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Review
Effect of Process Parameters on Friction Stir Welded Joints Between Dissimilar Aluminum Alloys: A Review
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Submitted:
30 May 2023
Posted:
30 May 2023
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Abstract
Friction Stir Welding is a suitable solid-state joining technology to connect dissimilar materials. To produce an effective joint, it requires a phase of optimization which leads to the definition of process parameters such as pin geometry, tool rotational speed, rotation direction, welding speed, thickness of the sheets or tool tilt angle. The aim of this review is to present a complete and detailed frame of the main process parameters and their effect on the final performance of a friction stir welded joint in terms of mechanical properties and microstructure. Particularly, the attention was focused on the connection between different aluminum alloys. Moreover, the experimental results were correlated to the development and the applications of tools, which can effectively use in the design of the manufacturing process, such as finite element analyses, artificial neural networks, statistical studies. The review wants to be also a point of reference to identify the best combinations of process parameters based on the dissimilar aluminum to be joined.
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Subject: Engineering - Industrial and Manufacturing Engineering
1. Introduction
Aluminum alloys are classified as non-heat-treatable and heat-treatable alloys. The first group includes pure aluminum alloys (series 1xxx) and alloys with manganese (3xxx series), silicon (4xxx series), and magnesium (5xxx series), as primary alloying element. The second group includes aluminum alloys with copper (2xxx series), magnesium and silicon (6xxx series), and zinc (7xxx series), as main additives [1]. Recently, thanks to the possibility of exploiting the best properties of each series, the aluminum alloys are widely combined in many industrial fields including automotive, aerospace, shipbuilding [2,3,4,5,6].
In this context, the joining process is strategic to create a stable and effectiveness connection. Particularly, in all cases, solid-state and liquid-state welding are the primary technology [7,8]. Whereas liquid-state welding is characterized by significant drawbacks (i.e., formation of intermetallic compounds with a low plastic deformability that makes brittle the joint), solid-state welding is more suitable to join aluminum. It includes the following technologies providing a direct metal-to-metal contact with adequate pressure to generate a performant joint without the production of intermetallic compounds: explosion welding and transition joints, magnetic pulse welding, roll bonding, friction stir welding (FSW), friction bit joining, and ultrasonic spot welding [9].
Among these technologies, friction stir welding is a sustainable and green solution in terms of energy consumptions and environmental impact [10]. Moreover, it is characterized by a limited set of process parameters; tool rotational speed, weld speed, axial force, and tool tilt angle are the main parameters that are involved in making high-strength joints [11]. This fact allows to find experimentally the optimal combination to produce effective joints that can used in several structural applications.
The process works by using a rotating tool made of a hard material, such as tungsten carbide, to generate friction and heat between the two workpieces. The heat softens the material and allows the tool to penetrate the surface. The tool then moves along the joint, stirring the softened material together and forming a solid bond.
FSW is particularly useful in joining aluminum and other non-ferrous metals. It can be used to join plates, sheets, and extrusions with a thickness ranging from 0.5 mm to several centimeters. The process can be automated, making it suitable for mass production. One of the main advantages of FSW is that it produces a joint with no porosity, solidification cracks, or other defects that can weaken the joint. This makes the process particularly useful in applications where the joint must be strong and reliable, such as in the aerospace industry. Additionally, FSW produces minimal distortion and reduces the need for post-welding treatments. Despite its advantages, FSW has some limitations. It is not suitable for joining materials that have a large difference in thickness or that have complex shapes. Additionally, the process can be slower than traditional welding methods, which can be a disadvantage in some applications.
Many studies, concentrated in the last ten years, have studied the FSW of dissimilar Al alloy joints focusing on several aspects such as process optimization, effect of process parameters, microstructure, mechanical properties, and heat treatments [12,13,14]. The aim of this review is to present how the process parameters influence the properties of a friction stir welded joint between two dissimilar aluminum alloys. Particularly, the work is structured in two main sections:
- First, each process parameter is investigated in terms of influence on the joint performances,
- Then, the attention is focused on the tools used to predict or better investigate these effects such as finite element analyses, artificial neural networks, statistical studies.
2. Effect of process parameters
Optimizing the process parameters of friction stir welding is significantly influenced by the complex thermal and mechanical interactions induced during the welding process. For this fact, it is not possible to identify a unique strategy to join two similar or dissimilar materials with this process. Consequently, there a wide literature that investigates the best combinations of parameters as a function of the materials to be joined.
Preliminarily, when the attention is focused on dissimilar materials and, especially, on the dissimilar aluminum, it is much needed to highlight the role played by the type of aluminum alloy (i.e., heat-treatable, or not heat-treatable) and the thickness considering that the large number of authors work with thin sheets.
The choice of aluminum alloy can significantly influence the welding process and the resulting weld properties.
Heat-treatable aluminum alloys, such as the 2XXX, 6XXX, and 7XXX series alloys, derive their strength from a precipitation hardening process. These alloys contain alloying elements like copper, magnesium, and zinc, which form strengthening precipitates during a specific heat treatment cycle. The high temperatures generated during FSW can partially or completely dissolve the strengthening precipitates in the heat-affected zone of heat-treatable alloys. This leads to a localized softening effect, reducing the overall strength of the weld in this zone. The heat generated during FSW may also cause a redistribution of precipitates in the weld zone, affecting the mechanical properties. The extent of redistribution depends on factors like the alloy composition, welding parameters, and cooling rate. Heat-treatable alloys may require a subsequent post-weld heat treatment to restore the desired mechanical properties after FSW. This process involves a specific temperature and time cycle to re-precipitate the strengthening phases, thereby regaining the desired strength [15].
Non-heat-treatable aluminum alloys, such as the 1XXX, 3XXX, and 5XXX series alloys, do not respond to precipitation hardening. They primarily gain strength through cold working or strain hardening. Non-heat-treatable alloys generally exhibit minimal softening effects in the heat-affected zone during FSW. Since their strength relies on cold working, the localized heating in FSW does not significantly affect their mechanical properties. FSW can induce grain refinement due to the severe plastic deformation imposed by the rotating tool, enhancing certain properties like tensile strength and fatigue resistance. Unlike heat-treatable alloys, non-heat-treatable alloys typically do not require post-weld heat treatment. The mechanical properties of the weld joint are largely retained without additional heat treatment [16].
When friction stir welding a joint between a heat-treatable aluminum alloy and a non-heat-treatable aluminum alloy, several factors can influence the behavior of the welded joint. Heat-treatable and non-heat-treatable alloys have different mechanical properties. The heat-treatable alloy typically has higher strength, while the non-heat-treatable alloy may have lower strength but better formability. This mechanical mismatch can affect the overall strength and performance of the welded joint. The joint may exhibit variations in strength, ductility, and toughness, particularly at the interface between the two alloys. During FSW, the heat generated by the frictional forces and the mechanical mixing of the materials can promote the formation of intermetallic compounds at the joint interface. These intermetallics may have different mechanical properties than the base alloys, which can further influence the joint behavior. The type, volume, and distribution of intermetallics can vary depending on the specific alloy combination and welding parameters used. The welding process can induce microstructural changes in both the heat-affected zone and the weld zone. The extent of these changes depends on factors such as the alloy composition, welding parameters, and cooling rate. Differences in microstructure, such as grain size, phase distribution, and precipitate formation, between the heat-treatable and non-heat-treatable alloys can affect the joint's mechanical properties, including strength, hardness, and corrosion resistance. FSW can introduce residual stresses in the welded joint due to thermal expansion and plastic deformation. The combination of dissimilar alloys can lead to differential thermal expansion and contraction, resulting in residual stress concentrations. Residual stresses can influence the joint's distortion, crack susceptibility, and overall mechanical behavior. Finally, the response of the dissimilar alloys to post-weld heat treatment may differ. Heat-treatable alloys often require specific heat treatment cycles to achieve the desired mechanical properties. However, the non-heat-treatable alloy may not respond to traditional heat treatments. Consequently, careful consideration should be given to the selection and optimization of these processes for dissimilar aluminum alloy joints.
Also, the thickness of the sheets being welded in FSW can have a significant effect on the welding process and the resulting weld quality [17].
Thicker sheets require more heat input to raise their temperature to the desired range for FSW. The increased thickness implies a larger volume of material to be heated, which requires more time and energy. Consequently, thicker sheets may require adjustments in the welding parameters, such as increased rotational speed and downward force, to generate sufficient heat for effective stirring and plasticization of the material. Thicker sheets have also a higher propensity for heat dissipation due to their larger volume, resulting in a more significant temperature gradient across the thickness during FSW. This non-uniform temperature distribution can affect the metallurgical properties of the weld, including the formation of defects such as voids, cracks, or incomplete bonding. Proper control of process parameters, such as rotational speed, travel speed, and dwell time, is crucial to ensure adequate heat input and minimize thermal gradients.
Thicker sheets may exhibit higher levels of residual stresses due to the larger thermal gradients and associated thermal expansion and contraction effects. These residual stresses can affect the structural integrity of the weld and may require post-weld heat treatment or other stress relief techniques to mitigate their influence.
The thickness of the sheets influences the material flow and mixing characteristics. Thicker sheets tend to exhibit slower material flow due to their higher thermal mass and increased resistance to deformation. This can result in variations in the mixing of material between the advancing and retreating sides of the weld. To compensate for this, optimized tool geometry and process parameters can be employed to promote better material mixing and achieve a homogeneous weld.
Thicker sheets generally offer increased joint strength in FSW due to the larger bonded area and more substantial material volume participating in the welding process. However, the increased thickness can also introduce challenges such as inadequate heat input, insufficient mixing, or defects if not properly addressed. To ensure high-quality welds, it is essential to optimize the process parameters, tool design, and post-weld inspections to account for the specific characteristics of thicker sheet materials.
The specific parameter adjustments in friction stir welding (FSW) for tool rotational speed, welding speed, and axial load as a function of sheet thickness can vary depending on the material being welded and other factors [18,19].
2.1. Tool shoulder and pin geometry
The tool used in friction stir welding is typically made of a hard, wear-resistant material such as tungsten carbide, tool steel or ceramics. It has a cylindrical shape with a shoulder at one end and a pin at the other [20]. The shoulder is larger in diameter than the pin and is used to apply downward force to the workpieces and to contain the material being stirred during the welding process.
The pin, which is the active part of the tool, has a specially designed profile that varies depending on the material being welded and the desired welding parameters. The pin profile typically includes a threaded or fluted section that helps to mix and stir the material being welded, as well as a smooth section that creates a solid-state bond between the workpieces.
The tool is typically rotated at a high speed (usually several thousand revolutions per minute) and is traversed along the joint line at a controlled rate. The rotation and movement of the tool generate heat through friction, softening the material and causing it to deform plastically. The softened materials are then stirred together by the tool, forming a solid-state weld without the need for filler material or fusion.
The tool geometry in FSW is a critical factor in determining the quality, strength, and efficiency of the welding process. The design and optimization of the tool geometry are important considerations in achieving successful and cost-effective FSW [21].
Generally, the size of the pin and shoulder of the tool determines the size of the stir zone, which is the area where the material is plastically deformed and mixed (i.e., a larger tool pin diameter produces a larger stir zone, while a larger shoulder diameter produces a wider heat-affected zone). The shape and design of the tool pin and shoulder affect the direction and magnitude of material flow during the welding process. A pin with a threaded or fluted design can create a more efficient mixing of the material, while a flat or smooth pin produces less material mixing. The tool geometry affects the amount of heat generated during the welding process. A larger tool shoulder diameter produces more heat due to the increased surface area of contact with the workpiece. The strength of the resulting weld joint is affected by the tool geometry, particularly the pin geometry. The shape and size of the tool pin affects the amount of plastic deformation and mixing that occurs during the welding process, which in turn affects the strength and quality of the resulting joint. Finally, the geometry of the tool affects the rate of tool wear during the welding process. A tool with a more complex geometry may wear faster than a simpler tool, which can increase the cost of the process and reduce productivity.
Typical tool pin profiles are cylinder or straight cylinder, square, hexagonal, triangular, threaded cylinder, cylindrical cam, conical, taper, pentagonal, taper cylindrical tool, taper square tools [21]. Commonly, shoulder profiles employed are flat, concave, and convex. Additional features on the pin such as a spiral or a groove improve frictional behavior as well as material flow [9]. The material stirring and mixing are significantly influenced by the choice of pin profile.
Verma et al. [22] study the effect of tool pin profile on friction stir welded joints between dissimilar plates made of AA6061-T6 and AA5083-O. Amongst different types of tool pin profiles, the FSW tool having straight cylindrical pin profile is characterized by maximum strength and elongation of the joint for different combinations of welding parameters. Furthermore, based on multiple response optimization, they determine that the welded joint created using the pin tilted at 1.11° and operating at a tool speed of 1568 rpm and a feed rate of 39.53 mm/min achieves the maximum ultimate tensile strength of 135.83 MPa and a tensile elongation of 4.35%. In their study, Palanivel et al. [23] examine the impact of different shoulder profiles on the combination of AA5083 and AA6351. Three shoulder features are utilized - partial impeller, full impeller, and flat grove. Results indicate that the full impeller shoulder tool is most effective in producing superior mechanical strength, attributed to its ability to generate enhanced material flow.
Dissimilar Al alloy combinations are joined using cylindrical or conical pin profiles, which may feature threads or threads with flats, by evidencing how the pin profile significantly affects material stirring and mixing. Various pin profiles, such as cylindrical or conical shapes, with additional features like threads or threads with flats, are utilized for dissimilar aluminum alloy combinations. When the pin profile lacks threads, it offers a smaller surface area for material interaction. On the other hand, threaded and flat features on the pin increase the contact area, and the threads guide the material flow around the pin in both rotational and translational directions. Hasan et al. [24] investigate the effect of pin flute radius during the joining process of dissimilar AA7075-T651 and AA2024-T351 aluminum alloys. Particularly, they focus on five pin tools with different flute radii (i.e., 0, 2, 3, 6, and ∞ mm) under a specific combination of spindle speed and traverse rate (900 rpm/150 mm/min), by observing that a pin tool with a flute of radius equals to that of the pin leads to the strongest joint. Kalemba-Rec et al. [25] study two different types of tools for welding 7075-T651 and 5083-H111 alloys. Both tools have similar dimensions and consist of a spiral shoulder, but with different pin design: i.e., triflute or tapered with a thread. Particularly, triflute pin guarantees the highest tensile strength and efficiency defect-free joint with a wider area of the stir zone respect the other kind of pin with 5083 on the advancing side and 7075 on the retreating side, using a tool rotational speed of 280 rpm. Better mixing of materials is obtained at higher rotational; however, under these conditions, the weld microstructure shows more defects such as porosity, voids, or wormholes. Ilangovan et al. [26] investigate three 5.7 mm length pin profiles, namely, straight cylindrical, threaded cylindrical, and tapered cylindrical to fabricate AA 5086-O (in retreating side) and AA 6061-T6 (in advancing side) aluminum joints, using a tool rotational speed of 1100 rpm, a welding speed of 22 mm/min and an axial force of 12 kN. The high-speed steel tool is characterized by a shoulder diameter of 18 mm and an inclination of 1 degree. They observe that i) the straight cylindrical pin profile is not effective because it induces cross-sectional macro level defects in the stir zone; ii) threaded and tapered cylindrical pin profiles guarantee defect-free joints with similar tensile properties; iii) the threaded cylindrical pin profile is preferred due to the formation of finer and uniformly distributed precipitates, circular onion rings and smaller grain. It contributes to better flow of materials between the two alloys and the generation of defect free stir zone. Additionally, this configuration yields elevated hardness values, measuring 83 HV in the stir zone, as well as a higher tensile strength of 169 MPa compared to the other two profiles. The enhanced hardness is primarily attributed to the formation of fine grains and intermetallics within the stir zone. Furthermore, the reduced size of weaker regions, such as the thermomechanical affected zone and the heat-affected zone, contributes to the overall improvement in tensile properties. In welding of 5052-H32 (in advancing side) and 6061-T6 (in retreating side) aluminum alloys this behavior changes. In fact, Balamurugan et al. [27] find that using a M2 HSS tool characterized by a pin height of 4.7 mm, a shoulder diameter of 18 mm with two different pin profiles (i.e., taper cylinder and threaded cylinder), under constant welding speed and tool rotation speed of 60 mm/min and 900 rpm respectively, generates a joint characterized by better tensile strength, larger nugget area, and smoother surface finish. However, the taper pin profile leads to a so fine grain microstructure that the connection results strong.
Recrystallization is a process by which the microstructure of a metal is transformed from a deformed, or strained, state to a more relaxed, or strain-free, state. During FSW, the high temperatures generated by the frictional heat of the rotating tool cause the microstructure of the metal to become deformed and strained. As the tool moves along the joint line, it creates a region of heat-affected zone on either side of the weld. Within this zone, the metal experiences a range of temperatures, from below its recrystallization temperature to well above it. As the tool passes through the metal, it causes the grains to deform and align themselves in the direction of the tool's rotation. However, as the metal cools, the grains attempt to revert to their original, strain-free state. This causes the grains to recrystallize and grow, with the new grains being oriented in a more relaxed, strain-free manner. The recrystallized grains help to reduce the residual stresses that are present in the weld, leading to a stronger and more ductile joint. Overall, the recrystallization phenomenon in FSW is an important aspect of the process that helps to ensure a high-quality weld. By reducing residual stresses and improving the ductility of the joint, recrystallization can help to improve the mechanical properties of the welded components and ensure that they are able to withstand the stresses and strains of their intended application [28,29].
The tool pin profile plays an important role in determining the extent of recrystallization that occurs during friction stir welding (FSW). The shape, size, and geometry of the tool pin can affect the temperature distribution, strain distribution, and shear deformation in the material during the welding process, which in turn can affect the degree of recrystallization that occurs. A tool pin with a larger diameter and a more rounded profile will generate more heat and cause more material flow during FSW, leading to a greater degree of recrystallization. This is because the larger pin will produce more frictional heat, leading to a higher temperature in the material, and the more rounded profile will result in a greater degree of material flow, causing more grains to be deformed and realigned. Conversely, a tool pin with a smaller diameter and a more angular profile will generate less heat and cause less material flow, resulting in a lower degree of recrystallization. This is because the smaller pin will produce less frictional heat, leading to a lower temperature in the material, and the more angular profile will result in less material flow, causing fewer grains to be deformed and realigned. In addition to the size and shape of the tool pin, the pin material can also affect the degree of recrystallization. A tool pin made of a material with a lower thermal conductivity, such as tungsten or molybdenum, can generate more heat during FSW, leading to a higher degree of recrystallization. On the other hand, a tool pin made of a material with a higher thermal conductivity, such as copper, can dissipate heat more quickly, leading to a lower degree of recrystallization. Consequently, the only profile is not a factor that can influence the grain of the microstructure, and this can lead to different behavior as observed between [26] and [27].
Studies on the polygonal pin profiles evidence a significant pulsating effect in the dissimilar joints, during the material stirring and mixing, leading to the adhesion between material and pin. For this fact, the pins more suitable are cylindrical or conical [9]. Tiwan et al. [30] study the microstructural and mechanical properties of dissimilar joints between AA2024-O and AA6061-T6, at varying tool rotation speed (i.e., 900, 1400, 1800 rpm), by observing that the pin geometry affects the size of the stir zone. Particularly, a tool having a cylindrical profile, at the tool rotation speed of 1400 rpm, is more suitable because it allows the lower sheet material around the pin to flow upward during welding without any retardation in contrast to a stepped pin. Consequently, simplicity is preferable to complexity.
When dissimilar materials are joined, polygonal pin profiles can generate several defects such as voids, tunnel, cracks, and fragmental defects [31]. In advancing side AA6061-T6 to retreating side AA7075-T651 friction stir welded joints, Raturi et al. [32] investigate four different pin profiles namely cylindrical, cylindrical tapered, cylindrical threaded with three flat faces, and truncated square pyramidal shaped hereafter referred as trapezoidal tapered, by changing both the rotational speed (i.e. 660, 900, 1200, 1700 rpm) and the feed rate (i.e., 36, 63, 98, 132 mm/min). They observe that cylindrical threaded with three flat faces tool pin and cylindrical grooved tool pin with suitable intermediate tool rotation and feed rate lead to good tensile and flexural strength. The quality of friction stir welded joints, as well as the tensile strength and flexural load of the welds, are predominantly influenced by two factors: tool pin profiles and tool rotational speed. Excessively high rotational speed can lead to inferior nugget shape and inadequate joining of dissimilar metals. This is caused by the generation of excessive heat, which results in intense material softening. Consequently, poor friction, slipping, insufficient material delivery, and ultimately, weak joint strength may occur. Similarly, a high feed rate can also result in reduced strength of dissimilar friction stir welded joints. An examination of the fracture surface reveals that joints prepared with appropriate tool pin profiles and process parameters exhibit ductile failure, as evidenced by the presence of micro-voids and dimples in the well-bonded region. However, in contrast, some joints prepared with very high tool rotational speeds exhibit tearing, rupture, and brittle failure.
The decrease in the mechanical performance from a simple pin profile (i.e., triangular) to a complex pin profile (i.e., hexagonal) is due to the decrease in traverse force and the enhanced structural stiffness with an increase in the number of pin sides and the resulting reduction in the bending moment and shear force [33]. Yuvaraj et al. [34] investigate simple HSS tool pin profiles (i.e., square, cylindrical, and triangle) in the welding of AA7075-T651 and AA6061 aluminum plates, by finding that the best joints result for a square profile tool pin with a tool offset of 0.9 mm, and a tool tilt angle of 2 degree. In this case, the joint exhibits fine grains along the stir zone due to adequate heat generation. Furthermore, the triangular pin reveals granular grain structure due to an additional heat generation and a consequent turbulent flow of material. Krishna et al. [35] investigate three pin profiles (i.e., straight cylinder, straight square and tapered hexagon) on Al 6061, in retreating side, and Al 7075, in advancing side, joints. They find that, by using straight cylinder tool pin profile, with a rotational speed of 950 rpm, a welding speed of 60 mm/min and 6061 in advancing side, it is possible to obtain higher mechanical properties due to geometrical configuration of the tool pin that does not show sharp edges providing also smooth and perfect welding. Whereas other samples of square and taper hexagon tool pin profile having sharp edge. The other configurations, characterized by sharp edge, lead to no efficient and smooth welding. El-Hafez and El-Megharbel [36] weld dissimilar aluminum alloys AA2024-T365 and AA5083-H111 by using square, triangular, and stepped. They find that the square pin produces the best strength, coupling with a welding speed of 16 mm/min and a rotational speed of 900 rpm, due to the pulsed action (four pulses per revolution) that produces a good metal flow and, consequently, a good stirring, in agreement with [37,38]. Shine and Jayakumar [39] perform dissimilar FSW between AA5083-H111 and AA6061-T6 aluminum alloy by using three pin profiles (i.e., straight square, threaded cylinder, and tapered cylinder. From the experimental results, they observe that the straight square pin shows greater hardness values in the weld nugget zone and higher tensile strength in comparison to the others.
2.2. Tool tilt angle
The tilt angle refers to the angle between the FSW tool axis and the workpiece surface. It is an important process parameter that can affect the quality and properties of the welded joint [40,41]. The tilt angle can be adjusted during the FSW process to control the heat input and material flow. Generally, a larger tilt angle results in a higher heat input and more material flow, which can lead to better mixing and homogenization of the welded material. This can result in a more uniform grain structure, improved mechanical properties, and increased joint strength. Moreover, a larger tilt angle can be useful for welding thicker materials or for achieving a desired weld shape. However, if the tilt angle is too large, it can cause defects such as tunneling and hooking, which can weaken the joint [42]. In addition, a larger tilt angle can also increase the likelihood of thermal distortion and residual stresses in the welded joint. Conversely, a smaller tilt angle can result in a lower heat input and less material flow, which can reduce the risk of defects and thermal distortion. However, if the tilt angle is too small, it can also cause defects such as hooking, as mentioned earlier.
Yuvaraj et al. [34] optimize the tool tilt angle in AA7075-T651/AA6061 joints by investigating the following values: i.e., 2, 3 and 4 degrees. Applying a statistical technique, they observe that the tool tilt angle is the most governing factor influencing the friction stir welded dissimilar joint tensile strength. When the tool tilt angle increases, the gap expands between the work piece and tool [43]. Particularly, the square profile tool pin and a great tilt angle of tool (i.e., 3 degrees) improve the mechanical properties of the joint of the weld joint.
2.3. Tool rotational speed
The tool rotational speed represents the speed at which the welding tool rotates as it moves along the joint between the two pieces of metal being welded. During the FSW process, the rotating tool generates heat and friction, which softens the metal and creates a plasticized region around the tool. The tool then moves along the joint, pushing the softened metal behind it to create a solid-state weld.
It is a critical parameter in FSW, and it has several effects on the welding process [44] by influencing:
- Heat generation: as the tool rotates, it generates frictional heat due to the contact between the tool and the workpiece, controlling heat generation or heat input as they relate to the material plastic flow [12]. Higher rotational speeds result in more heat generation, which can cause the material to soften and lead to better mixing and bonding between the two workpieces.
- Weld quality: a too low rotational speed can result in incomplete weld formation and poor bonding between the two workpieces. On the other hand, if the rotational speed is too high, it can lead to defects in the weld, such as poor surface (flash), voids, porosity, tunneling or formation of wormholes because of the excessive heat input.
- Tool wear: higher rotational speeds can lead to more wear on the tool, which can reduce its lifespan.
- Welding force (i.e., required to push the tool through the workpiece): The rotational speed of the tool can also affect the force. Higher rotational speeds generally require higher forces to maintain the tool's position and prevent it from slipping out of the joint.
Moreover, this parameter affects differently the joint behavior as a function of the sheet aluminum alloy [48]. Changing the tool rotation rate influences the size and macrostructure of weld nugget zone in a friction stir welded AA2524-T351 aluminum alloy. The width of this zone increases by increasing the tool rotation rate. As the tool rotates at a high speed, the area of the recrystallized zone expands with increasing temperature, leading to this phenomenon [49]. Generally, insufficient energy is provided for dynamic recrystallization at lower rotational speeds, resulting in incomplete dynamic recrystallization and ineffective grain refinement strengthening. At higher rotational speed, the higher strength in weld nugget zone is attributed to smaller grain size [50]. In the weld of AA5086-H32 alloy, lower rotational speed produces various defects due to inadequate heat is produced resulting in improper softening of the material [51]. The excessive heating and deformation caused by tool rotation in AA5052-O lead to an increase in b-phase (Mg2Al3) particles through magnesium atom diffusion towards grain boundaries. The dissolution of these intermetallic particles within the weld nugget zone subsequently results in reduced joint strength [52]. As the rotational speed of the FSW tool increases for the 6082-T6 aluminum alloy, the weld temperature first rises and then falls, leading to the formation of smaller equiaxed recrystallized grains in the nugget zone. Moreover, the hardness of the nugget zone increases as the rotational speed is raised to 1200 rpm due to the enhanced dislocation density. This increase in dislocations is attributed to the precipitation and dissolution of the second phase, as well as the refinement of the aluminum matrix grain size in the microstructure of the zone [53]. For friction stir-welded 7075-T6 Al alloys, exists a strong relation between rotational speed and weld properties: i.e., by raising the rotating speed from 600 to 1550 rpm increases the average nugget grain size from 6.8 to 8.9 mm. Also, at a medium rotating speed, the optimum mechanical properties are achieved. [54].
Several studies on FSW involve dissimilar aluminum alloys [12,55]. Ghaffarpour et al. [56] join dissimilar aluminum alloys of 5083-H12, in retreating side, and 6061-T6, in advancing side, changing the rotational speed (i.e., 700, 1600, 2500 rpm) in combination, respectively, with the pin diameter (i.e., 2, 3, 4 mm), the shoulder diameter (i.e., 10, 12, 14 mm), and the traverse speed (i.e., 25, 212.5, 400 mm/min). They find that the effect of the pin diameter is not as pronounced as the effect of the rotational speed. As the rotational speed and pin diameter increase, the input heat increases, resulting in higher tensile strength. Moreover, the effect of the rotational speed is more significant compared to the effect of the traverse speed and the shoulder diameter. Thermocouple measurements, tool torque, extent of material mixing, and macrostructural observations all indicate that the temperature under the tool is more strongly dependent on the rotation than the traverse speed as observed also in AA5083-AA6082 joints [57]. The increase in tensile strength with greater friction heat can be attributed to the improved mixing of dissimilar alloys due to proper stirring resulting from the higher heat input. Additionally, the plasticization effect during FSW is enhanced at higher heat inputs. Consequently, softer materials are easier to mix and stir. Nevertheless, the strength exhibits a maximum value with an increase in heat input, implying that further increments in heat generation lead to a reduction in strength beyond a certain optimum heat level. The lowest hardness is recorded in the heat affected zone of the AA6061-T6 sheet. It is also observed that by increasing the tool rotational speed, the hardness of the mixing zone is reduced. This can be explained in the following ways. Firstly, the higher rotational speed generates more heat, leading to local annealing in both sheets. Secondly, the frictional heat increases the temperature above the aging temperature of the 6061-T6 sheet. Consequently, the fine Mg2Si precipitates, which serve as the hardening phase in AA6061, either dissolve or grow, leading to a decrease in hardness. This same phenomenon can occur in 5083-H12 as well. Furthermore, excessive heat can cause grain growth in both alloys, thereby contributing to decrease strength and hardness. Consequently, the optimal rotational speed takes intermediate values [58]. Palanivel et al. [59] make AA5083-H111 (in retreating side)/AA6351-T6 (in advancing side) joints using three different tool rotational speeds (i.e., 600 rpm, 950 rpm and 1300 rpm) and five different tool pin profiles (i.e., straight square, straight hexagon, straight octagon, tapered square, and tapered octagon), with constant welding speed of 60 mm/min, axial force of 8 kN and tilt angle of 0 degrees. The high carbon high chromium steel tool is characterized by a shoulder diameter of 18 mm, a pin diameter of 6 mm and a pin length of 5.7 mm. The two parameters affect the strength due to variations in material flow behavior, loss of cold work in the AA5083 heat affected zone, dissolution and AA6351 over aging of precipitates and formation of macroscopic defects in the weld zone. The best friction stir welded joint is obtained for a tool rotational speed of 950 rpm and a straight square pin profile. Mastanaiah et al. [45] study the effect of process parameters on dissimilar friction stir welds in AA2219-T6/AA5083 aluminum alloys, placing 5083 in advancing side. Particularly, they investigate five levels of rotational speed (i.e., 400, 800, 1200, 1600, 2000 rpm), welding speed (30, 210, 390, 570, 750 mm/min), and tool offset (-2, -1, 0, +1, +2 mm), using a H13 grade tool steel tool with a 15 mm diameter shoulder, a frustum shaped threaded pin of 6 mm top diameter and 4 mm bottom diameter and a tilt angle of 2 degrees. Welds free of defects can be achieved under a wide range of conditions. However, it is important to note that when performing welds at the lowest rotation speed, highest traverse speed, and with a tool offset towards the AA2219 alloy side, defective welds may occur. The degree of intermixing is determined by the tool rotation speed and traverse speed. It is possible to observe that at higher tool rotation speeds and lower tool traverse speeds, there is a greater degree of intimate mixing between dissimilar alloys. Laska et al. [55] produce dissimilar butt joints from AA5083, in advancing side, and AA6060, in retreating side, alloys by changing the rotational speed from 800 to 1200 rpm, with constant welding speed of 100 m/min and tilt angle of 2 degrees. The tool shoulder has a flat surface with a diameter of 18 mm. The pin length measures 2.5 mm. The pin itself has a hexagonal shape with a distance across the flats of 6 mm. The pin is made of 73MoV52 steel, while the shoulder is composed of X210Cr12 steel. The findings demonstrate that an increase in tool speed leads to an increase in hardness within the weld nugget zone. This is attributed to the higher heat input and more efficient recrystallization process. The weld with the highest tool rotational speed exhibits the highest hardness in the nugget zone. Conversely, raising the heat input decreases the hardness of the heat-affected zone, where recrystallization does not occur. In the heat-affected zone on the AA6060 side, the lowest density of dislocations with the highest mobility is observed, contributing to a reduction in strength within this zone. Das and Toppo [60] investigate three different tool rotational speeds (i.e., 900, 1100 and 1300 rpm) for making AA6101-T6 (in advancing side)/AA6351-T6 (in retreating side) joints, using a high carbon and high chromium steel taper cylindrical thread pin with a tilt angle of 2 degrees and a welding speed of 16 mm/min. The tool has a shoulder diameter of 25 mm, big diameter of the pin 8 mm, small pin diameter of 6 mm and pin length of 11.7 mm. In Charpy impact tests, it is observed that the minimum energy occurs at 900 rpm. This phenomenon can be attributed to the low friction pressure and insufficient friction time, which result in inadequate generation of frictional heat and insufficient time for the formation of a strong bond between the two dissimilar metals. However, as the rotational speed rises to 1100 rpm, the impact energy of the joint also increases. Subsequently, as the rotational speed further escalates to 1300 rpm, the impact energy decreases. This decline in impact energy could be attributed to grain refinement taking place in the weld zone due to the high heat generated. Das et al. [61] analysis other mechanical properties of these two alloys changing the rotational speed from 900 rpm to 1500 rpm and the axial force (i.e., 4, 5, 6, 8 kN), with constant welding speed of 60 mm/min and using an EN32 steel tool with a cylindrical threaded pin profile having tool tilt angle of 2 degrees. The flat faced shoulder diameter, pin diameter and tool pin length are 18 mm, 6 mm, and 5.85 mm respectively. They find that the rotational speed of 1300 rpm produces better mechanical and metallurgical properties joints. At lower rotational speeds, the tensile strength tends to be poor primarily because the tool stirring action is inadequate. This insufficient stirring leads to the generation of minimal frictional heat at rotational speeds of 900 rpm and 1100 rpm. Consequently, the material flow is compromised, resulting in lower tensile strength. However, an increase in rotational speed (1300 rpm) leads to an improvement in ultimate tensile strength. This occurs because the heat input at this speed is sufficient, promoting better weld quality. The weld region exhibits equiaxed fine grains, further enhancing the tensile strength. Nevertheless, when the rotational speed exceeds a certain threshold (1500 rpm), excessive heat input becomes a factor. This excess heat input causes reprecipitation and reduces the dislocation density of strengthening precipitates, such as Mg2Si. As a result, the tensile strength is lowered. In bending tests, when the rotational speed is set to 900 rpm, the joints demonstrate lower ductility. Additionally, micro cracks can be observed on the outer surface of the weld joint. These issues arise due to improper mixing of the metals and insufficient downward force applied during the process. At 1100 rpm, there is an increase in heat generation, resulting in improved flow of the softened mixed material comprising the two alloys. Consequently, the ductility of the joints improves. At a high rotational speed of 1500 rpm, the material flow becomes excessive due to the intense heat generated between the tool shoulder and the workpiece interface. This excessive flow causes the intermetallic compound Mg2Si to break, leading to a decrease in bending strength. However, when the rotational speed is set to 1300 rpm, the joint exhibits good ductility. This is attributed to the uniform interdiffusion of the intermetallic compound Mg2Si in the weld nugget region. As a result, the friction stir welded (FSW) joints demonstrate enhanced bending strength. Micro hardness tests reveal variations in hardness at the nugget region. These variations occur due to differences in heat input during the FSW process, which also impact the microstructures of the base alloy. At 900 rpm, the hardness is generally lower compared to the base alloys and other joints. This can be attributed to a softening effect that occurs at the weld joints. The hardness is dependent on the distribution of the intermetallic compound Mg2Si and the grain microstructure within the nugget region. At a rotational speed of 1300 rpm, the rate of heat input increases. This leads to the formation of fine equiaxed grains and a well-spaced microstructure within the weld nugget region. As a result, the weld nugget region exhibits an optimum micro hardness value. This indicates that the hardness at the nugget region is influenced by the grain size. However, as the rotational speed further increases to 1500 rpm, there is a decrease in hardness. This is primarily due to the high heat input, which leads to softening in the nugget region. Consequently, there is a reduction in grain size and the dissolution of strengthening precipitates, such as Mg2Si, further contributing to the drop in hardness. Aval [62] conducts a study on the impact of rotational speed on residual stress in dissimilar welded aluminum plates of 6082-T6, in advancing side, and 7075-T6, in retreating side, alloys, using a H13 steel tool with a shoulder of 23 mm in diameter, a triangular frustum pin, and a tilt angle of 2 degrees. During the experiments, various tool rotational speeds are tested, including 800, 1000, 1200, and 1400 rpm. Additionally, welding speeds of 90, 120, and 150 mm/min are used. However, only the welds created using rotational speeds of 1000 and 1200 rpm, with welding speeds of 90 and 120 mm/min, yield satisfactory results. His findings reveal that as the rotational speed increases (from 1000 to 1200 rpm) and the traverse speed decreases (from 120 to 90 mm/min), both factors contribute to increased heat generation, higher peak temperatures, and reduced maximum tensile residual stress. Interestingly, these results contradict the findings of Giorgi et al. [63]. To explain these contradictory results, Richards et al. [64] propose that the stress profile observed in friction stir welding (FSW) is a result of the mismatch in plastic strains induced by steep temperature gradients during the welding process. As heat is generated by the FSW operation and distributed within the component while the tool advances, it leads to these plastic strains. Therefore, it can be inferred that peak temperatures alone do not solely determine the maximum tensile residual stress. Instead, the temperature gradient, particularly the material cooling rate and its uniformity, have a more significant influence on the maximum tensile residual stress. These conclusions are supported by the findings of Campanelli et al. [65], who conducted an analysis on the effects of preheating the weld zone using a high-powered laser. Haribalaji et al. [66] investigate the FSW of AA2014, in advancing side, and AA7075, in retreating side, aluminum alloys. The process parameters are 1000, 1200, and 1400 rpm of rotational speed, 30, 45, and 60 mm/min of welding speed, 3, 6, and 9 kN of axial force, three different tool pin profile (i.e., straight cylinder, tapered, and threaded pin) and tilt angle of 0, 1, and 2 degrees. The tool, made in high carbon steel H13, have a shoulder diameter of 20 mm and a pin diameter of 6 mm. They observe that rotational speed and axial force are significant factors in tensile strength and microhardness. Setting correctly them can prevent the formation of the defect-free welds. The best welding parameters for achieving maximum tensile strength are a rotation speed of 1000 rpm, a welding speed of 45 mm/min, an axial force of 6 kN, and a tilt angle of 2 degrees. On the other hand, for achieving maximum hardness, the optimal parameters are a rotation speed of 1000 rpm, a welding speed of 60 mm/min, an axial force of 6 kN, and a tilt angle of 2 degrees. These optimal parameters are obtained by utilizing a threaded tool pin profile.
As the sheet thickness increases, the tool rotational speed typically needs to be adjusted to accommodate the additional material volume and ensure adequate heat generation. Thicker sheets require more heat input, so increasing the rotational speed helps in generating more frictional heat at the interface between the tool and the workpiece. However, the rotational speed cannot be increased infinitely, as excessive speeds can lead to overheating or material defects. Therefore, a balance must be struck to achieve the desired heat input without compromising weld quality.
2.4. Welding speed
The welding speed in friction stir welding refers to the rate at which the tool moves along the joint line during the welding process. It is also known as the traverse speed, feed rate or travel speed. It can vary depending on several factors, including the material being welded, the thickness of the workpieces, and the desired quality and strength of the joint. Generally, it is relatively slower compared to traditional fusion welding processes such as arc welding or laser welding.
The typical welding speeds in friction stir welding can range from a few centimeters per minute (cm/min) to several tens of centimeters per minute, depending on the specific application. However, it is important to note that the focus in FSW is not on achieving high welding speeds but rather on controlling the process parameters to ensure proper heat generation, mixing, and consolidation of the material.
The selection of the optimal welding speed in FSW involves a trade-off between process efficiency and joint quality. A slower welding speed allows for better heat input control, enhanced material mixing, and improved joint integrity, which is particularly important for high-strength materials or critical applications. It can be also associated with defects such as tunneling. On the other hand, increasing the welding speed can improve productivity but may require careful adjustments to maintain the desired joint properties. Palanivel et al. [67] study microstructure and mechanical characterization of dissimilar friction stir welded AA5083-H111 and AA6351-T6 aluminum alloys by investigating three different welding speeds (i.e., 36, 63, 90 mm/min) at a constant rotational speed of 950 rpm, using high carbon and high chromium steel tool having a straight square pin profile. The tool has a shoulder diameter of 18 mm, pin diameter of 6 mm and pin length of 5.7 mm. When performing FSW at higher welding speeds, several issues can arise. One problem is that the exposure time in the weld area becomes shorter, leading to inadequate heat and insufficient plastic flow of the metal. Consequently, defects such as voids may appear in the joints. Additionally, the reduced plasticity and slower rates of diffusion in the material can result in a weak interface. Furthermore, higher welding speeds are associated with low heat inputs, causing the welded joint to cool down more rapidly [68]. This faster cooling rate prevents the formation of a well-mixed flow region. The welding speed plays a crucial role in determining the exposure time of frictional heat per unit length of the weld. This, in turn, affects the grain growth and precipitates within the welded material. Achieving an optimal exposure time and the appropriate translation of stirred material leads to effective consolidation of the material, resulting in finer grains. For instance, when a joint is subjected to such conditions at a welding speed of 63 mm/min, it exhibits the highest resistance. This suggests that the combination of the specific welding speed and corresponding exposure time promotes favorable consolidation and grain refinement, ultimately enhancing the overall strength and quality of the joint. The factors that determine the tensile strength of dissimilar aluminum alloy joints are the presence of macroscopic defects in weld zone and the degree of plastic flow and amount of mixing of both the materials.
Welding speed is always investigated in combination with tool rotational speed to obtain a defect free joint with a good metallurgical bond and mechanical properties. Devaiah et al [69] use FSW for joining AA5083-H321, in advancing side, and AA6061-T6, in retreating side, aluminum alloys using a H13 steel tool having a cylindrical taper threaded pin profile, changing the welding speed (i.e., 40, 63, 80 and 100 mm/min) and keeping constant rotational speed (i.e., 1120 rpm) and tilt angle (i.e., 2.5 degrees). The tool has a shoulder diameter of 18 mm, pin diameter of 6 mm and pin length of 4.7 mm. The optimal joint is made with a tool rotation speed of 1120 rpm and a welding speed of 80 mm/min. This combination induces an adequate heat generation and proper mixing of the material in the weld zone. Furthermore, it is possible to observe that the weld zone exhibits the formation of finer grains, primarily attributed to the occurrence of dynamic recrystallization. This phenomenon contributes to the refinement of the grain structure within the weld zone. When examining the fracture surface of both tensile and impact specimens in AA5083 to AA6061 weldments, a ductile fibrous fracture is evident at the weld zone. This fracture morphology indicates that the joint possesses good ductility and toughness characteristics. The welding speed significantly influences the formation of the plastic flow region during friction stir welding. Specifically, the choice of welding speed determines the extent and quality of the mixing that occurs within the material. It has been observed that at the lowest or highest welding speeds, the mixed flow region is absent or poorly formed in the joints. Jia et al. [70] optimize the welding parameters of the friction stir welding of dissimilar 6061-T6 (in advancing side)/5083-H111 (in retreating side) aluminum alloys. Particularly, welding parameters include rotational speeds of 2000, 2400, and 2800 rpm, traverse speeds of 1200, 1500, and 1800 mm/min, and plunge depths of 0.20, 0.25, and 0.30 mm. Experiments are conducted using a welding tool equipped with a right-hand threaded pin surface and three involute grooves on the shoulder. The tool is welded at a tilt angle of 2.5 degrees and has a pin length of 2.65 mm. The pin bottom diameter measures 3 mm, while the shoulder diameter is 14.0 mm. The yield strength of the welded joint first increases and then decreases with increasing the traverse speed. A higher traverse speed reduces the amount of frictional heat generated and makes it difficult to achieve sufficient material flow and mixing. In FSW, the tool rotation and traverse speed create frictional heat, which softens the material and allows for plasticized material flow and mixing. However, when the traverse speed is set too high, there is insufficient time for the heat to build up, resulting in inadequate softening of the material. Insufficient frictional heat leads to challenges in achieving proper material flow and mixing. The softened material is not able to flow and mix effectively, which can negatively impact the weld quality. Inadequate mixing can result in defects such as incomplete bonding, lack of homogeneity, or improper consolidation of the weld. The traverse speed has a significant impact on the material mixing of dissimilar aluminum alloys. A lower traverse speed is more conducive to the mixing of dissimilar aluminum alloys. Anandan et al. [71] investigate a friction stir welded joint between dissimilar 7050-T7651 and 2014A-T6 aluminium alloys changing the welding speed from 25 to 85 mm/min (i.e., 25, 45, 65, 85 mm/min), using a cylindrical tapered tool pin made of H13 tool steel, with a tilt angle of 2 degrees and a rotational speed of 1000 rpm. They observe better mechanical and metallurgical properties than other welding speeds at 65 mm/min has because of proper material mixing and finer grains obtained in the weldment. At low welding speeds it is possible to observe the formation of keyholes and high concavity, while at high welding speeds the stir zone decreases of about 37%. Keyholes and concavity were mainly formed as a result of increased heat generated during the FSW process. Conversely, the decrease in heat generation led to a reduction in the size of the stir zone. Dimov et al. [72] focus on the mechanical behavior of a AA6061-T651/AA7075-T651 dissimilar friction stir weld by controlling pin length, rotational speed, advancing speed and vertical force to reach 5.75 mm, 400 rpm, 120 mm/min, and 10 kN respectively. The meso-scale strain distribution is primarily influenced by the local alloy composition, which is identified as the critical parameter. Additionally, at a smaller scale, the presence of intermetallic Mg-Si and Fe-rich particles further contributes to strain localization within each individual alloy. Khan et al. [73] select aluminum alloys AA2219-O, in advancing side, and AA7475-T761, in retreating side, as base materials for welding using a high carbon high chromium steel cylindrical tool having threaded pin with 14 mm shoulder diameter and 4 mm pin diameter, with a tilt angle of 2.5 degrees, two rotational speeds (i.e., 710 and 1120 rpm) and two welding speeds (i.e., 160 and 250 mm/min). They discover that an increased strain rate results in a greater flow stress necessary for plastic deformation. Nonetheless, when considering the relationship between strain rate and the speed at which the tool traverses, it becomes evident that the dependence is more pronounced compared to the rotational speed of the tool. A higher traverse speed leads to a reduction in heat input per unit weld length and an increase in strain rate. Both factors contribute to an increase in flow stress. Additionally, as the traversing speed increases, the net traverse force exerted on the tool experiences a significant rise. Ahmed et al. [74] join aluminum alloys AA7075-T6, in retreating side, and AA5083-H111, in advancing side, using a H13 steel with 18 mm diameter concave shoulder and 4.8 mm long unthreaded taper cylindrical pin with a tilt angle of 3 degrees, at a constant rotation rate of 300 rpm and different traverse speeds of 50, 100, 150, and 200 mm/min. Despite using the same parameters for two alloys, they display different responses in terms of the recrystallized fine grains after FSW. In the case of AA7075, significant grain refinement occurs in the nugget zone with an average grain size of 6 μm at a welding speed of 50 mm/min, which is further reduced to 2 μm by increasing the welding speed to 200 mm/min. On the other hand, AA5083 joints in the NG zone exhibit a relatively coarser recrystallized grain structure with an average grain size of 9 μm at 50 mm/min, which decreases to 3 μm at 200 mm/min. These findings indicate that the initial characteristics of the materials have a substantial impact on the final grain structure after FSW. The crystallographic texture in the nugget zone displays a simple shear texture, with no significant influence observed when varying the welding speed.
Thicker sheets generally require slower welding speeds to allow for sufficient heat transfer and plasticization of the material. Slowing down the welding speed ensures that the heat generated by the friction stir process has enough time to prop-agate through the thickness of the sheets and achieve the desired weld quality. A slower welding speed also helps in maintaining better control over the material flow and mixing during the stirring process. However, excessively slow speeds may lead to excessive heat input and potential defects. Therefore, the welding speed should be optimized based on the specific material, sheet thickness, and de-sired weld characteristics.
2.5. Position of sheets
The direction of the tool movement during FSW influences the resulting joint in terms of microstructure and mechanical properties, i.e., the placement of the alloy affects material flow as it strongly influences material stirring and mixing [12,75,76]. The FSW process exhibits inherent asymmetry in material flow behavior between the advancing side (AS) and retreating side (RS) of the stir zone. This means that the position of the base material, whether in the AS or RS, has a substantial impact on various aspects, including the temperature distribution, material composition within the stir zone, and the plastic flow behavior of the metal. These factors, in turn, significantly influence the mechanical properties of dissimilar joints produced by FSW [77,78]. Particularly, when it comes to advancing and retreating sides in a dissimilar aluminum FSW joint, the following effects can be observed:
- Heat Input: the advancing side experiences higher heat input compared to the retreating side. As the tool moves forward, it generates more frictional heat, resulting in increased plastic deformation and temperature in the advancing side. This can lead to different thermal cycles and thermal gradients on the two sides of the joint.
- Grain Structure: the different heat inputs on the advancing and retreating sides can result in variations in the grain structure of the weld. The advancing side generally experiences more severe deformation and recrystallization, leading to finer grain sizes compared to the retreating side. The grain structure affects the mechanical properties of the joint, such as strength and toughness.
- Composition Variation: dissimilar aluminum alloys may have different compositions and mechanical properties. The advancing side, experiencing higher heat and deformation, can lead to localized diffusion of alloying elements between the base materials. This diffusion can influence the composition and resulting properties of the joint.
- Residual Stresses: the differences in heat input and resulting microstructure can lead to variations in residual stresses along the joint. Residual stresses are important because they can affect the structural integrity and distortion of the welded components.
The material flow in friction stir welding (FSW) is a complex process, and therefore, the placement of materials becomes a significant parameter in the welding procedure. This aspect is equally important as factors like rotation speed and welding speed, as it can greatly influence the outcome of the welding process [79]. To optimize the FSW process for dissimilar aluminum alloys, it is important to carefully consider the effects of advancing and retreating sides. Process parameters, such as tool rotational speed, traverse speed, and tool design, can be adjusted to achieve the desired joint properties. Additionally, post-weld heat treatment or other techniques may be employed to further refine the microstructure and properties of the weld.
Some researchers consider that the base material with lower solution temperatures which is easily softened at higher temperatures should be positioned on the RS where a lower temperature is measured [80]. Simar et al. [81] study similar and dissimilar friction stir welds made of aluminum alloys 2017-T6 and 6005A-T6. They point out that better performance joints were produced when the base metal, characterized by lower mechanical properties, is placed on the RS. However, Kim et al. [82] study the joining of dissimilar A5052 and A5J32 Al alloy, at a rotational speed from 1000 rpm to 1500 rpm and welding speed from 100 mm/min to 400 mm/min, using a tool with the following characteristics: tool shoulder diameter of 8 mm, threaded cylindrical pin diameter of 3 mm and length of 1.45 mm, tilt angle of 3 degrees. They show that by placing the high strength Al alloy on the AS generates excessive agglomerations and defects due to limited material flow. Consequently, the high strength Al should be placed at the RS to minimize this effect.
Donatus et al. [83] study AA5083-O and AA6082-T6 friction stir welded joints. using traverse speeds of 400 mm/min or 300 mm/min at a constant tool rotation speed of 400 rpm. The process is conducted using a 2-part MX-Triflute tool with a probe diameter to length ratio of 1:0.8 (i.e., 7.0 mm tip diameter with a cone angle of 5°), a scroll shoulder diameter of 25 mm and a tilt angle of 0 degrees. Particularly, the AA5083-O is at the AS of the weld whilst the AA6082-T6 is at the RS. They observe that in friction stir welding, material primarily flows from the advancing side to the retreating side without significant mixing. However, material flow from the RS to the AS occurs mainly within the tool shoulder region, with the highest level of material displacement observed at the transition area between the tool shoulder and the tool pin domains. Furthermore, they notice that material extrusion predominantly occurs in the thermomechanical affected zone of the RS, which is influenced by the rotational movement of both the tool shoulder and the tool pin. In terms of grain structure, the finest grains are found in the regions closest to the tool edge within the RS. Zhao et al. [84] study the connection between Al 6013-T4 and Al 7003 alloys, highlighting the impact of exchanging the advancing side and retreating side materials on the resulting joint cross sections. It is observed that the material on the AS undergoes more significant deformation during the welding process. Placing Al 6013-T4 on the AS promotes a more effective plastic flow in the weld. Regardless of whether Al 6013-T4 is positioned on the AS or RS, it is identified as the weaker region in both tensile specimens and hardness samples. The fracture location aligns with the position of minimum hardness, indicating a correlation between fracture and lower material strength. Park et al. [85] shows that the material mixing patterns in the FSW joints are quite different depending on the locations of the base metals. In an AA5052-H32/AA6061-T6 joint, the placement of AA5052 on advancing side exhibits better and improved mixing of base metals in the stir zone. Whereas the placement of base metals does not affect the location of fracture as the welds fail from advancing side of weak heat affected zone. Niu et al. [86] aim to identify the effect of base metal locations on the corrosion behavior of friction stir welded dissimilar 2024-T351 to 7075-T651 aluminum alloys joints. The process is performed using a tool having a threaded pin with 5.9 mm in diameter and 6.0 mm in length, a concave shoulder of 15 mm in diameter, and a tilt angle of 2.5 degrees, at rotation rate of 600 rpm and welding speed of 200 mm/min. They find that the stir zones show similar corrosion resistance to the base metal located on the retreating side, with intergranular corrosion being the dominant form. In particular, the finely recrystallized grains within the stir zones exhibit a more pronounced occurrence of intergranular corrosion compared to the base metals. However, the presence of grain boundary precipitates and precipitate-free zones, which are distributed intermittently within the stir zone of the 2024 alloy, effectively mitigate the damage caused by intergranular corrosion in the stir zone.
2.6. Axial force
Axial force, also known as the vertical force or downward force, is an important parameter in friction stir welding [87,88,89]. During the process, the axial force is applied vertically onto the workpiece through the rotating tool. The force creates a downward pressure that holds the workpieces together and maintains contact between the tool and the material being welded [90]. The magnitude of the axial force can vary depending on factors such as material type, thickness, and tool geometry. The axial force has several effects on the FSW process:
- Material Penetration: it ensures that the rotating tool penetrates the workpiece to the desired depth. It helps in achieving proper material mixing and bonding between the adjacent surfaces.
- Heat Generation: the downward pressure exerted by the axial force enhances the contact between the tool and the workpiece. This contact generates frictional heat due to the relative motion between the tool shoulder and the material. The heat softens the material, allowing it to deform and join.
- Plastic Deformation: as the rotating tool moves along the joint line, the force helps in deforming and stirring the material, facilitating metallurgical bonding. The plastic deformation allows the material to flow around the tool and form a solid-state weld.
- Quality of the Weld: proper application of force ensures that there is sufficient contact between the tool and the workpiece, promoting effective heat transfer and material flow. Insufficient axial force may result in inadequate mixing, incomplete bonding, or defects in the weld, while excessive force can lead to excessive material displacement or even tool breakage.
- Weld Strength and Integrity: by applying a suitable force, the material is effectively consolidated, leading to a sound weld joint with improved mechanical properties.
It is important to optimize the axial force in FSW to achieve high-quality welds. The force should be carefully controlled to ensure proper penetration, material flow, and bonding without compromising the tool integrity or causing detrimental effects on the workpiece. The optimal axial force depends on various factors, including the material being welded, its thickness, and the specific FSW parameters employed.
Using an unsuitable or improper axial force can lead to various defects in the weld. It can result in a poor contact between the rotating tool and the workpiece, that can lead to incomplete joint formation, where the material is not adequately mixed or bonded. This defect is characterized by visible gaps or voids along the weld line. When the force is not sufficient to induce significant plastic deformation and stirring, the material may not achieve the desired homogeneity and metallurgical bonding. This can lead to poor mechanical properties and reduced weld strength. When the axial force is too low, the rotating tool may not penetrate the workpiece adequately. This can result in a tunnel defect, where the tool fails to fully engage with the material. As a result, a void or cavity is formed within the weld, compromising its integrity and mechanical properties. Excessive axial force can cause excessive material displacement and flow around the rotating tool. This can lead to a flash defect, where material is pushed out of the joint line and forms an undesirable protrusion or flash on the surface of the weld. Flash defects can weaken the weld and require additional post-weld machining or removal. Excessive axial force can subject the rotating tool also to high mechanical stresses, increasing the risk of tool breakage. The force should be controlled within the recommended limits to prevent tool failure during the welding process. Tool breakage not only disrupts the welding operation but also introduces potential contaminants into the weld. Finally, inadequate axial force can lead to non-uniform deformation and inadequate thermal cycling during FSW. This can result in residual stresses and distortion in the weld and the surrounding material, affecting the structural integrity and dimensional accuracy of the welded component [91,92,93].
Ramamoorthi et al. [90] aim to assess the impact of axial force (i.e., 5kN, 6kN, 7kN and 8kN) during FSW on the dissimilar joint mechanical properties of aluminum alloys (AA5086 and AA6063), at rotational speed of 2000 rpm and feed rate of 60 mm/min. The degree to which the tool pin dips into the process region and the resulting material flow are influenced by the shoulder pressure and, consequently, by the axial force. Moreover, the shoulder pressure is responsible for ensuring sufficient and effective stirring of the material, leading to the refinement of aluminum alloy grains [94,95,96]. The axial force serves also as a significant constraint for the determination of weld efficiency and joint strength, i.e., 5kN and 6kN axial forces lead to inappropriate mixing and inferior quality of bond. In conclusion, the joints made by the axial force of 7kN have the best performance.
The axial load, or downward force, applied to the FSW tool affects the contact pressure between the tool and the workpiece. Thicker sheets require higher axial loads to promote good material contact and facilitate effective heat transfer. The increased downward force helps in overcoming the resistance to material deformation caused by the thickness of the sheets. It also aids in maintaining a consistent material flow and proper mixing during the welding process. However, it is crucial to avoid excessive downward forces that can cause excessive material displacement or tool wear. The axial load should be adjusted within the optimal range to achieve a balance between material deformation and process stability.
3. Design tool
Predicting the process parameters and mechanical behavior in FSW involves understanding the complex thermal and mechanical interactions during the welding process [97]. The combinations of process parameters are typically random and uncertain [98,99]. Meanwhile, the relationship between the welding parameters and the mechanical properties of the joints is highly non-linear [100,101]. Furthermore, despite the diffusion of five-axis numerically controlled machines, the equipment is limited for studying complicated geometries [102]. Consequently, a deep investigation is still challenging [103].
Several tools and techniques are available to aid in the prediction of process parameters and mechanical behavior in FSW. These can be split into distinct categories [104]. The first category involves statistical approaches, such as Response Surface Methodology (RSM), Taguchi, and ANOVA, that are used to determine the best parameters. The second group include the heuristic techniques such as genetic algorithms (GA), simulated annealing (SA), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS), that are used to optimize the responsiveness and value of parameters. The last group include Finite Element Analysis (FEA), and Computational Fluid Dynamics (CFD). Recently, these tools and techniques can be used individually or in combination [105,106].
In the next sections, the attention is focused on the main tools used in friction stir welding of dissimilar aluminum alloys.
3.1. Statistical approaches
Statistical methods such as design of experiments (DoE) can be employed to systematically vary process parameters, such as tool rotational speed, welding speed, and applied force, to understand their effects on weld quality and mechanical properties. By using statistical techniques like response surface methodology (RSM), it is possible to identify optimal parameter settings that lead to desirable weld characteristics, such as defect-free joints, high strength, and improved fatigue resistance. RSM is a comprehensive mathematical and statistical framework employed to tackle experimental problems by analyzing the relationships between variables and responses. It specifically focuses on scenarios where a limited number of variables impact the outcomes. The primary objective of RSM is to determine the nature of these relationships, enabling the optimization of responses through variable manipulation. Elatharasan and Kumar [107] predict, using RSM, the ultimate tensile strength, yield strength and displacement of friction stir welded (AA6061-T6 and AA7075-T6) aluminum alloy by by varying rotational speed (i.e., 800, 1000 and 1200 rpm), welding speed (i.e., 30, 60 and 90 mm/min) and axial force (6, 8 and 10 kN). Nait Salah et al. [108] aim to figure out the best FSW settings for stir casted activated carbon reinforced AA6061, AA7075 composite, AA6061 alloy and AA7075 alloy, including as input rotational speed (i.e., from 1000 rpm to 1400 rpm), axial load (i.e., from 8 kN to 12 kN), tool pin profile (i.e., Taper, Cylindrical, Square, T. Cylindrical and Triangle) and material utilized. Suhin et al. [109] use RSM to the mathematical model the FSW input parameters to fabricate AA3003/AA6061 joint. According to the developed model, the rotational speed of the tool and the welding speed are the primary influential parameters. Increasing rotational speed and decreasing welding speed result in higher heat input to the welded joints. The confidence interval analysis indicates that both the ultimate tensile strength and microhardness increase as rotational speed is increased. The optimized values for ultimate tensile strength, strain percentage (% strain), and microhardness are determined to be 95.8 MPa, 12.18%, and 74.47 HV, respectively. The corresponding optimized values for rotational speed, welding speed, and tilt angle are 1172 rpm, 57.44 mm/min, and 1.252°, respectively.
By collecting and analyzing real-time or post-weld data, statistical techniques like control charts can help detect and prevent potential defects or deviations from desired weld quality. By analyzing large datasets and employing techniques such as fault tree analysis or Pareto analysis, it is possible to pinpoint the key factors causing defects like voids, cracks, or lack of fusion. This information can guide process improvements and the development of mitigation strategies.
Statistical analysis can be employed to investigate the relationship between process parameters, microstructure evolution, and resulting mechanical properties. For example, techniques like regression analysis can be used to model the influence of FSW parameters on grain size, hardness, or tensile strength. This understanding helps researchers optimize the welding process for specific applications and predict material behavior under different loading conditions.
Statistical approaches can aid also in assessing the reliability and fatigue life of FSW joints. By applying probabilistic models and using techniques like Weibull analysis, researchers can estimate the probability of failure or predict the fatigue life of welds under different loading conditions. This information is vital for ensuring the long-term performance and durability of FSW structures.
3.2. Heuristic techniques
3.2.1. Artificial Neural Networks
- Predictive Modeling: by training an ANN with input-output pairs of FSW process parameters and corresponding weld quality, the network can learn the complex relationships between these variables. Once trained, the ANN can predict the outcomes of FSW for new input parameters, allowing to estimate weld quality, defects, or other relevant properties.
- Optimization: by constructing an ANN-based surrogate model, which approximates the relationship between process variables and a desired objective (i.e., joint strength, fatigue life), optimization algorithms can efficiently explore the parameter space and identify the combination of inputs that maximizes the objective. This can lead to improved weld quality and process efficiency.
- Fault Detection: by training an ANN with sensor data from the welding process, such as temperature, torque, or force measurements, the network can learn normal patterns and identify deviations that indicate potential faults or defects. This allows for real-time monitoring of the welding process and early detection of issues, enabling timely corrective actions.
- Process Control: by employing ANN as part of control algorithms, the network can analyze sensor data in real-time, make predictions, and adjust process parameters accordingly. This adaptive control approach can enhance the stability, accuracy, and repeatability of the FSW process, leading to improved weld quality.
- Material Characterization: by training an ANN with input data such as material composition, microstructural features, and mechanical properties, the network can learn the relationships between these parameters and welding outcomes. This can aid in understanding how different materials behave during FSW and enable the selection of suitable welding parameters for specific materials.
Arya and Jaiswal [111], and Okuyucu et al. [112] demonstrate the possibility of the use of neural networks for the calculation of the mechanical properties of welded Al plates using FSW method. Results of their studies show that, the networks can be used as a suitable alternative.
Gupta et al. [113] investigate the application of ANN for modelling and multi-objective optimization of friction stir welding parameters of dissimilar AA5083-O/AA6063-T6 aluminum alloys, by changing the following parameters: rotational speed (i.e., 700, 900, 1100 rpm), welding speed (i.e., 40, 60, 80 mm/min), shoulder diameter (15, 18, 21 mm), pin diameter (i.e., 4.5, 5, 5.5 mm). For the validation of the models, predicted results of each response are compared with the experimental results. Moreover, the adequacy of the ANN models is checked using statistical analysis. Particularly, ANN is an information processing framework comprising interconnected neurons that collaborate to perform tasks. Neurons, the fundamental units of an ANN, are linked through synapses, each associated with a weight factor. The ANN architecture consists of three layers: the input layer, containing input parameters; the hidden layer, where information is transmitted from the input layer; and the output layer, which yields the architecture's output. The number of neurons in the hidden layer is determined based on the minimum mean square error criterion. In the study, five neurons are employed in the hidden layer for each response, resulting in 4-5-1 network architectures for developing ANN-based models.
Shojaeefard et al. [114] focus on the microstructural and mechanical properties of AA7075-O to AA5083-O aluminum alloys, developing an Artificial Neural Network (ANN) to correlate the process parameters and the mechanical properties. They use a tool characterized by tapered pin profile having pin major and minor diameter respectively of 10 mm and 5 mm, pin length of 5.85 mm, shoulder diameter of 20 mm and shoulder angle of 12 degrees, by changing the rotational speed (i.e., 500, 565, 700, 900, 1400, 1600 rpm) and the welding speed (i.e., 30, 63, 96 mm). The ANN model results effective in predicting the ultimate tensile strength and the hardness as a function of weld and rotational speeds.
Kraiklang et al. [115] propose a methodology that combines artificial multiple intelligence systems and machine learning to predict the ultimate tensile strength, maximum hardness, and heat input of friction stir welding in AA5083 and AA6061 alloys. They have the possibility to change many factors, both continuous and categorical: tilt angle (i.e., 0, 3 degrees), rotational speed (150, 1500 rpm), welding speed (i.e., 15, 135 mm/min), shoulder diameter (i.e., 18, 25 mm), pin geometry (i.e., straight, hexagonal and threaded cylinder), reinforcement particle type (i.e., silicon carbide, aluminum oxide), tool pin moving (i.e., straight, zigzag, circles). The machine learning model incorporates two methods, Gaussian process regression and support vector machine, into a unified model. Artificial multiple intelligence systems are employed as the decision fusion strategy to combine these two methods. This integrated model is then utilized to forecast the three objectives using seven controlled/input parameters: tool tilt angle, rotating speed, travel speed, shoulder diameter, pin geometry, type of reinforcing particles, and tool pin movement mechanism.
Verma et al. [116] reports on the employment of the machine learning techniques (namely support vector machine), artificial neural networks, and random forest, for predicting the tensile behavior of friction stir welded dissimilar aluminum alloys joints (6083-T651 and 8011-H14).
3.2.2. Genetic Algorithms
Genetic algorithms are a type of optimization algorithm inspired by the principles of natural selection and genetics. Although, they share some similarities with the Artificial Neural Networks, they are fundamentally different in terms of their approach and functionality. For example, GA focus on searching and exploring the solution space to find the optimal or near-optimal solution. They do not inherently learn or generalize from data but rely on the principles of selection and evolution to improve the quality of solutions. While ANN are designed to learn from data through a training process. They adapt their internal parameters (weights and biases) to minimize a given objective function (i.e., error) based on input-output pairs. ANNs could generalize from the training data and make predictions on unseen data.
In the context of FSW, Genetic algorithms can be employed to search through the parameter space and identify the best combination of parameters that yield desirable welding characteristics, such as reduced defects and improved mechanical properties.
They can be used to develop models that predict the quality of FSW joints based on input parameters and material properties. By training the genetic algorithm on a dataset containing experimental or simulated FSW results, it can learn the relationships between process variables and the resulting weld quality. This model can then be used to predict the quality of FSW joints for new parameter combinations, aiding in process optimization and defect prevention.
Genetic algorithms can be employed to optimize the tool design by searching for the most effective combination of tool parameters. This can involve optimizing the shape of the tool pin, shoulder, and other tool features to enhance material flow, heat generation, and defect reduction during FSW.
In some cases, FSW may involve welding complex geometries or multiple passes. Genetic algorithms can be used to determine the optimal path planning and trajectory for the FSW tool to ensure uniform heating and mixing of the material, minimize defects, and achieve desired joint properties.
Abd Elaziz et al. [117] develop a new metaheuristic algorithm, called Marine Predators Algorithm, integrated with a Random Vector Functional Link network to predict the tensile behavior of dissimilar FSW joints of AA5083 and AA2024 aluminum alloys where the model input parameters are rotational speed (i.e., 800, 1000, 1200, 1400, 1600 rpm), welding speed (i.e., 40, 50, 60, 70, 80 mm/min), tool axial force (15, 20, 25, 30, 35 kN), and tool pin profile (i.e., tapered hexagon, tapered square, straight cylinder, tapered cylinder with grooves, and paddle shape). Particularly, the model can predict tensile strength and tensile elongation with a coefficient of determination of 1 and 0.999, respectively. Tapered hexagon pin profile is recommended to achieve optimum tensile strength with moderate values of tool rotational speeds, welding speeds, and tool axial force, while straight cylinder pin profile should be avoided as it weakens the tensile strength of the welded joint for all welding conditions.
Gupta et al. [118] use genetic algorithm to optimize the FSW process between AA5083-O and AA6063-T6 aluminum alloys. They consider regression models – suitable to predict the responses at 99% confidence level - as objective functions. The optimal process parameters are tool rotational speed of 900 rpm, welding speed of 60 mm/min, shoulder diameter of 18 mm and pin diameter of 5 mm for maximum tensile strength and minimum grain size.
Yunus and Alsoufi [119] investigate two dissimilar aluminum alloys AA7075 and AA6061, placing them on the advancing side and retreating side alternately, by adopting a potential and novel approach using genetic programming. The process is performed with a special purpose tool (cylindrical taper profile with pin diameter of 6 mm, 10° taper, pin length of 1.8 mm, and shoulder diameter of 20 mm) at different sheet thicknesses (i.e., 3, 4, and 5 mm), tilt angles (i.e., 3, 4 degrees), rotational speeds (i.e., 600, 900, 1200 rpm), welding speeds (i.e., 70, 90, 115 mm/min). The proposed GP approach is independent of conventional mathematical principles and prior knowledge about the solution type. It employs an evolutionary process to automatically develop mathematical models that accurately fit historical experimental data, without assuming anything about the problem's shape, size, or complexity, regardless of the number of input parameters involved. In this study, the Discipulus GP software and C programming are utilized to formulate new models for elongation, tensile strength, and impact strength under various input conditions. By substituting input conditions, the joint properties can be quickly determined without the need for additional experimental runs. These new GP models serve as an alternative method for estimating joint properties in situations where experimental results or correlations are unavailable. It is important to note that the precision of the solutions achieved through GP depends on the quality of the evolutionary inputs, the amount of test data available, and the accuracy of that data.
3.3. Finite Element Analysis
FEA plays a crucial role in studying FSW by providing valuable insights into the process and aiding in the optimization of welding parameters. FEA is a numerical simulation technique used to analyze complex engineering problems by dividing the problem domain into finite elements and solving mathematical equations to predict the behavior of the system [120]. In the context of FSW, FEA helps in the following ways [121]:
- Thermal Analysis: by considering factors such as tool rotation, tool traverse speed, and material properties, FEA can simulate the heat generation and distribution predicting the temperature distribution, the thermal cycles, and the heat affected zone evolution during the welding process. This information is significant for understanding the thermal history and potential defects in the weld.
- Mechanical Analysis: by considering the interaction between the tool and workpiece, FEA can evaluate the mechanical aspects of FSW, including stress and deformation distribution predicting the material flow, the plastic deformation, and the residual stresses in the weld. This analysis helps to optimize tool geometry and process parameters to minimize residual stresses and distortion in the final weld [122].
- Process Optimization: FEA allows for parametric studies, where different welding parameters and tool designs can be simulated to assess their impact on the welding process. By analyzing the temperature, stress, and deformation fields, FEA can help identify optimal process parameters that lead to improved weld quality, reduced defects, and enhanced mechanical properties.
- Defect Prediction: FEA can aid in identifying potential defects in the FSW process. For example, by analyzing the temperature field, FEA can predict the likelihood of defects like lack of fusion, voids, or excessive material flow. This information can guide process improvements and minimize the occurrence of defects.
- Tool Design and Optimization: by simulating the contact and frictional behavior between the tool and workpiece, FEA can assess tool wear, heat generation, and stress distribution on the tool. This enables the development of tool designs that enhance performance, durability, and efficiency.
The methodology used in finite element analysis can be divided into implicit, explicit and CFD - that is analysed in the next section – analysis. The implicit analysis method is well-suited for static equilibrium cases, as it relies on force and momentum equilibrium methods. It provides automated and direct user control of the time step derivative, making it applicable to both linear and non-linear models with significant steps. However, due to its time-consuming nature in fast dynamic processes, these models are more suitable for scenarios where precise results are required rather than quick calculations. The explicit analysis method is founded on energy equilibrium, enabling it to efficiently tackle dynamic equilibrium problems. It is the preferred choice for scenarios where simulation time is of utmost importance while maintaining accurate results [123].
The choice of formulations or modelling scales establishes the framework that connects the components or equations of the simulation [123]. Das et al. [124] develop a finite element model using coupled Eulerian-Lagrangian (CEL) approach for interpreting the tool-material interaction and predicting both volumetric defect and surface texture with optimum condition of mass scaling. Several solid mechanics approaches, such as the Eulerian or Lagrangian approach, smoother particle hydrodynamics (SPH), coupled Eulerian and Lagrangian (CEL), and arbitrary Lagrangian and Eulerian (ALE) [125] techniques, can be employed to model the thermo-mechanical behavior of the Friction Stir Welding (FSW) process [126,127]. CEL approach is suitable to the modelling of dissimilar FSW process [128]. Particularly, the analysis of the thermo-mechanical behavior of the FSW process using the coupled Eulerian-Lagrangian method is highly influenced by the mass scaling factor. Customizing this factor to create a computationally efficient model holds great potential in accurately predicting defects in the FSW process. Mass scaling is a key aspect of explicit time integration schemes used in quasi-static simulations of the process, which involves highly nonlinear contact conditions between the FSW tool and the workpiece. Mass scaling enables the discretization of the solution domain into multiple small elements [129]. However, applying mass scaling automatically may not always be feasible, leading researchers to explore a manual approach of multiplying the density with a mass scaling factor, as discussed in the literature [130].
The main software commonly used for modelling the FSW process include ABAQUS, ANSYS [123,131], and DEFORM [132,133]. A wide literature exists but it is focused mainly on similar aluminium alloys due to joining of the dissimilar materials is usually more difficult in comparison with joining similar material or those materials that have minor differences [126].
Zahari et al. [134] show some techniques on modelling the FSW weld joints for prediction of dynamic behaviour in AA6061 and AA7075 joints by varying rotational speed (i.e., 900, 1000 and 1100 rpm), welding speed (i.e., 30, 40 and 50 mm/min) and tilt angle (i.e., 0, 1 and 2 degrees).
Sivasankara Raju et al. [135] investigate dissimilar aluminium alloy sheets AA6061 and AA5052, using Deform 3D, to examine the effect of heat generation on the welding process at various tool speeds (i.e., 1000, 1200, 1400 rpm) and feed rates (i.e., 20, 40 and 60 mm/min). They evaluate distribution and values of temperature in weld-zones, by observing that: i) aluminium alloy sheets have a temperature gradient that increases as they advance and decreases as they retreat; ii) nugget zone hardness levels are lower than the base alloy; iii) for AA6061, the temperature rises with increasing tool speed and falls with increasing feed rate; iv) at a constant welding speed, quantity of heat in the weld zones rises by increasing the tool rotational speed; the temperature firstly increases quickly and then fluctuates before progressively dropping. The model is validated using the thermocouples in some experimental tests.
Al-Badour et al. [136] develop a thermo-mechanical finite element model, based on CEL method, to simulate the friction stir welding of dissimilar Al6061-T6 and Al5083-O aluminum alloys using different tool pin profiles and changing the sheet position, at rotational speed of 900 rpm, welding speed of 150 mm/min and tilt angle of 2 degrees. The results from the finite element analysis indicate that the maximum temperatures at the weld joint remained below the melting point of the materials. By positioning the harder alloy (Al6061-T6) on the advancing side, the maximum process temperature and strain rate are reduced, although it increases the tool reaction loads. Furthermore, employing a specialized tool pin improves the material mixing, resulting in improved joint quality and reduced volumetric defects.
Salloomi and Al-Sumaidae [137] investigate the effect of rotational (i.e., 550, 950 rpm) and traverse speeds (i.e., 60, 60 mm/min) on thermal and the residual stress environments generated in FSW of AA2024-T3 to AA6061-T6, using effectively CEL finite element algorithm. The AA2024 is placed on the advancing side while the AA6061 on the retreating side. The H13 steel tool is characterised by 20 mm diameter concave shoulder with a 4 mm diameter tapered threaded pin and a height of 4.7 mm. They find that: i) the increase of the tool rotational speed leads to a relative increase in the temperature field, while the increase of the welding traverse speed causes a relatively slight decrease in the temperature field; ii) the longitudinal residual stress component is higher on the AA6061 side than the AA2024 side. This decreases with the increase of the rotational speed while it increases with the increase of the traverse speed; iii) the plastic strain on the AA6061 side is higher than that of AA2024, contributing highly to the temperature and residual stress fields. These results confirm that the tool rotational speed is more influential than the tool traverse speed.
3.3.1. Computational Fluid Dynamics
CFD provides insights into the fluid flow and heat transfer phenomena that occur during welding [138]. The primary concept behind this form of analysis involves replacing a continuous problem domain with a discrete domain. This is achieved by applying a grid to the area of interest and subsequently making approximations of the governing equations [123]. CFD allows to simulate and analyze the complex fluid flow patterns, temperature distributions, and material deformation that take place during FSW. It helps in understanding the fundamental physics of the process, optimizing process parameters, and predicting the resulting weld quality. Particularly, CFD allows to study:
- Fluid flow analysis: it helps in understanding the velocity profiles, the flow patterns, and the material displacement within the workpiece. By analyzing the fluid flow, it is possible to study the mixing and stirring of materials and identify regions of potential defects or inhomogeneity.
- Temperature distribution: by accounting for factors like heat generation, heat transfer, and cooling mechanisms, CFD simulations provide valuable insights into the temperature profiles and the gradients that influence the weld quality. This information helps in optimizing the welding parameters to control the heat input and avoid defects like overheating or insufficient heating.
- Residual stress and distortion analysis: the thermal and mechanical interactions between the tool, workpiece, and surrounding environment can be analyzed to predict the residual stresses and distortions that arise after welding. Understanding these effects aids in optimizing process parameters, tool design, and subsequent post-welding operations.
- Process optimization: CFD simulations allow for virtual experimentation, enabling the exploration of different process variables without the need for physical prototypes. It is possible to analyze the effects of tool geometry, rotational speed, traverse speed, and other parameters on the fluid flow, temperature distribution, and resulting weld quality.
Tang and Shen [139] develop a new CFD based model which is more suitable to simulate the heat transfer in dissimilar AA2024-AA7075 joints. The tool made of tungsten alloy consists of a cylindrical shoulder of 25 mm in diameter and a conical pin of 8 mm and 6 mm in upper and lower diameter, respectively, and 5 mm in length. It rotates at 1000 rpm and moves with a feed rate of 72 mm/min. The results clearly demonstrate a noticeable asymmetric temperature distribution between the advancing side and the retreating side. Additionally, the temperature profile exhibits discontinuity due to variations in material properties such as heat capacity, viscosity, and thermal conductivity. The mutual interaction between temperature and material properties further amplifies the disparities in the workpiece's temperature distribution. Furthermore, welding time plays a crucial role in determining the welding temperature. The temperature curves reveal a rapid increase followed by a slower decrease in temperature.
Padmanaban et al. [140] investigate the joining of dissimilar alloys AA2024 and AA7075 developing a numerical model based on CFD. They perform the process with a HSS taper threaded tool having a shoulder diameter of 17.5 mm, pin diameter of 5 mm and a pin height of 4.65 mm, by changing the rotational speed from 900 rpm to 1200 rpm and the welding speed from 20 mm/min to 60 mm/min. Particularly, they model the process using a viscoelastic stream beyond a rotating cylindrical tool. The result show that the peak temperature increases with tool rotation speed and shoulder diameter but decreases with welding speed.
5. Conclusions
This review is characterized by two goals, i.e., the highlighting of the main process parameter in a friction stir welding process between two dissimilar aluminum alloys and the showing the main tool to design and predict the mechanical behavior of dissimilar aluminum joints.
Although this joining technology is relatively new, a wide literature exists on the optimization of the process for both similar and dissimilar materials and, also, for both similar and dissimilar aluminum alloys. However, the thermal and mechanical behavior during the process is so complex that it requires significant experimental testing with an uncertain result about the more relevant parameters influencing strength, microhardness, or other mechanical properties. This is just more complicated if the materials to be joined are different. In this context, the literature about this topic is always in continuous evolution.
Moreover, to give an added value to the review, the main design tools are presented. In fact, to improve the prediction of the thermo-mechanical behavior and, consequently, the optimization of the process, several techniques, such as finite element analysis or artificial neural networks are going deeper. These techniques allow also to process many parameters that can be made variables.
Author Contributions
Conceptualization, Chiara Borsellino and Guido Di Bella; methodology, Chiara Borsellino and Guido Di Bella; data curation, Chiara Borsellino, Guido Di Bella and Federica Favaloro; writing—original draft preparation, Guido Di Bella and Federica Favaloro; writing—review and editing, Chiara Borsellino, Guido Di Bella and Federica Favaloro; supervision, Chiara Borsellino. All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
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