Friction Stir Welding Process Research Articles

Behavior of microstructure and mechanical properties in the stir zone of friction stir welded ME21 magnesium alloy

In this work, Friction Stir Welding (FSW) of magnesium alloy ME21 in a T-profile is performed with a relatively higher welding speed than usual in this material. The FSW process parameters, i.e., rotational speed and welding speed, are investigated focused within its inherent microstructural and mechanical properties on the stir zones of the welded joints. The concept of a Stribeck Curve in tribological systems, is transferred to FSW to explain the transition between insufficient and full plasticization of the material. The stir zone presented a strong grain refinement due to the activation of dynamic recrystallization along with a completely changed crystallographic texture and orientation due to FSW. The hardness is overall improved, while the ductility and tensile strength were reduced. The microstructure and texture in the stir zone were not strongly influenced by changes in the welding parameters.

Effect of tools rotational speed on the mechanical properties of one-step double-acting friction stir welded aluminum alloy AA 6061 hollow panel

The utilization of Aluminum hollow panels enhances structural strength while simultaneously ensuring a lightweight and efficient use of materials. During their application, these panels necessitate a welding process that is susceptible to porosity due to the disparity in hydrogen gas solubility between liquid and solid aluminum. Solid-state welding techniques, such as Friction Stir Welding (FSW), have proven to be effective and appropriate solutions for overcoming this issue. However, due to the thickness of the hollow panels, FSW process is unfeasible as it requires welding on both sides, resulting in prolonged production times. Consequently, the development of a one-step double-acting FSW technique becomes necessary, involving the simultaneous utilization of two tools. The usage of two tools introduces two sources of friction-stir forces, heat, and axial forces, demanding an assessment of the novel response from the specimens. This research aims to analyze the effect of a specific parameter, namely the tool rotation speed, within the one-step double-acting FSW process on the physical and mechanical properties of the AA6061 hollow panels. The One-Step Double-Acting FSW process involved conducting variations in the tool rotation speed on both sides of the welds. Specifically, for the 4G weld position (underside of the workpiece with an overhead weld position), speeds of 1200, 1500, and 1800 rpm were employed. Meanwhile, a consistent rotation speed of 1500 rpm was maintained for the 1G weld position (overside of the material with a flat weld position). The transverse speed and tilt angle are set at 30 mm/min and 2°, respectively. Elevating the tool rotation speed results in increased hardness, load capacity, and bending strength of the weld joints. The specimen subjected to the highest rotational speed (1800 rpm) exhibits the most exceptional mechanical properties, including a hardness of 73.46 HVN, load capacity of 18.47 kN, and bending strength of 60.56 MPa.

Digital Twin Virtual Welding Approach of Robotic Friction Stir Welding Based on Co-Simulation of FEA Model and Robotic Model

Robotic friction stir welding has become an important research direction in friction stir welding technology. However, the low stiffness of serial industrial robots leads to substantial, difficult-to-measure end-effector deviations under the welding forces during the friction stir welding process, impacting the welding quality. To more effectively measure the deviations in the end-effector, this study introduces a digital twin model based on the five-dimensional digital twin theory. The model obtains the current data of the robot and six-axis force sensor and calculates the real-time end deviations using the robot model. Based on this, a virtual welding model was realized by integrating the FEA model with the digital twin model using a co-simulation approach. This model achieves pre-process simulation by iteratively cycling through the simulated force from the FEA model and the end displacement from the robot model. The virtual welding model effectively predicts the welding outcomes with a mere 6.9% error in lateral deviation compared to actual welding, demonstrating its potential in optimizing welding parameters and enhancing accuracy and quality. Employing digital twin models to monitor, simulate, and optimize the welding process can reduce risks, save costs, and improve efficiency, providing new perspectives for optimizing robotic friction stir welding processes.

МЕТОДЫ МОДЕЛИРОВАНИЯ ТЕМПЕРАТУРЫ И ПЕРЕМЕЩЕНИЯ МАТЕРИАЛА ПРИ СВАРКЕ ТРЕНИЕМ С ПЕРЕМЕШИВАНИЕМ

Numerical modeling of the friction stir welding (FSW) process is challenging due to numerous complexities. This is a fully coupled thermomechanical problem that involves large plastic deformation, heat flow, nonlinear contact, and friction. Accurate modeling of the FSW process will provide a better understanding of the material flow and temperature distribution during the process, which is important for optimizing process parameters and reducing the likelihood of weld defects during FSW. Various approaches to modeling the temperature and material distribution in the FSW process are considered, such as the Euler or Lagrange approach, smoother particle hydrodynamics (SPH), coupled Euler and Lagrange methods (CEL), and arbitrary Lagrange and Euler methods (ALE). In this work, temperature and material distributions during the FSW process of a dissimilar aluminum-copper joint were simulated by finite element analysis using CEL and ALE approaches. In addition, it was shown that the temperature distribution along the weld line is asymmetrical: the maximum temperature on the advancing side is higher than on the retreating side.

Grey-Taguchi Approach for Optimizing FSW Parameters in Joining AA6262 and AA5083 Alloys

T his study concentrates on the friction stir welding (FSW) joining of dissimilar AA6262 and AA5083 plates. Four input parameters, namely tool rotational speed (TRS), welding speed (WSP), tool tilt angle (TTA), and pin depth (PDP) are chosen to analyze the output responses of tensile strength (UTS), hardness (HBN), and wear rate (WTR) in welding. The primary objective is to optimize the variables of the FSW process for AA6262 and AA5083 alloys using a combination of Taguchi and Grey Relational Analysis (GRA) methods, while also comprehending the correlations among process variables through the application of these two methods. Utilizing the Taguchi approach, L27 orthogonal array is constructed for conducting FSW experiments. After this, the multi-objective optimization challenge is streamlined into a single-objective optimization task by leveraging the grey relational degree. The significance and impact of the distinct variables are elucidated through the application of Analysis of Variance (ANOVA). Confirmation tests and predictive analysis are performed to validate the optimal variables, which are then compared against the acquired outcomes. The results highlight the welding speed as the paramount parameter, exerting substantial influence over the output responses of the welds in contrast to the other variables under examination. The application of the Grey-Taguchi methodology yields enhancements in Tensile Strength (245.23 MPa) and Hardness (99.2 Hv), while concurrently decreasing the Wear Rate (0.00456 m3/Nm). Furthermore, the Grey Relational Grade exhibits an advancement of 0.1312. These findings collectively signify an increased operational efficiency in the welding process, particularly for dissimilar alloys.

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets.

The article describes machine learning using artificial neural networks (ANNs) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). The ANNs were built using a total of 608 experimental data. Two types of networks were built. The first one was used to classify good/bad joints with MLP 7-19-2 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 2 neurons), and the second one was used to regress the tensile load-bearing capacity with MLP 7-19-1 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 1 neuron). FSW parameters, such as rotational speed, welding speed, and joint and tool geometry, were used as input data for ANN training. The quality of the FSW joint was assessed in terms of microstructure and mechanical properties based on a case study. The usefulness of both trained neural networks has been demonstrated. The quality of the validation set for the regression network was approximately 93.6%, while the errors for the confusion matrix of the test set never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. Predictive maps were developed and presented in the work, allowing for the selection of optimal parameters of the FSW process for three types of joints.

Mechanical properties and corrosion behaviour of dissimilar friction stir-welded Al 7075 and Mg AZ31 alloys with Cd and Zn interlayer

ABSTRACT The objective of present research work is to study joint strength and corrosion behaviour of dissimilar Friction Stir Welding (FSW) of Al 7075 and Mg AZ31 alloys with Cd and Zn interlayer. The FSW process performed at rotational speed 1300 rev min–1 and travel speed 20 mm min−1. The comparative study of mechanical properties and corrosion behaviour of FSW with Cd and Zn interlayers has been attempted for the first time. The result shows that the maximum tensile strength of 129 MPa has been achieved for FSW with Cd interlayer as compared to 85 MPa in the case of FSW with Zn interlayer. The corrosion rate was found in order of FSW with Cd > FSW with Zn > BM AZ31 > BM-7075. It has been concluded that FSW with Zn interlayer resulted in comparatively lower strength and better polarisation resistance than the FSW with Cd interlayer in 3.5 wt-% sodium chloride (NaCl) solution.

A heat source model for dissimilar Al/Cu friction stir welding process based on tool torque measurement

A heat source model for dissimilar Al/Cu friction stir welding process based on tool torque measurement

Modeling and Experimental Investigation of the Impact of the Hemispherical Tool on Heat Generation and Tensile Properties of Dissimilar Friction Stir Welded AA5083 and AA7075 Al Alloys.

This study investigated the effect of a hemispherical friction stir welding (FSW) tool on the heat generation and mechanical properties of dissimilar butt welded AA5083 and AA7075 alloys. FSW was performed on the dissimilar aluminum alloys AA5083-H111 and AA7075-T6 using welding speeds of 25, 50, and 75 mm/min. The tool rotation rate was kept constant at 500 rpm. An analytical model was developed to calculate heat generation and temperature distribution during the FSW process utilizing a hemispherical tool. The experimental results were compared to the calculated data. The latter confirms the accuracy of the analytical model, demonstrating a high degree of agreement. Sound FSW dissimilar joints were achieved at welding speeds of 50 and 25 mm/min. Meanwhile, joints created at a welding speed of 75 mm/min exhibited a tunnel-like defect, which can be attributed to the minimal heat generated at this particular welding speed. At a lower welding speed of 25 mm/min, a higher tensile strength of the dissimilar FSWed joints AA5083 and AA7075 was achieved with a joint efficiency of over 97%.

Multi-objective analysis of solid-state joints: Effect of tool pin shoulder clearance on thick dissimilar aluminum plates

ABSTRACT Friction stir welding is a well-established technique developed extensively over the past two decades for various applications. This study investigates the effect of repositioning the tool pin shoulder (0.18 mm) above the surface of a 12 mm thick dissimilar aluminum alloy (AA6063-AA5083) on tensile strength, welding temperature, and micro-hardness. Trials were conducted according to the L9 Taguchi method and further simulated using ABAQUS software. The input parameters spindle speed, tool pin length, and traversing speed were each tested at three levels. The impact of these parameters on weld quality was analyzed using ANOVA. Optimization of the process to achieve the desired temperature and tensile strength was carried out using grey relational analysis for both experimental and simulated results. Mathematical models were generated using the multi-variable regression method and the response surface method (RSM). Predicted data from the RSM model were compared to experimental outcomes, revealing a maximum deviation of 8.69% for temperature and 5.17% for tensile strength. Furthermore, the study demonstrated that ABAQUS accurately simulates the friction stir welding process with an accuracy of up to 93.71%.

Intelligent Prediction of FSW Physical Quantity and Joint Mechanical Properties

Friction stir welding (FSW) process parameters influence welding temperature field and axial force, which affect welding strength. At present, how the FSW process parameters of aluminum alloy 2219-T8 thick plates influence process physical quantity and how the process physical quantity changes the tensile strength about the welded joint are unknown. We focus on the intelligent prediction of FSW temperature, axial force, and mechanical properties, to provide a basis for FSW process control of aluminum alloy 2219-T8 thick plate. Firstly, we conducted the FSW experiment of aluminum alloy 2219-T8 thick plate. Then, we input the welding process parameters, set up a prediction model by particle swarm optimization-back propagation (PSO-BP) neural network to predict the peak temperature and axial force. Finally, we input the peak temperature and axial force, use genetic algorithm-back propagation (GA-BP) neural network to establish a weld tensile strength estimation model, and comply with the prediction of tensile strength.

Analyzing Defects in One-Step Double-Acting Friction Stir Welded of Hollow Aluminum Panel: Influence of Travel Speed

This study investigates hollow aluminum panels AA6061 produced through extrusion. The One-Step Double-Acting Friction Stir Welding (ODFSW) technique was used to weld the panels. The tool rotated at 1500 RPM, and the travel speeds were 15 and 30 mm/min. ODFSW is a friction stir welding process that uses two tools moving simultaneously on the top and bottom surfaces of the workpiece. This method offers an environmentally friendly alternative to conventional fusion welding, which requires more energy and generates higher greenhouse gas emissions. The research aims to evaluate the influence of travel speed on weld defects in ODFSW joints, including flash, surface surface grooves, and surface roughness. The results show that while higher travel speeds can improve weld quality. It also poses challenges in heat management and material mixing, leading to larger defects on the bottom side due to uneven heat distribution. At a travel speed of 15 mm/min, defects such as long flash, significant surface surface grooves, high surface roughness, and exit holes were observed, mainly due to excessive welding temperatures and extended contact times. In contrast, at 30 mm/min, although surface surface grooves on the top side were reduced, defects on the bottom side increased, along with higher surface roughness.

Mechanical characteristics of dissimilar friction stir welding processes of aluminium alloy [AA 2024-T351 and AA 7075-T651

Aerospace industries have remained dependent on aluminium alloys for airframe structural components manufacturing due to their superior strength, fracture toughness, and ability to resist corrosion. Especially, AA2024 and AA7075 have been the most prominent and timely tested robust aluminium alloys in these manufacturing sectors. However, joining these aluminium alloys through conventional fusion welding is difficult. The present investigation focused on the mechanical and metallurgical properties of these high-strength dissimilar aluminium alloys 2024-T351 and 7075-T651 using a Friction Stir Welding (FSW) process. The effects of factors such as rotational speed RS (800–1200 rpm), welding speed WS (20–60 mm/min), and tool plunge depth (0.2–0.4 mm) on the ultimate tensile strength (UTS) and yield strength (YS) have been evaluated. The experimental procedure employed is based on RSM. The fractured surface morphology was investigated using SEM. The investigation result showed higher tensile strength (147 MPa) at the combination of welding parameters (1200 rpm, 60 mm/min, and 0.4 mm). The fabrication industries became the great beneficiaries of this emerging technology of the FSW.

Combined Impact of SiC/TiB2 Nanoparticles in Friction Stir Welding of Different Aluminium Alloys

In this work, Friction Stir Welding process was done on the dissimilar alloys AA8014 and AA2024, with focus on analysing mechanical properties such as tensile strength (UTS), hardness (BHN), and wear rate (WRT). The influence of SiC/TiB2 nanoparticles, with Wt% ranging from 2 to 8 wt%, was examined on the mechanical properties. The rotational speed (RS) was changed from 1200 rpm to 1400 rpm, while the welding speed was kept at a constant 150 mm/min throughout the process. The results specified that an increase in RS to 1400 rpm, combined with 8 wt% , improved the ultimate UTS to 239.4 MPa. SEM examination of the fractured surface revealed that micro dimples were produced at this higher rotational speed. This phenomenon is attributed to the more intense stirring action at 1400 rpm, which leads to turbulent material flow in the stir zone. The resulting turbulence could cause uneven particle distribution and entrap air or other voids, contributing to the formation of micro dimples in the weld nugget.

Microstructural and corrosion behaviours of dissimilar friction stir welded aluminium alloys

In this study, the friction stir welding (FSW) process was employed to investigate the effect of tool geometry on the corrosion behaviour and microstructure of friction stir welded AA7075-7651 and AA1200-H19 using Central Composite Design. The workpieces were machined and welded, and the interfaces were milled. A 2-level full factorial experimental design was deployed using Response Surface Methodology (RSM). A rotational speed of 1500 rpm, welding speed of 30, 60, and 90 mm/min, and a 2° tilt angle of the tool with a plunge force of 7 kN were utilized. The results show that regardless of the tool geometry, multi-response optimum weldment can be achieved at 60 mm/min welding speed and a tilt angle. The microstructure of the optimal weldments presents an ‘onion ring’ pattern, indicating proper mixing of the alloys during FSW. Analysis of the corrosion behaviour revealed a decrease in the polarization resistance when the transverse speed increased from 30 to 90 mm/min, as polarization resistance has a direct relationship with corrosion rate. It can be concluded that FSW ensures excellent weldment, as evident in the microstructural evolution of the resulting weldments, and that tool geometry plays a significant role in the corrosion inhibition efficiency of the alloys.

Ispitivanje i optimizacija površine zavara i mehaničkih performansi termoplastičnih delova proizvedenih aditivnom proizvodnjom i zavarenih zavarivanjem trenjem

This study presents an experimental investigation into the weldability of ABS M30 (acrylonitrile butadiene styrene) plates produced by Additive Manufacturing (AM) using Friction Stir Welding (FSW). The effects of FSW process parameters on the yield stress and their optimal levels were determined using the Taguchi method. The optimal welding parameters were found to be a 16 mm tool shoulder diameter, 800 rpm tool rotation speed, and 10 mm/min traverse speed. The weld area of each sample welded using FSW was examined at a macroscopic level. The direction of tool rotation significantly affects the quality and strength of the FSW. When the FSW was performed with a clockwise rotation of the welding tool, a perfect weld could not be achieved. The tunnel effect resulted in gaps in the weld area of the samples at high rotation speeds. Differences were observed in the density between the weld area of the samples and the main parts.

Optimization of process parameter of FS-welding of aluminum-lithium alloy (AA8090) by using desirability analysis

This study explores the potential of Friction Stir Welding (FSW) for Aluminum Lithium (AA8090) Alloys, emphasizing the crucial role of experimental investigations in optimizing the FSW process. Using a vertical machine center (VMC) with a customized fixture and tool for FSW, factors like Rotational speed (RS), Tilt angle (TA), and Welding Speed (WS) are considered to enhance ultimate tensile strength (UTS) and % elongation (EL). A regression model based on Response Surface Methodology's Central Composite Design (CCD) is used to analyze UTS and % EL. The effectiveness of welded joints is comparable to the parent metal, with optimal UTS and EL achieved at 1428 rpm rotational speed, 36.4 mm/min welding speed, and 1.5° tilt angle. While significant interaction effects are observed in UTS, none are noted in EL. FSW joint performance is evaluated through microstructural analysis, microhardness distribution and fractography analysis.

Finite element analysis of friction stir welding process to predict temperature distribution

Finite element analysis of friction stir welding process to predict temperature distribution

The Influence of Tool Rotational Speed on Mechanical Properties of Friction Stir Welded Dissimilar AA5083-H112/AA6061-T6 Joints

The relatively new and innovative welding technique known as friction stir welding (FSW) has found widespread application in the automotive, railway, and aircraft industries. Due to engineering and financial considerations, welding of dissimilar metals is frequently carried out in these industries. During the friction stir welding process, three tool rotational speeds—910, 1500, and 2280 rpm—were utilized, while the welding speed remained constant at 30 mm/min. Several experimental works, including fractography, tensile tests, and observations of the microstructure, were carried out following welding. Results showed that ultimate tensile strength (UTS) and yield strength (YS) expanded as the tool rotational speed was expanded and the best UTS and YS, typically 228 MPa and 156 MPa at the speed of 2280 rpm. The precipitation of AA6061-T6 in the stir zone (SZ) and the increased degree of mixing were linked to the improved weld strengths.

Predicting Axial Force in Friction Stir Welded Joints of AA7075-T6 and AA2024-T3 Using Machine Learning Techniques

The friction stir welding (FSW) process has gained popularity in joining various types of aluminum alloys. The heat generated during the process is caused by the friction between the shoulder of the welding tool and the workpiece. The axial force (Fz) of the weld plays a crucial role in the welding process. If the axial force is insufficient to generate heat will result in defective workpieces. Therefore, the aim of this study was to predict the axial force of the FSW process of dissimilar aluminum alloys (AA7075-T6 and AA2024-T3) using machine learning techniques. The data used to create the predictive model was obtained from experiments involving 5 factors, each with 2 levels: 1) welding speed (mm/min), 2) rotational speed (RPM), 3) type of tools, 4) plunge depth (mm), and 5) dwell time (sec). The axial force was measured using a dynamometer with a sampling frequency of 10 Hz. The predictive model was created using all 4 algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. The performance of the four predictive models was evaluated using four metrics: mean absolute error (MAE), mean absolute percent error (MAPE), mean square error (MSE), and root mean square error (RMSE). The results showed that the AdaBoost algorithm had the best performance, with MAE, MAPE, MSE, and RMSE values of 509.08, 0.24, 452591.73, and 672.75, respectively. The AdaBoost algorithm was then used to predict the axial force using a dataset of 29,282 data, with predicted minimum average axial force of 1871.6 N. When compared to the axial force measured in the experiment, which was 1265 N, the results showed that the AdaBoost algorithm was capable of predicting the axial force with acceptable accuracy.