Friction Stir Welding Experiments Research Articles

Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

High strength AA2219-T8, Al-Cu alloy of 18 mm plate is welding by friction stir welding (FSW) technology and used for heavy-lift launch vehicles tank. During FSW thick AA2219-T8 plate, joint tensile strength and microhardness are affected by multiple interconnected variables including welding force, temperature, and geometric quantity of the butt face. The joint mechanical properties are difficult to predict and ensure. This paper proposed a hybrid predicting approach based on multi-source information fusion, called Down-Sampling and One-Dimensional Convolutional Neural Network (DS-1DCNN). Initially, FSW experiments were conducted to collect the axial force, welding surface temperature at advancing and retreating side, gap, and mismatch signals, which were fused through DS technology. To extract temporal information and features from signals, a 1DCNN prediction model was established, with the fused multi-source signals as input and the tensile strength and Vickers hardness as output. Subsequently, the optimal model architecture and hyper-parameters of 1DCNN were determined through experiments and grid search combined with K-fold cross-validation, and an online prediction system was developed. Finally, effective validation experiments were conducted and the experimental results agreed with the DS-1DCNN prediction results with an acceptable error of approximately 5%. Parameter sensitivity analysis revealed that the number of convolutional layers and learning rate were identified as critical parameters affecting the model performance. Additionally, time analysis showed that the calculation time of the prediction system was less than 200 ms which can meet the time requirements of real-time thick-plate FSW application. The research lays a foundation on the real-time quality monitoring in the process of FSW thick-plate butt joint.

Ultrasonic Influence on Macrostructure and Mechanical Properties of Friction Stir Welded Joints of Al/Mg Sheets with 2 mm Thickness.

Friction stir welding (FSW) and ultrasonic vibration enhanced FSW (UVeFSW) experiments were conducted by using 6061-T6 Al alloy and AZ31B-H24 Mg alloy sheets of thickness 2 mm. The suitable process parameters windows were obtained for the butt joining of Al/Mg sheets. The effect of ultrasonic vibration on the macrostructure and mechanical properties of the dissimilar joints was studied. The results showed that the width of the weld nugget zone (WNZ) was enlarged to some extent and the hardness distribution in WNZ was more uniform in UVeFSW. In addition, the application of ultrasonic vibration effectively promoted the interpenetration degree of dissimilar materials in the WNZ so that the mechanical interlocking on the bonding interface of dissimilar Al/Mg materials was enhanced. The facture positions were changed from the bonding interface in FSW to the boundary between WNZ and the thermo-mechanical affected zone, and the ductile fracture zone was expanded. The highest ultimate tensile strength was 205 MPa at the process parameters set of 1200 rpm-50 mm/min in UVeFSW in this experiment. The average ultimate tensile strength of FSW/UVeFSW joints was 172.3 MPa and 184.4 MPa, respectively, and the average ultimate tensile strength was increased by 7.02% with the introduction of ultrasonic vibration.

Strain-based multi-dimensional force sensing system for robotic friction stir welding

Accurately measuring the force in friction stir welding (FSW) is essential for monitoring and controlling the welding process. However, current research primarily focuses on force sensing in gantry FSW equipment, which has limited demand compared to FSW robot. Commercial force sensors are mostly designed for six-axis welding robot and lack for high-force hybrid FSW robot. To address this gap, we developed a wireless multi-dimensional force sensing system for hybrid FSW robot. Our system utilizes elastomer and incorporates 8 pairs of strain gauges to detect tiny deformations. Through calibration, we achieved a wider measurement range than most commercial sensors, ensuring high accuracy with low cross-talk. Extensive testing determined the static and dynamic characteristics of the system. We performed robotic FSW experiments, analysing the force/torque characteristics in each direction and investigating the effects of welding speed and plunge depth on axial force. The results demonstrate the stability and practicality of our system.

Thermal and Mechanical Investigation of Friction Stir Welding with Disparate Materials AA6061 and AA7075

Background:: The primary objective of this study is to assess the impact of welding conditions on the mechanical properties of friction stir-welded butt joints created from two distinct aluminium alloys, namely, AA6061 and AA7075. Friction stir welding (FSW), known for its innovation and low-energy solid-state bonding technique, was employed in this research. Methods:: FSW experiments were carried out on both AA6061 and AA7075 alloys using a computer numerical control (CNC) machine. The selection and design of the tool geometry were meticulous, with an emphasis on new pin profiles that are nearly flat at the weld contact point. Precisely, four distinct tool geometries were machined from HC-HCr (High carbon, high chromium steel): Circular, Square, Tapered third, and Triangular. Critical process variables that significantly influence weld quality include rotation speed (800 rpm-1400 rpm) and traverse speed (12 to 25 mm/min). These variables were carefully optimized to achieve flawless welds. During the friction stir welding process, the nugget zone undergoes significant deformation, leading to the formation of a new microstructure that substantially impacts the mechanical properties of the joint. Results:: This study comprehensively investigates the thermal and mechanical properties of friction stir welding using aluminium alloys AA6061 and AA7075, considering various tool shapes. Among the four tool shapes employed, two were found to yield higher hardness values (referred to as BH). Notably, the square-shaped tool produced the highest temperature, reaching up to 690ºC, as determined by thermocouple readings. Based on the findings, the optimal FSW parameters for enhancing hardness involve an axial feed and spindle speed of 800 rpm combined with a feed rate of 15 mm/min. These parameters were identified as crucial for achieving the desired mechanical properties in the friction stir-welded joints. Conclusion:: This study presents new developments in FSW technology, which may have patent implications.

Microstructure, microhardness and mechanical behavior of dissimilar AA7075/AA6061 alloys under friction stir welding

Friction stir welding (FSW) of dissimilar AA6061 and AA7075 aluminum alloys faces challenges such as an unclear forming mechanism and low welding efficiency. In order to improve welding efficiency and elucidate the variations in metallography, Microhardness, and mechanical properties of dissimilar alloy FSW joints, this article designed FSW experiments using the Taguchi method with welding speeds ranging from 600 to 1000 mm/min. We studied the relationship between material mixing and welding defects through macroscopic metallography, microscopic metallography, and Microhardness, while the impact of welding parameters on the microstructure and properties was evaluated. The results show that void defects do not exist in dissimilar alloy FSW joints, and the Microhardness changes are “U” and “W” shaped. Material mixing played a decisive role in determining the microstructure and properties of dissimilar aluminum alloy FSW joints, with increased rotational speed enhancing material flow. The order of influence on the mechanical properties of the joint was found to be plunge depth, welding speed, and rotational speed. Under the conditions of 1000 mm/min, 2400 rpm, and 0.25 mm, the yield strength (YS) and ultimate tensile strength (UTS) of the dissimilar alloy FSW joints reached 87.3% and 73.4% of the AA6061-T6 alloy, respectively.

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.

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.

Multi-objective optimization of process parameters during friction stir welding of similar AA6061 using MOORA

This work is an attempt to select the optimum process parameters for friction stir welding of similar AA6061 aluminum alloy based on multiple criteria decision-making approach. The friction stir welding experiments have been conducted according to the orthogonal L9 array and the chosen input parameters are tool rotational speed, feed, and tilt angle. The responses measured are tensile strength, hardness, and % of elongation of welded joint. The multi-criteria decision-making technique namely multi-objective optimization based on the ratio analysis (MOORA) is used to find the optimum process parameters combination. The optimum conditions are tool rotational speed of 1120 rpm, feed of 30 mm/rev and tilt angle at 1 o.

Prediction of the tensile strength of friction stir welded joints based on one-dimensional convolutional neural network

Friction stir welding (FSW) is a complex thermo-mechanical coupling process. Tensile strength is an important evaluation index of the mechanical properties of welded joints. How to realize the real-time prediction of tensile strength of the friction stir welded joints to reflect the dynamic change of welding state is a problem in the field. To solve this problem, this paper presents a multi-scale one-dimensional convolutional neural network (Multi-scale 1D CNN) prediction model using time series data of temperature and axial force as inputs to realize the online prediction of tensile strength of welded joints. Firstly, FSW experiments are carried out to obtain time series data of temperature and axial force. Tensile strength values of the welded joints is obtained by tensile tests. The time series data and tensile strength values are fused as a dataset. Then Multi-scale 1D CNN, traditional 1D CNN and Multi-channel 1D CNN prediction models are established and trained with the dataset, respectively. Finally, by comparing the prediction performance of the three models, Multi-scale 1D CNN is proved to be more suitable for analyzing time series data to feedback the dynamic change of tensile strength of the joints during welding.

Experimental investigations on the position of plates in friction stir welding of dissimilar alloys

Friction Stir Welding (FSW) is a solid-state welding procedure that involves heating and stirring the surfaces of two metal components to cause them to fuse together. The paper aims to present the influence of the plate position in friction stir welding of dissimilar aluminum alloys, of the same family. Friction stir welding experiments are conducted on AA6061 and AA6082 for different plate positions. The results of mechanical and microstructural characterization reveal that AA6082 on the advancing side provides better strength due to the material properties, temperature developed, and distribution along and across the weldments.

Evolution of microstructures and intermetallic compounds at bonding interface in friction stir welding of dissimilar Al/Mg alloys with/without ultrasonic assistance

Complete understanding of the evolution behaviors of the microstructures and intermetallic compounds (IMCs) along the interface materials flow path in friction stir welding (FSW) of dissimilar Al to Mg alloys is of great significance. In this study, conventional FSW and ultrasonic vibration enhanced FSW (UVeFSW) experiments of Al/Mg alloys were performed, and the instantaneous evolution features of the interface materials around the tool were "frozen" by using the "sudden stop" and simultaneous cooling techniques. The microstructures and IMCs formation at different locations around the exit hole were observed and characterized by scanning electron microscope, energy dispersive spectrometer and transmission electron microscope. It was found that before the materials started to deposit near the back of the tool, “IMC+Mg+IMC+Al” multilayer microstructure and simple IMC layer with (β+γ) sequentially emerged on the Al/Mg interface. With the application of ultrasonic vibration, the multi-layered interface structure only appeared at the middle stage of materials flow around the pin, and ultrasonic vibration just began to play a suppression role on the growth of two sub-layers IMC at a position where the materials deposit. With assistance of ultrasonic vibration in UVeFSW, the tool drove a larger volume of Mg alloy to move toward the retreating side, and the final IMCs thickness was thinner than that in FSW.

A Comparative Study of Mechanical and Microstructural Behavior for Metal Active Gas and Friction Stir Welded Micro-Alloyed Structural Steel

Manufacturing tiny components into gigantic structures seems unimaginable without welding connections, whether it is for materials, parts, structures, or equipment. In the recent competitive market scenario, manufacturing industries are desperately looking for a viable alternative to fusion-based conventional material joining technologies, to improve upon cost-effectiveness along with performance. The present investigation is to evaluate the performance of structural steel thick plate joints prepared by advanced solid-state friction stir welding (FSW) over conventional metal active gas welding (MAG). The FSW experiments were carried out with different tool designs and configurations. The outcomes were evaluated in terms of microstructural integrity and mechanical joint resistance. Impressive results were obtained with FSW joints, characterized by an almost defect-free microstructure with a leaner heat-affected zone and higher joint resistance as compared to conventional MAG-welded joints.

Analysis on friction stir welding process of 7022 aluminum alloy

The state of the simulation technology widely used in friction stir welding (FSW) studies requires greater and greater accuracy. An analysis is made of an FSW dynamic heat source model and related information. The model is combined with results from FSW experiments and the welding temperature field. A relationship is obtained for the friction coefficient between the tool and welding materials with temperature. By incorporating tool force measurements and analysis, a simulation model which is much more in line with actual FSW results is thus established. In the simulation process, the temperature of each part of the tool, the material model, fixture constraints, and the role of tool–material forces are analyzed and discussed in detail. In the new model, the different input heats on the advancing and retreating sides on the shoulder, the pin surround, and top of the pin are distinguished. The mechanical properties of the materials with change in temperature and the constitutive equations for the welding, before and after, are obtained. The contact relationship between the fixture and workpiece was built. In the welding process, the forces of the tool toward the welding material were applied. The 7022 aluminum alloy FSW process was simulated by the constructed simulation model. The temperature field, residual stress, and deformation in the simulations were in good agreement with the experiment results.

A dynamic prediction method of transient temperature in friction stir welding process

Establishing an accurate heat source model is the primary work in analyzing welding temperature field. We propose a novel transient temperature prediction method for friction stir welding by analyzing the material movement process to build an accurate heat source model. For verifying the present method, several groups of friction stir welding experiments were carried out, and the computed results obtained by the present method are compared with the experimental results. The effects of rotational speed, tilting angle, welding speed and normal force on the calculation results are analyzed. It is found that the peak temperature in the welding temperature field increases with the increase in rotating speed, tilting angle and normal force, and decreases with the increase of welding speed. The peak time decreases with the increase of rotational speed, tilting angle and welding speed, and almost does not change with the normal force.

Desirability function analysis based multi response optimization of process parameters during friction stir welding of AA6061-AA7075

The goal of this work is to choose the best friction stir welding process parameters for the alloy AA6061-AA7075 based on a multi-criteria decision-making technique. The tool rotational speed, feed, and tilt angle were selected as the input parameters for the friction stir welding experiments, which were carried out using a L9 orthogonal array. The measured responses are tensile strength, hardness, and elongation percentage of a welded joint. The multi-criteria decision-making technique namely Desirability Function Analysis (DFA) is used to find the optimum process parameters combination. The optimum conditions are tool rotational speed of 710 rpm, feed of 30 mm/rev, and tilt angle at 2o.

Experimental investigation on impact energy of friction stir welded aluminum and copper dissimilar joint using full factorial method

This research work carried out friction stir welding (FSW) of dissimilar aluminum AA3003-H12 and copper C12200-H01, with wide application in the refrigeration and heat exchanger industry. The main aim of this study is to investigate the influence of process parameters, i.e. pin type (PT), weld speed (WS), rotational speed (RPM), and shoulder diameter (SD) on impact energy (IE) of Al-Cu welded joint. The experimental study used the full factorial method with mixed levels of process parameters. Analysis of Variance (ANOVA) determines the significance of process parameters on impact energy. The results of the analysis of variance (ANOVA) shows that rotational speed (RPM) is the most influential process parameter contributing to the impact energy (IE) of dissimilar Al-Cu weld joint. The response optimizer tool in Minitab 18 software gives optimum weld conditions of process parameters for better weld performance. The FSW experiment with a tapered pin, weld speed of 16 mm/min, rotational speed of 1120 rpm, and shoulder diameter of 22.5 mm obtained the maximum impact energy value of 6.5367 J. The fine-grain recrystallization formed intermetallic compounds in the stir zone (SZ). These intermetallic compounds give a maximum microhardness of 382.24 Hv (0.1). The microstructure analysis of the stir zone (SZ) shows an equiaxed grain structure on the Cu side, while the Al side shows a fine recrystallized grain structure.

Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Vehicle weight has been a critical concern in the aerospace and automobile industries for decades. Integrating dissimilar aluminium and polymer hybrid structures is beneficial for weight reduction without affecting structural performance. In the present work, aluminium alloy 7475 and polyphenylene sulfide (PPS) sheets were joined using the friction stir welding (FSW) technology in lap joint configuration. A series of FSW experiments have been performed by the design matrix developed using response surface methodology. Tensile lap shear strength (TLS) is calculated for each experimental run. In this study, an attempt has been made to assess the potential of machine learning algorithms to predict the TLS of the joint. It was found that the support vector machine (SVM) model with RBF kernel was the most effective for predicting the TLS. Furthermore, FSW process parameters are optimized by means of the desirability approach. The optimal set to attain maximum TLS is identified as the tilt angle of 2°, welding speed of 5.12 mm/min and tool rotational speed of 1185.92 r/min. Finally, a confirmation test was performed to validate the optimal set and the adequacy of the developed SVM model. From the confirmation test, the error percentage between experimental and prediction values is less than 5%. Metallographic analysis revealed that the joining mechanism is the macro and micromechanical interlocking assisted by chemical bonding.

Temperature distribution and mechanical properties of FSW medium thickness aluminum alloy 2219

Friction stir welding (FSW) is a solid-state jointing technology, and has the advantages of high joint strength, low residual stress, and small deformation after welding. During FSW, the welding temperature directly influences the quality of the weldment. A heat generation model of FSW medium thickness aluminum alloy 2219 is established considering the friction heat generated at the interface between the tool and the workpiece and the plastic deformation heat of the weldment material near the tool. The heat transfer model is set considering heat conduction, convection, and radiation. Using JMatPro technology, temperature-related material parameters of aluminum alloy 2219 are obtained. The built heat generation model is imported into the simulation software ABAQUS through the DFLUX subroutine, and the simulation of FSW process is achieved. The effectiveness of the simulation is verified by FSW experiments. The simulation has high prediction accuracy. Based on the simulation, the influence of welding parameters on temperature distribution is explored; subsequently the influence of welding temperature on mechanical properties of welded joint is also studied. The research guides the prediction of the temperature distribution and the improvement of the mechanical performance of FSW medium thickness aluminum alloy 2219.

Parametric studies of friction stir welding with tool using a vibrating shoulder

The friction stir welding (FSW) is recent solid state welding process with ability to weld literally most materials. FSW utilizes frictional heat to soften and forge the components together under an axial force. The consolidation occurs by material movement through a stick-and-slip stirring action of the tool. The stick and slip action creates a load-no-load condition which is evident in the minor-asperities in the welding force curve. This paper has made a novel attempt to even out the minor asperities by devising a novel spring supported tool-shoulder which evens out the welding load variation. In additional to even out the force it also reduces the heat transfer from the welding area to the tool body by harmonic vibratory movements. The harmonic movement in turn creates a vacuum which literally reduces the heat transfer to nill. Furthermore, the harmonic forging of the material being processed results in the defect-free joint. The experimental validation was done by performing FSW experiments about L9 orthogonal array and the experiment results were modeled and compared with the welds made by classic FSW tool.

Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO

This paper explicates the joining of AA 6061/TiO2 composites by the friction stir welding (FSW) process. FSW experiments were conducted as per the three factors, three-level, central composite ivy– face-centered design method. Mathematical relationships between the FSW process parameters, namely tool geometry, welding speed, and tool rotational speed, and the output responses such as hardness, yield strength, and ultimate tensile strength were established using response surface methodology. Adequacies of established models were assessed through the analysis of variance method. Further, the paper elucidates the application of the teaching–learning-based optimization (TLBO) algorithm to identify the optimal values of input variables and to obtain an FSW joint with superior mechanical properties. The optimized experimental condition obtained from the TLBO yields an FSW joint with a UTS of 174 MPa, yield strength of 120 MPa, and hardness of 126HV. The study revealed that the result of the TLBO algorithm matched the findings of the FSW experiments.