, expulsion, and incomplete fusion welding spots, and the performance of the extracted features in the classifier is analyzed. Improvements are made to classify normal and expulsion spots with low accuracy, a new method is proposed that can accurately classify the quality of the welding spots. Through online evaluation tests of quality on 200 welding spots, the online evaluation test showed results that closely matched the validation test results. The results of the classification performance tests show that this study can be used for online evaluation of RSW quality and automatic classification of the welding spot defects of the DC01.

Monitoring of friction stirred spot weld quality for dissimilar Al6061 to polycarbonate using tool assisted thrust-torque signatures

Friction stir processing procedures are often found to be an alternative choice for the joining of thermoplastic polymers without melting the base materials. In recent years, dissimilar metallic to thermoplastic assemblies are highly used in aerospace and automotive industries as mechanical fastener or fusion welding techniques are rather inadequate for extreme discrepancy in thermal properties. The present work addresses the feasibility of friction stirred spot welding for non-ferrous aluminium (Al 6061) to polycarbonate using taper cylindrical tool of hardened H13 steel along with machine integrated axial thrust and stirring torque signals. The real-time positional and axial velocity of the rotating tool has been acquired to distinguish between plunging and dwelling stage. The process stability that is, the deviation of thrust-torque in respective phases was processed. The interaction of plunge depth and dwell time on tool pin assisted keyhole with associated material expulsion for different tool revolving speed was established. The weld mechanical behaviour has been scanned using load versus elongation diagram with associated weld pair interface. Lastly, sensors’ based process monitoring extended to weld quality prediction have been made using various significant features of thrust-torque-velocity based approaches. The tool spinning speed followed by dwell time was found to be important on weld quality index. The joint efficiency was significantly upgraded (more than 60%) at intermediate dwell time (40 s) with low tool plunging (0.3 mm) or reversed parametric setting at high tool revolving speed (2500 rpm) due to substantial reduction in axial thrust with higher stirring torque. The local deviation in thrust-torque during plunging was equally responsible with global mean thrust-torque in dwelling to dictate the joint strength and the weld ductility. The material stirring torque was better indicator of weld shear strength while pin assisted slot size highly correlated with tool thrust though tool plunging speed was highly crucial.

A study on spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network

During RSW, the number of qualified samples is much more than the unqualified ones, forming an unbalanced set, thus affecting the training effect of the model, meanwhile, most samples are unlabeled, and if all the joints are marked, it is more expensive. Based on this, a method of spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network is proposed. Firstly, labels are added to the noise to generate labeled data and unlabeled data, which are mixed in a certain proportion to ensure the diversity of generated data. Then, the real data is divided into two parts, in which the unlabeled part plays a game with the generated data to generate samples as close to the real as possible, meanwhile, the data with true discriminant results and labeled data are input into the autoencoder to obtain the feature vectors set of different states. Finally, the training parameters and test samples are input into the classifier to obtain the judgment results. The proposed method was applied to application case, and the results showed that it not only had a good fitting effect, but also had a high classification accuracy. Consequently, the method proposed was effective.

Real-time monitoring system for resistance spot welding quality

In this study, a stable real-time monitoring system was established to monitor electrical and mechanical signals during resistance spot welding process. The sudden decrease of electrode voltage signal, the fluctuation of dynamic electrode force and the obvious decline of dynamic resistance could be used for recognizing expulsion phenomena during resistance spot welding which would reduce welding quality and should be avoided as much as possible. In order to research the welding quality estimation methods, four estimation models were built based on regression analysis and back-propagation neural network. The results showed that the estimation accuracy of back-propagation neural network was higher than the model of regression analysis, and the characteristic values of dynamic signals during resistance spot welding process could improve the estimation accuracy significantly.