Investigation of the Effects of Tool Positioning Factors on Peak Temperature in Dissimilar Friction Stir Welding of AA6061-T6 and AA7075-T6 Aluminum Alloys

Abstract

Among the emerging new welding techniques, friction stir welding (FSW) is used frequently for welding high-strength aluminum alloys that are difficult to weld by conventional fusion-welding techniques. This paper investigated the effects of tool-positioning factors on the maximum temperature generated in the dissimilar FSW joint of AA6061-T6 and AA7075-T6 aluminum alloys. Three factors of plunge depth, tool offset, and tilt angle were used as the input parameters. Numerical simulation of the FSW process was performed in ABAQUS software using the coupled Eulerian–Lagrangian (CEL) approach. Central composite design (CCD) based on response surface methodology (RSM) was used to analyze and design the experiments. Comparison of the numerical and experimental results showed that numerical simulations were in good agreement with the experimental ones. Based on the statistical model results, plunge depth, tilt angle, and tool offset were the most significant factors on maximum process temperature, respectively. It was found that increasing the plunge depth caused a sharp increase in the maximum process temperature due to increased contact surfaces and the frictional interaction between the tool and workpiece.

1. Introduction

Friction stir welding (FSW) was one of the relatively novel welding techniques invented at The Welding Institute (TWI) in the UK in 1991 [1,2]. This process is categorized into a group of welding processes called solid-state bonding techniques [3]. The FSW joint is formed using a non-consumable tool that plunges into the workpiece and translates along the weld line [4,5]. Unlike other conventional welding processes, this technique does not need any filler materials, and the tool retracts from the workpiece after welding formation [6]. The main factors in producing an appropriate and defect-less joint are heat generation and its proper distribution into the welding zones [7]. To achieve the best conditions to form an adequate plastic flow, the maximum temperature created in the weld nugget should be in the range of 0.8 to 0.9 of the melting temperatures of involved materials in the joint [8]. In the FSW process, total heat is generated by two factors: severe friction between tool and workpiece and material flow created during the joint formation [9]. Based on the literature, a significant portion of generated heat belongs to the frictional condition between tool and workpiece, and the rest is related to plastic flow [10,11]. Due to asymmetrical heat distributions in the FSW joint, different regions are formed in the welding cross-section [12,13]. These regions significantly differ in plastic deformation, heat distribution patterns, and microstructural modifications [14]. In welding dissimilar materials, controlling heat distribution patterns has more significant importance because two dissimilar materials have different mechanical and thermal properties [15]. This leads to intensifying asymmetry in heat distribution in workpieces [16]. Many parameters affect heat distribution and the peak temperature created in weld zones [17]. These parameters can be classified into two major classes: tool shape and process parameters [18]. Tool shape consists of shoulder and pin geometries, and process parameters include translational speed, rotation speed, downward force, plunge depth, tilt angle, and tool offset [19,20]. These factors directly impact heat generation, temperature distribution, and plastic flow patterns [21,22]. Changing process parameters generally leads to a significant change in total generated heat and material flow [23]. These changes result in variation in the mechanical and microstructural properties of the final joint. Of the six mentioned process parameters, three factors are directly related to how the tool is positioned [24]. These factors are tilt angle, plunge depth, and tool offset from the weld line, which determine tool positioning in the welding patch [25]. Using a tilt angle leads to significant changes in forged material volume and plastic flow pumped in weld regions [26]. The plunge depth parameter indicates the value of shoulder penetration in workpieces during the FSW run [23,27]. Plunging the shoulder into workpieces creates severe friction and plastic flow in the upper zone, leading to extensive changes in heat generation, heat distribution, stir zone shape, and positioning different welding zones in the welded cross-section [28]. In welding dissimilar materials, tool offset towards higher-strength material is a practical solution for creating a relatively balanced contribution in material plastic flow [29,30]. More balanced heat distribution can be formed in two dissimilar parts by offsetting the tool at optimal distances [31]. According to the above, all three factors of tilt angle, plunge depth, tool offset significantly affect the FSW joint [32,33]. If the three mentioned parameters are optimized values, proper thermal conditions are created during the welding process. This leads to the formation of a defect-free joint with the highest mechanical properties.

There are relatively few studies that directly investigated the effects of three factors of tilt angle, plunge depth, and tool offset in the FSW process of similar and dissimilar joints. Soltani et al. [34] investigated three parameters of tilt angle, rotational speed, and feed rate in the dissimilar FSW joint of aluminum and stainless steel. The results showed that excessive increasing rotational speed or decreasing feed rate could lead to the formation of defects such as pleats, voids and decreased effective thickness in the welding region. Ghiasvand et al. [35] investigated the effects of tool offset, pin offset, and position of dissimilar alloys on the maximum temperature of FSW of AA6061 and AA5086 alloys. The results showed that pin offset was the most influential parameter affecting the maximum temperature. In all pin and tool offsets, placing the harder alloy (AA6061) at the advancing side resulted in more maximum temperature increment than the harder alloy at the retreating side. Zhang et al. [36] investigated offset effects on heat generation and material flow during the FSW process. According to their results, using a proper tilt angle led to increased heat generation, material mixing, and material plasticization in the weld nugget. Dialami et al. [37] numerically studied the effect of tilt angle on heat generation and material flow. They reported that using a tilt angle increased the peak temperature and material flow in the welding core because of softer materials with higher temperatures on the rear side of the tool. Kumar et al. [38] used different tilt angles to join AISI 316L stainless steel using the FSW process. Based on their results, tilt angle significantly impacted the welding zone’s dynamic volume, peak temperature, and shear layer thickness placed below the shoulder. Yuvaraj et al. [39,40] investigated and optimized the FSW tool parameters for welding dissimilar aluminum alloys of AA7075-T651 and AA6061 by implementing the Taguchi method. Based on their results, the tilt angle of the tool was the dominant factor with a contribution percentage of 45.85%, and other parameters like tool pin profile involved 40.27% and tool offset involved 12.36% for improving the tensile properties of the FSWed dissimilar AA7075-T651 and AA6061 joint. Kumar et al. [41] studied the effect of varying tool plunge depth on mechanical properties of dissimilar FSW joints of aluminum alloy plates of 2024-T351 to 7075-T65. A taper-threaded cylindrical (TTC) tool is employed to produce the composites at the weld nugget zone (WNZ) of FSW dissimilar joints by varying the plunge depth. Their results indicated that the lower plunge depth resulted in insufficient heat generation. Higher plunge depth led to decreased material flow to the advancing side of the weld region. Acharya et al. [42] investigated the influence of tool tilt angle on the mechanical strength of the friction stir-welded aluminum matrix composite. Results showed that excessive flashing has occurred in the weld bead with an increase in the tilt angle. It was found that the mechanical property of the joint increased as the tilt angle increased from 1 to 2 degrees, and after that, it decreased. Aghajani et al. [43] investigated the effect of tilt angle on the thermal history of the FSW joint of Al–Mg–Si alloy using the computational fluid dynamic method. They used a various range of tilt angles. Based on the obtained results, a higher temperature was generated on the advancing side by increasing the tilt angle. Zheng et al. [44] investigated the effects of plunge depth on mechanical properties and microstructure of dissimilar FSW joint of 2A70 aluminum alloy and Inconel 600 nickel-based alloy. The results of their study indicated that the plunge depth parameter severely affects the joint’s strength, and at a plunge depth of 0.3 mm, the highest joint efficiency was obtained. Ramachandran et al. [45] researched the influence of the tool offset value on the mechanical properties and microstructure of a dissimilar FSW joint of AA5052-H13 aluminum alloy and HSLA steel. Their results showed that the offset distance is a significant factor determining the microstructure modifications and mechanical properties of the FSW joints. Safeen et al. [46] statistically investigated the effects of four parameters of tilt angle, translational speed, rotation speed, and tool shape on microhardness and strength of the FSW joints of AA6061-T6 aluminum alloy. They reported that tool shape, rotation speed, translational speed, and tilt angle have the most impact on the strength and hardness, respectively. Periyasamy et al. [39] studied the influences of tilt angle, pin diameter, and tool offset on the tensile strength of dissimilar FSW joint of AA6061 and AA7075-T651. Their results showed that pin diameter, tool offset, and tilt angle had the most impact on the tensile strength, respectively.

So far, most studies performed have investigated the effects of process parameters on mechanical properties of FSW joints, and there little research on the thermal aspects of the FSW process. In all previous studies, three factors of tilt angle, plunge depth, and tool offset were considered alone or with other process parameters such as rotation speed, translational speed, and tool shape. It should be noted that the three aforementioned parameters directly determine the positioning conditions of the tool in the welding region. Investigations of effective levels of these factors simultaneously with other parameters like process speeds and tool shape lead to a misunderstanding of their effects on mechanical and thermal aspects of the FSW process. Since tilt angle, plunge depth, and tool offset have the same origins, they should be considered separate from other process parameters. Due to the lack of study in this field, there is a need to investigate the effects of these factors on heat generation and peak temperatures created in the FS welding zones. Therefore, this paper studied the impact of three factors of tilt angle, plunge depth, and tool offset on the peak temperature created in the dissimilar FSW joint of AA6061-T6 and AA7075-T6 aluminum alloys. ABAQUS finite element software with a coupled Eulerian–Lagrangian (CEL) approach was used for numerical simulations. The response surface method (RSM) was also used for a design of experiment (DOE) and statistical analysis of the results.

2. Materials and Methods

In the present study, three parameters of tool offset, plunge depth, and tilt angle were considered as independent variables, and the peak temperature created in the weld zone was considered as the response. As discussed before, this FSW tool offset refers to shifting of the tool axis from base metal interfaces (Figure 1a), and the tool tilt angle refers to the tilting of FSW tool axis compare raw metals normal axis (Figure 1b). It should be noted that the peak temperature in the FSW process occurred in the weld nugget below the tool shoulder. In experimental conditions, due to severe stir in the weld nugget, it is impossible to record the peak temperature in this region using thermocouples or thermometers, so other methods must be used to record and study this parameter. Therefore, the numerical simulation technique was used as an efficient means. It should be noted that the experimental run was used only to survey the accuracy of numerical models. For this purpose, an experimental model was welded with the FSW process, and the temperature history of a specified point was recorded and used to compare with numerical results.

To perform the FSW joint, two dissimilar aluminum alloys of AA6061-T6 and AA7075-T6 were used and cut in dimensions of 120 × 50 × 5 mm. The raw materials were obtained from a local market (AmalCast, Arak, Iran) and the properties of base metals used from the manufacturing company’s data sheets. The chemical composition and mechanical properties of the aforementioned alloys are illustrated in

Table 1. Chemical composition of AA6061-T6 and AA7075-T6 Aluminum alloys.

Aluminum Alloy	Chemical Composition (%)
AA6061-T6	Al	Mg	Si	Cu	Fe	Cr	Mn	Zn	Ti
	Balance	0.81	0.61	0.29	0.2	0.13	0.03	0.02	0.01
AA7075-T6	Al	Zn	Mg	Cu	Fe	Si	Cr	Ti	Mn
	Balance	5.11	2.04	1.11	0.61	0.33	0.229	0.027	0.014

and

Table 2. Mechanical properties of AA6061-T6 and AA7075-T6 Aluminum alloys.

Aluminum Alloy	Yield Stress (MPa)	UTS (MPa)	Elongation (%)
AA6061-T6	268	311	17
AA7075-T6	485	568	11

.

A conventional milling machine was used to perform the FSW process. According to the literature, in order to increase the joint strength and reduce the volumetric weld defects, the AA7075-T6 alloy, which has more tensile strength, was placed on the retreating side, and the other alloy was placed on the advancing side [47,48,49]. The surfaces of specimens were polished and cleaned by acetone to remove aluminum oxide which normally causes volumetric defects in the weld zone. A simple cylindrical tool with a flat pin and shoulder was used. The tool was made of H13 hard working tool steel. After manufacturing the tool, a thermal hardening operation was performed to increase its hardness. In Figure 2a, the schematics of the tool and its dimensions are displayed. In the experimental model (the model used for the verification step), all three parameters of tool offset, plunge depth, and tilt angle were equal to 0.

The translational and rotational speeds were 90 mm/min and 1180 rpm, respectively. It should be noted that the aforementioned speeds were set as constant for both experimental and numerical models performed in this study. A type K infrared thermometer (model Tir-8855 made by Terminator) was used to record the temperature, which has an accuracy of 1.5% and can measure the temperature up to 1370 °C. The thermometer was placed on the milling machine’s table to move simultaneously at the welding speed. The specified point for recording temperature history was on the advancing side and located at a distance of 15 mm from the weld line. The thermal recording point (P1) is depicted in Figure 1a. Figure 2b shows the welding machine and the temperature recording unit used.

3. Finite Element Method

There are different techniques to conduct a numerical simulation of the FSW process. Each of these techniques has certain advantages and disadvantages. In the present study, the FSW process was simulated using the CEL method. The CEL method is based on the combined use of Eulerian and Lagrangian elements during the simulation process. The tool is also modeled by this method, giving the possibility of investigating tool shape effects and interactions on the welding conditions. In all models conducted in this study, the workpiece and tool dimensions were considered the same as experimental dimensions. All models consisted of two dissimilar aluminum alloys, AA7075-T6 on the retreating side and AA6061-T6 on the advancing side. Like the experimental model, rotational and translational speeds of the tool were constant in all runs and equal to 1180 rpm and 90 min/min, respectively. It should be mentioned that the positions of two dissimilar alloys in advancing or retreating sides were assumed to be constant. The welding step is only simulated, and the plunging and retracting steps were ignored because of their minor effects on the peak temperature created in the process. Given that the main parameter in FSW joint forming is the plastic flow, this parameter should be considered in numerical simulation to model the materials flow correctly. Material flow in FSW has a viscoplastic behavior. Thus, one must use a proper constitutive model to consider the yield stress as a function of strain, strain rate, and temperature. Hence, in the present study, Johnson–Cook’s model was used to model the generated plastic flow [50]. Using this equation, heat generated due to plastic work was considered in calculating total heat in addition to the frictional heat generation.

$$\sigma =\left(A+B{\epsilon }_p^n\right)\left[1+C\ln \frac{{\dot{\epsilon }}_p}{{\dot{\epsilon }}_0}\right]\left[1-{\left(\frac{T-T_r}{T_m-T_r}\right)}^m\right]$$

(1)

In this equation, terms σ, ${\epsilon }_p$, ${\dot{\epsilon }}_p$, ${\dot{\epsilon }}_0$ are, respectively, the yield stress, the effective plastic strain, the effective plastic strain rate, and the normalized strain rate. Also, T, T_r, and T_m are temperature, transition temperature, and melting temperature, respectively. A, B, C, n and m are the constants of the material. These parameters are illustrated in

Table 3. Johnson–Cook’s parameters for AA7075-T6 and AA6061-T6 alloys [51,52].

	A (MPa)	B (MPa)	n	m	C	Tmelt (°C)
AA6061-T6	324	114	0.42	1.34	0.002	635
AA7075-T6	546	678	0.71	1.56	0.024	652

for AA6061-T6 and AA7075-T6 aluminum alloys. Also, temperature-dependent thermal and elastic properties of the alloys used are shown in

Table 4. Mechanical and thermal properties for AA7075-T6 and AA6061-T6 alloys [53,54].

Aluminum Alloy	Temperature	Density	Young’s Modulus	Conductivity	Specific Heat	Thermal Expansion
	°C	kg/m3	GPa	W/m·°C	J/Kg·°C	10−6/°C
AA6061-T6	25	2700	68.5	167	896	23.4
	100		66.1	180	978	24.6
	150		63.1	184	1004	25.6
	200		59.1	192	1028	26.6
	250		54	201	1052	27.5
	300		46.4	207	1078	28.5
	450		31.7	230	1133	30.7
AA7075-T6	25	2810	71.7	130	960	21.6
	100		70.8	148	980	23.4
	200		61.7	154	1064	24.3
	400		51.4	160	1146	26.2
	500		38.6	170	1203	28.5

.

It should be mentioned that to model the heat generated by tool–workpiece friction, two fractions named inelastic heat fraction and heat generated by friction fraction were used in software. The coefficient 0.9 was introduced to software to convert 90% of energy produced by plastic flow and friction to heat, which finally increases the tool temperature and workpiece.

A general contact algorithm was utilized to simulate contact interactions between the tool and workpieces. For this purpose, a temperature-dependent friction coefficient was introduced into ABAQUS software. According to literature, this parameter decreases with increasing temperature from a maximum value of 0.4 at room temperature to a minimum of 0.01 at melting temperature [55,56]. The tool and workpieces were modeled as Lagrangian and Eulerian parts, respectively. After mesh sensitivity analysis, the seed size of 1 mm was used for regions placed in the welding region and adjacent regions. Finally, the tool and workpiece were meshed by 1432 C3D8T and 15,000 Ec3D8RT elements, respectively. Figure 3 shows the final mesh of the tool and workpiece.

To verify the simulation results obtained in this study, numerical and experimental results of the base model (model used for verification process) were compared. In Figure 4, the temperature distribution contour of the verification model has been displayed, and Figure 5 shows the comparison chart between numerical and experimental results.

According to Figure 5, it was found that the numerical approach (CEL method) could properly evaluate temperature distribution. Due to good agreement between numerical and experimental results, it was proved that the results obtained from other models in this way could also be relied on.

4. Design of Experiments (DOE)

To investigate variables’ main effects and interactions on the response, the central composite design based on the RSM approach was utilized. Minitab and Design Expert software were used to perform analysis of variance (ANOVA). In the current research, three quantitative variables named tilt angle, plunge depth, and tool offset were considered inputs, and the maximum temperature in the process was considered output. According to the CCD, each variable was considered at five levels. In

Table 5. Input factors and their levels.

Factor	Unit	Level 1	Level 2	Level 3	Level 4	Level 5
Tool offset	mm	0	0.5	1	1.5	2
Tilt angle	Degree	0	1	2	3	4
Plunge depth	mm	0	0.1	0.2	0.3	0.4

, factors and their levels have been shown.

5. Results and Discussion

5.1. Statistical Model Survey

According to the DOE scheme used in this study, 20 samples were modeled and simulated by the software.

Table 6. Design matrix and response values.

Model	Tilt Angle (Degree)	Plunge Depth (mm)	Tool Offset (mm)	Peak Temperature (°C)
1	1	0.1	0.5	438
2	1	0.1	1.5	443
3	3	0.1	0.5	550
4	3	0.1	1.5	470
5	1	0.3	0.5	455
6	1	0.3	1.5	462
7	3	0.3	0.5	564
8	3	0.3	1.5	506
9	2	0.2	0	439
10	2	0.2	2	388
11	0	0.2	1	476
12	4	0.2	1	576
13	2	0	1	533
14	2	0.4	1	563
15	2	0.2	1	535
16	2	0.2	1	535
17	2	0.2	1	535
18	2	0.2	1	535
19	2	0.2	1	535
20	2	0.2	1	535

shows the DOE matrix and the response obtained from each model.

In statistical studies, the R-squared and adjusted R-squared values determine the accuracy of the regression equation obtained. Table 6 shows the coefficients of determination of the statistical model. A 95% confidence level was considered in statistical analysis, and ANOVA was used to investigate variables’ main and interaction effects. The ANOVA test for UTS is shown in

Table 7. The coefficients of determination.

Source	Std. Dev.	R2	Adjusted R2	Predicted R2	PRESS
Linear	45.51	0.3729	0.2554	−0.1407	60290.36
2 Factor Interaction	48.22	0.4280	0.1640	−0.3120	69344.58
Quadratic	15.29	0.9558	0.9160	0.6469	18663.92	Suggested
Cubic	16.74	0.9682	0.8993	−5.5313	3.452 × 105	Aliased

. In the statistical analysis based on the DOE approach, essential factors determining the governing model’s precision and accuracy are the coefficients of determination: R-squared and adjusted R-squared. R-squared (R2) is the proportion of the variation in the dependent variable that is predictable from the independent variables. Adjusted R-squared is a modified version of R-squared adjusted for the number of predictors in the model. In both cases, the coefficients of determination typically range from 0 to 1. The closer the values of these parameters are to 1 or 100% the higher the accuracy of the model adaptation and the higher the quality of the proposed regression model. In Table 7, the coefficients of determination of the present model have been shown. It should be noted that a 95% confidence level was considered, and ANOVA was used to investigate independent variables’ main and interaction effects. The ANOVA test for the response is shown in

Table 8. Analysis of variance (ANOVA) test for the peak temperature.

Source	Sum of Squares	Degree of Freedom	Mean Square	F-Value	p-Value
Model	50,514.69	9	5612.74	24.01	<0.0001	Significant
A-Tilt Angle	3249.00	1	3249.00	13.90	0.0039	Significant
B-Plunge Depth	15,129.00	1	15,129.00	64.71	<0.0001	Significant
C-Tool Offset	1332.25	1	1332.25	5.70	0.0381	Significant
AB	2812.50	1	2812.50	12.03	0.0060	Significant
AC	72.00	1	72.00	0.3080	0.5911	Insignificant
BC	24.50	1	24.50	0.1048	0.7528	Insignificant
A2	26,002.92	1	26,002.92	111.23	<0.0001	Significant
B2	409.17	1	409.17	1.75	0.2153	Insignificant
C2	54.03	1	54.03	0.2311	0.6410	Insignificant
Residual	2337.86	10	233.79
Lack of Fit	2337.86	5	467.57
Cor Total	50,514.69	9	5612.76	24.01	<0.0001

.

To test the regression model, normalization of the statistical distribution of residuals and lack of self-correlation and independence among residuals are needed. Due to Table 6, a quadratic regression model was chosen. The main factors and their squares and interactions constitute the regression model. To evaluate the model, one must test it statistically, the results of which are shown in Figure 6. Figure 6a,c are representative of the scattering of residuals around the normal line. Thus, the model residuals have the normal distribution process pattern. This result can be obtained by the Kolmogorov–Smirnov test. Figure 6b,d show the normal and independent distribution of residuals and lack of self-correlation among residuals, respectively.

The final regression equation for the peak temperature was obtained as Equation (2) as a function of dependent variables.

$$T_{\max }=285.3+145.9A+809B+12.5C-32.16A^2-403B^2-5.9C^2+18.75AB+6AC+35BC$$

(2)

According to the ANOVA test and Equation (1), all the main factors directly affect the peak temperature of welding samples, and their variations significantly change the response value. Figure 7 has shown the effectiveness and contribution of the model’s parameters as a Pareto chart. Due to the ANOVA and expressed chart, among the three main factors, plunge depth, tilt angle, and tool offset have the most impact on the peak temperature of the welded samples.

5.2. Plunge Depth Effect

In Figure 8, the maximum temperature variations vs. plunge depth variations for welded samples have been shown. Based on Figure 8, it was found that increasing plunge depth led to a significant increase in the maximum temperature of the FSW process. In general, friction and plastic flow created in the FSW process result in heat generation in the weld zone [57,58]. Generated heat produced by the aforementioned factors significantly changes with plunge depth. By immersing the tool into the workpiece, frictional interactions between the tool and workpiece increase due to increased contact surfaces. This leads to increasing vertical material flow heat generated in contact regions [43,59]. It should also be noted that by plunging the tool shoulder into the workpiece, the amount of material forging towards the thickness direction undergoes significant growth. It causes an increase in the plasticization process and heat is generated due to plastic work.

5.3. Tilt Angle Effect

In Figure 9, the maximum temperature variations vs. tilt angle variations for welded samples are shown. The presence of a proper tilt angle during the welding step causes materials trapped and plasticized below the tool shoulder to be pumped into the weld nugget due to an oblique forging force applied by the tool [59,60]. By forging accumulated plastic flow in the trailing edge of the tool, a good amount of compression and material mixing occurs in the welding core, leading to heat concentration and increasing the maximum temperature created in this region. It should be noted that the tilt angle factor also has an optimal level and must be placed at this. By increasing the tilt angle from its optimal value, frictional interactions between the tool and workpiece and frictional interface decrease in the tool’s leading side. It causes a significant decrease in heat and plastic flow generations, and finally decreases the maximum temperature created during the FSW process. Using an optimal tilt angle leads to placing the maximum temperature of the welding process in its optimal range, between 0.8 to 0.9 of the melting temperatures, and increasing the mechanical properties of the FSW joints. According to Figure 9, it was found that the maximum temperature occurred at a tilt angle of 1.8 degrees, and the maximum temperature decreased in tilt angles higher and lower than this value. According to Figure 9, it was shown that lower values of the peak temperatures occurred in samples with higher tilt angles.

5.4. Tool Offset Effect

In Figure 10, the maximum temperature variations vs. tool offset variations for welded samples have been shown. In the dissimilar FSW process, each alloy’s contribution in heat generation, heat distribution, and material flow created in the process determines the joint’s final mechanical and microstructural properties. The lack of balanced contributions of material flow produced by two dissimilar materials leads to the lack of proper material mixing in the welding core [61,62]. This finally causes increasing weld defects and decreasing joint quality. The amount of heat and the maximum temperature generated in the welding process must be formed so that two dissimilar materials undergo thermal softening correctly and make sufficient material flow contributions to the joint formation. One of the factors affecting heat generation and the peak temperature in the FSW joint of two dissimilar materials is the tool offset towards harder material. By changing the tool offset parameter, heat generated in the welding region varies due to modifications in material plasticized volume and material flow pattern. Based on the results displayed in Figure 10, increasing tool offset towards a harder alloy caused an increase in the peak temperature. However, this factor had significantly less effect on the response than other independent variables. By offsetting the tool towards a harder material, the amount of contribution in plastic flow formation increases, and the volume of plasticization in the welding region undergoes severe growth that finally leads to increased heat generation.

5.5. Interactions of Parameters

The contour plots of interactions are two-dimensional plots that are very useful for studying the interaction effects of two factors on outcome in a single plot. Figure 11 have shown two-dimensional interaction contours of the effects of three factors investigated on the peak temperature response. According to Figure 11, all three factors of tool offset, tilt angle, and plunge depth had significant interactions on the response and each other. Based on the displayed contours, it was found that the highest interactions belonged to the interaction of two parameters of plunge depth and tilt angle. Due to the results, if the positioning parameters of the tool are not appropriately chosen, the peak temperature may rise to the melt temperature, leading to local melt in the welding region, which significantly decreases the joint quality.

6. Conclusions

The present study investigated the numerical and statistical analysis of the effect of three factors of tilt angle, plunge depth, and tool offset on the maximum temperature created in the FSW process of two dissimilar aluminum alloys: AA6061-T6 and AA7075-T6. The following outcomes were obtained:

• Among the three main factors, plunge depth, tilt angle, and tool offset had the most significant impact on the maximum temperature of the welded samples, respectively.
• Increasing the plunge depth caused a sharp increase in the maximum process temperature due to increased contact surfaces and the frictional interaction between the tool and the workpiece.
• The highest interaction of variables was related to the two factors of plunge depth and tilt angle, and the lowest interaction was related to the two factors of plunge depth and tool offset.
• The highest process temperature was formed at a tilt angle of 1.8 degrees, and the maximum process temperature decreased at values less and more than this angle. The lowest peak temperature values occurred in samples with larger tilt angles.
• Increasing the eccentricity of the tool towards the harder alloy led to an increase in the maximum process temperature.