Thermal history analysis of friction stir processed and submerged friction stir processed aluminum

Materials Science and Engineering A 465 (2007) 165–175

Thermal history analysis of friction stir processed and submerged friction stir processed aluminum Douglas C. Hofmann, Kenneth S. Vecchio ∗ Department of Mechanical and Aerospace Engineering, University of California at San Diego, La Jolla, CA 92093, United States Received 26 July 2005; received in revised form 1 February 2007; accepted 1 February 2007

Abstract In this research, equations are developed to model the rate of heat input from different geometries of friction stir processing (FSP) tools. The model is then compared with actual heat input obtained from embedded thermocouples within the stirred region. The cooling curves obtained from the thermocouple data are then applied to the Derby–Ashby model for high angle grain boundary migration to predict the final grain size of a bulk sample produced by the friction stirring method. Submerged friction stir processing (SFSP) is introduced as a way of increasing the cooling rate of the bulk samples in an attempt to decrease the grain size. Microstructures obtained from both FSP and SFSP are characterized using transmission electron microscopy. © 2007 Elsevier B.V. All rights reserved. Keywords: Friction stir processing; Thermal history analysis; Grain refinement; Severe plastic deformation; Nanocrystalline grain size

1. Introduction Friction stir processing (FSP) is a severe plastic deformation method that is used to produce bulk samples of fine-grained microstructure utilizing the same technique employed to join samples in friction stir welding (FSW). The thermomechanical history of a sample produced through FSP affects the final grain size and therefore, the mechanical properties of the bulk material. In the current research, a model is developed to determine the rate of heat input from various parts of a FSP tool, and that model is then compared with actual data obtained by a series of embedded thermocouples in the stirred region. The cooling curves obtained from the thermocouples are then used in a grain boundary migration model to predict the amount of grain growth in the stirred region. The actual grain size, as observed by TEM, is then compared to the model. The heat input into the sample during FSP has a major affect on the grain size and the amount of recrystallization that occurs. For the current research, the most important modeling work was done by Schmidt et al. [1]. Their work produced an analytical model for heat generation in FSW based on the geometry of the tool. There has been a longstanding ∗

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controversy about exactly what part of the tool contributes to the majority of the heat input. This can be addressed by first creating a model for heat input and then comparing the model to actual heat flow data. The model by Schmidt et al. [1] shows that the heat input was 86% from the shoulder of the tool, 11% from the sides of the bit, and 3% from the tip of the bit. The analysis shows that the majority of the heat is input by the shoulder and not by the welding tool bit itself, as once thought [2]. Another heat input model was derived by Song and Kovacevic [3]; their data showed that only 2% of the heat generation was created by the bit. They compare this with Russel and Shercliff [2], where it was estimated that 20% was input by the bit. A goal of the present research is to model what percentage of the heat is input by the various parts of the welding tools, and to compare these results with experimental measurements. An alternate method of FSP is also discussed here, wherein the entire stirring procedure was done submerged in water. This technique, called submerged friction stir processing (SFSP) [4], is beneficial in that the water absorbs the residual frictional heat created by the spinning bit on the sample’s surface and cools the stirred region significantly faster than in air. In addition, the contact situation between the tool shoulder and the sample surface has been shown to be similar to stir processing experiments done in air so that a solidly stirred region can be obtained by this method [4].

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The major incentive for developing the friction stir processing technique is to produce bulk metals with highly refined grain structures via the severe plastic deformation path. As such, it is necessary to model the grain growth that may occur due to the remnant heat in the sample from the stir processing. This modeling can then be used to optimize the cooling conditions of the stirred sample to obtain nanoscale grain structure. A kinetic model to determine the recrystallized grain size resulting from high angle boundary migration acting in a deformed sample has been developed by Derby and Ashby [5]. According to their model, the time required for a grain boundary to grow to a diameter S is a function of the linear grain boundary migration rate, g: t=

S 2g

(1) 2. Experimental procedure

The linear grain boundary mobility rate is a function of the grain boundary mobility, M, and the driving force, F: g = MF

(2)

The grain boundary mobility is dependant on the grain boundary diffusivity multiplied by the boundary layer thickness, δD0 . In addition, the driving force, F, is the stored work energy of the subgrain walls. This can be approximated as a function of the shear modulus, μ, the Burgers vector, b, and the subgrain boundary misorientation angle, θ:   Q δD = δD0 exp − (3) RT δD0 exp(−Q/RT )b (4) kT 3μbθ F= (5) L In the above equations, Q is the activation energy, R the gas constant, k the Boltzman’s constant, T the temperature in Kelvin, and L is the subgrain diameter. Hines and Vecchio [6] combined Eqs. (3)–(5) to determine the time–temperature profile required to obtain a grain diameter of 0.2 ␮m (the grain size observed in the shear band cooling of a deformed copper sample) according to the following equation: M=

t(T ) =

Vecchio [6] calculated that the final grain size obtained within the shear bands should be less than 5 × 10−6 ␮m, while the observed grain size was 0.2 ␮m. This demonstrated that either the boundary migration mechanism is not operative in dynamic shear band recrystallization, or that the model breaks down under the rapid cooling conditions obtained from shear band cooling (from >500 to 300 K in 10−3 ␮s). However, the model may be suitable to describe the cooling and subsequent grain growth obtained through FSP and SFSP (300 K drop in less than 20 s). A focus of the current research is to use the thermomechanical history of a friction stir processed sample to predict its final grain size. It is also pertinent to determine the necessary cooling conditions for FSP that will lead to nanometer scale grains (<50 nm) in a bulk FSP sample.

SLkT 6δD0 b2 μθ exp(−Q/RT )

All friction stir processing (FSP) and submerged friction stir processing (SFSP) was completed on a Bridgeport Series I, 2-HP mechanical mill. The mill has a low gear capable of spindle speeds from 60 to 400 rpm and a high gear capable of 400–3300 rpm. It has lever-controlled automatic feeds on both the x- and y-axes that can travel between 0 and 14.8 mm/s. The head can tilt up to 30◦ in any direction to allow for angle of attack (non-perpendicular to the sample surface) welding. Downward force is applied to the sample by hand with a 30.5 cm long handle. All welding tools conformed to standard sized collets: 12.7, 19.1 and 25.4 mm diameter; two typical processing tools are shown in Fig. 1. The apparatus used for FSP and SFSP is shown in Fig. 2, and a schematic diagram of it is shown in Fig. 3. The apparatus consists of a large aluminum block fitted with sample constraints and contained in a Plexiglas tank filled with water to perform SFSP. Thermal measurements were made using 10 K-type thermocouples embedded in a steel channel set below the sample being stirred. Five thermocouples recorded the temperature directly below the tool, and five more were at the edge of the sample, shown in Fig. 4.

(6)

Eq. (6) can be rearranged to yield the grain size as a function of the time-dependent temperature profile associated with a nonisothermal temperature profile, such as occurs in adiabatic shear bands or friction stir processing: t(T )6δD0 b2 μθ exp(−Q/RT ) (7) LkT The Derby–Ashby grain boundary migration model [5] has been successfully employed to predict recrystallized grain sizes under a wide array of isothermal recrystallization treatments. However, the application of this model or its associated mechanism may not be valid under rapid non-isothermal temperature profiles. For example, using the model by Derby and Ashby [5], Hines and

S=

Fig. 1. Two tools used for FSP showing radius of curvature and escape channel. Tools made from end-mills that were ground to size on a circular grinder. Escape channel made on lathe. (Left) 12.7 mm shoulder diameter and (right) 19.1 mm shoulder diameter.

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Fig. 2. The welding apparatus used for thermal analysis. An aluminum block is encased in a Plexiglas case to contain water. Omega digital controller with 10 K-type thermocouples is shown.

Fig. 3. Schematic diagram of the stirring fixture apparatus used for FSP and SFSP.

For the current research, all of the samples that were used in FSP and SFSP were 3.2 mm thick plates of Al-6061-T6, with an initial grain size of approximately 50 ␮m. The welding tools used were created from tool steel and had a 19.1 mm diameter shoulder, 3.18 mm diameter bit, and a bit length of 2.79 mm. The tool velocity used was 1000 rpm and the feed rate was varied between 0.42 and 1.69 mm/s. Typically, a friction stir weld starts with a plunging action, where the bit in rotated against the sample until enough heat is generated for sample to plastically deform and accept the bit. For many of the tests, this plunging was removed to limit the heat input into the sample, and thus reduce grain growth.

3. Results and discussion Equations need to be derived to model the heat flow in friction stir processing and submerged friction stir processing, The model by Schmidt et al. [1] is modified here to take into account the differences in the tool design used for FSP as opposed to FSW. The tools used in this research contain a radius of curvature, as shown in Fig. 5, to prevent shearing of the tool bit during the multiple passes associated with creating bulk samples. The first part in developing heat input equations is to understand the contact states present so that the friction on the surface of a sample can be estimated. Using Coulomb’s Law of Friction, the shear stress on the sample is equal to the friction coefficient multiplied by the contact pressures: τfriction = μ(p) = μ(σ)

Fig. 4. Steel insert, showing location of 10 thermocouples. Temperature readings were taken from beneath sample being welded.

(8)

where μ is the friction coefficient, p and σ are the distinct contact pressures used by Schmidt et al. [1]. The three contact states are the sticking condition, the sliding condition, and the partial sliding/sticking condition. These contact states are complicated to model, and are not necessary to determine specifically if one aims to find the relative percentages of the total heat input for the various parts of the tool. The

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Fig. 5. Comparison of FSP and FSW tools. (a) Typical FSW tool showing escape channel and (b) FSP tool used in current work showing radius of curvature from grinding wheel.

Fig. 6. The four parts of a FSP tool that touch the sample.

total heat input into a sample is the sum of the multiple parts of the FSP tool. Using Fig. 6, the bit is divided into four parts: the tool shoulder, the bit cylinder, the bit tip, and the bit radius. The total heat input is the sum of the heat inputs from these parts of the tool: QTOTAL = Q1 + Q2 + Q3 + Q4

(9)

For the first three tool geometries (shoulder, bit cylinder, and bit tip), cylindrical coordinate surface orientations are introduced. From these orientations, the heat generation for each of the three parts is derived. Fig. 7 from Schmidt et al. [1] shows the cylindrical coordinate system used in this analysis.

Heat generation for FSP is given below, where ω is the tool angular speed and M is the torque (Eq. (11)). The torque M, is simply equal to the contact force times the position on the tool radius (Eq. (12)), and the contact force is then equal to the contact shear stress, τ contact , times the surface area (Eq. (13)). The total heat generation is then the double integral over the tool radius and the angle θ (Eq. (14)): dQ = ω(dM)

(10)

dQ = ωr dF

(11)

dQ = ωrτcontact dA

(12)

 Q=

Fig. 7. Surface orientations in cylindrical coordinates used to derive heat flow equations. Model from Schmidt et al. [1].

ωrτcontact dA dθ

(13)

The derivations for the heat generation for the first three parts of the welding tool are shown below, where Q1 is the heat generated by the tool shoulder (Eqs. (15) and (16)), Q2 is the heat generated by the bit cylinder (Eqs. (17) and (18)), and Q3 is the heat generated by bit tip (Eqs. (19) and (20)). The surface areas associated with each of these heat generating parts is shown schematically in Fig. 8.

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Fig. 8. (a) The tool shoulders, source of Q1 , (b) the bit cylinder, source of Q2 , and (c) the bit tip, source of Q3 .

• For the tool shoulder, Q1 is  2π  Rs ωτcontact r 2 dr dθ Q1 = 0

R+ρ

2π Q1 = ωτcontact (R3s − R3 − 3ρR(R − ρ) − ρ3 ) 3 • For the bit cylinder, Q2 is  2π  H−ρ ωτcontact R2 dz dθ Q2 =

(14)

The value for the heat input from the bit radius can be rewritten in cylindrical coordinates as follows:  2π  0  ωτcontact (R+ρ+ρ sin(β))2 +(ρ−ρ cos(β))2 Q4 = 0

(15)

−π/2

× (R + ρ + ρ sin(β))ρ dβ dθ

(21)

Q2 = ωτcontact 2πR2 (H − ρ)

(17)

• For the bit tip, Q3 is  2π  R ωτcontact r 2 dr dθ Q3 =

(18)

This integral can be estimated numerically using known values for the welding tool dimensions. Non-dimensional ratios for heat inputs can be obtained without having to estimate the contact shear stress (τ contact ), by dividing the heat input from either the shoulder or bit by the total heat input. The bit radius does not contribute to the heat input during the plunge but does contribute when the shoulder is pressed down on the sample. Thus, it can be assumed that the bit radius is part of the shoulder input. The fraction of heat input (f) for each part of the tool is shown below:

(19)

fshoulder =

0

0

(16)

0

0

Q3 = ωτcontact

2π 3 R 3

The fourth part of the tool, the bit radius, generates heat given by Q4 . This part contributes to a significant area of the tool and is necessary to avoid the shearing of the bit from the shoulder. The radius is a result of grinding the bit down to size on a circular grinding wheel. The bit radius can be widened or reduced by dulling or sharpening the grinding wheel. The radius is assumed to be one-fourth of a circle as shown in Fig. 9. To find the surface area of the bit radius, an equation is written to represent the radius in the x–y plane and then it is revolved around the y-axis to create the entire surface. The value for the heat input from the bit radius can be written in Cartesian coordinates as follows:   2   dy 2 2 Q4 = ωτcontact x + y x 1 + dx dθ (20) dx

Fig. 9. The bit radius, source of Q4 .

fbit =

Q1 + Q 4 QTOTAL

Q2 + Q3 QTOTAL

(22) (23)

3.1. Experimental temperature data Fig. 10 shows an example of the thermocouple data obtained from a single pass FSP where the plunging action was completed. The figure shows that below the spinning bit, the maximum temperature reached in the sample was 480 ◦ C, less than the melting temperature of Al-6061-T6 at 650 ◦ C. Another interesting thing to note about the plot is that the temperature difference between the weld and the edge of the sample is between 200 and 400 ◦ C. This means that there is a large input of heat into the sample at the weld interface, and it happens quite rapidly. In addition, material remote from the friction stirred region can experience a temperature rise greater than 100 ◦ C as a result of a single friction stir pass. A 3D plot can also be used to show the motion of the bit across the sample using the temperature at each of the thermocouples on the weld interface. From Fig. 11, it can be seen that the temperature is above 400 ◦ C only for the brief period when the bit is directly above the thermocouple. The next step in the heat flow analysis is to match the actual thermocouple data with the model derived previously. Two FSP experiments were performed; one with the 19.1 mm (0.75 in.) diameter shoulder, and the other with a 12.7 mm (0.5 in.) diam-

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Fig. 12. Heat flow graph showing FSP with 19.1 and 12.7 mm shoulder fit with linear trend lines to the heat input rate. The shallow slopes represent heat input from the bit and the steep slopes are the heat input from the entire tool. Dividing these slopes leads to the heat input percentages. Fig. 10. Heat flow for FSP in Al-6061 done at 1000 rpm and 0.42 mm/s. The maximum temperature reached on the bottom of the sample was 480 ◦ C, lower than the 650 ◦ C melting temperature of the alloy.

Fig. 11. 3D plot of FSW across the weld interface. The axis into the page is the cross-section of the weld, the horizontal axis is time and the vertical axis is temperature. The plot shows that the maximum temperature is reached when the bit is directly over the thermocouples.

eter shoulder, and the data is compared to the model, shown in Table 1. The actual thermocouple data was obtained from the first thermocouple under the samples and is shown in Fig. 12. Table 1 Heat input percentages for various bit designs FSP in aluminum Radius of curvature, ρ (mm) Bit radius, R (mm) Shoulder radius, Rs (mm) Height of bit, H (mm)

12.7 mm shoulder 1.59 1.59 6.35 3.05

19.1 mm shoulder 1.59 1.59 9.53 3.05

25.4 mm shoulder 1.59 1.59 12.70 3.05

Literature 0.00 3.00 9.00 4.00

% input from shoulder % input from bit cylinder % input from radius % input from tip

80.6 4.0 13.9 1.4

93.9 1.2 4.4 0.5

97.40 0.53 1.87 0.19

83.9 12.9 0.0 3.2

Shoulder + radius input (%) Bit input (%)

94.6 5.4

98.3 1.7

99.3 0.7

83.9 16.1

From Fig. 12, the heat input from the bits are the shallow slopes portion of the data curves, when the bit and the edges of the bit are touching the sample. The steeper slope portion of the data represent heat input from the all parts of the tool. Linear fits of these regions are included in the plot to show the average slope value. Thus, the heat input from the bit can be found by dividing the shallow slope by the steep slope. The actual heat flow data is tabulated in Table 2 along with the prediction from the model. The data shows that the derived model is extremely accurate for the bits used in this research. More importantly, clearly demonstrates that the tool shoulder contributes the majority of heat input. For the tools used in this experiment, the shoulder provided more than 90% of the heat contribution. However, by simply increasing the size of the bit, this percentage can be dropped as low as 80% from the shoulder. FSP was attempted with shoulders less than 12.7 mm with bits comparable with the thickness of the sample, but there was not sufficient confinement under the shoulder to produce a solid stirred region. Conversely, large bits were attempted, where the diameter was much larger than the sample thickness. In these cases, the material was unable to completely flow around the bit, and again solid stirred regions were not obtained. Thus, this research shows that the heat input from the shoulder should be between 80 and 99% of the total heat input for optimal friction stir processing. Due to the complex tool–sample interaction, it was more convenient to measure the interface temperature during FSP using the bottom of the sample so that the spinning bit did not damage the thermocouples. However, this indicates that the temTable 2 Comparison of model with actual heat flow data

Heat input from shoulder data (%) Heat input from bit data (%) Shoulder heat input from model (%) Bit heat input from model (%)

12.7 mm shoulder

19.1 mm shoulder

94.7 5.3 94.6 5.4

98.7 1.3 98.3 1.7

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Fig. 13. Actual thermocouple data showing the difference between the surface temperature and the bottom temperature of a sample during FSP.

perature readings from the thermocouples do not represent the actual temperature inside the welded region. As a result of the temperature gradient across the thickness of the aluminum samples, the actual surface temperature is noticeably higher than the thermocouples indicate. To determine the temperature gradient across the sample, a thermocouple was embedded in a channel at the surface of the sample, just under the shoulder, and another was placed under the sample. A spinning shoulder with no bit was placed over both thermocouples, and the temperature profiles from both thermocouples were recorded, shown in Fig. 13. At equilibrium conditions, the surface temperature reached 500 ◦ C, while the bottom temperature reached approximately 350 ◦ C. On average, under typical stirring conditions used here, the surface temperature is approximately 150 ◦ C higher than the base temperature. However, as the temperature gets closer to ambient, the difference between the bottom temperature and the surface temperature decreases, as shown in Fig. 14. An exponential fit can be applied to the data in Fig. 14 to obtain an equation to estimate the top surface temperature curve with the approximate bottom surface temperature measurement. For this data, the exponential fit relating the change between the top surface and the bottom temperatures is given as follows: T = 77.35 e0.0088x

Fig. 14. A plot showing the difference in temperature between the surface and the bottom of the FSP samples. An exponential fit is applied to this data to fit heat flow data to reflect actual surface temperatures.

3.2. Grain size modeling from thermal history The grain size modeling approach used by Hines and Vecchio [6] based on the work of Derby–Ashby [5] was employed to determine the final grain diameter using the thermal history experimentally measured for the stirred material. In the model, the grain size, S, is given by the following equation (using the

(24)

where T is the temperature obtained from the thermocouple measurement and x is the difference between the surface temperature and the bottom temperature at T. Using this exponential fit, the surface temperature of a sample can be estimated using the thermocouple readings from the bottom of the plate using Eq. (25) and the adjusted cooling curve for a FSP experiment is shown in Fig. 15:   1 T Tsurface = ln +T (25) 0.0088 77.35

Fig. 15. Cooling curve obtained from thermocouple data adjusted to reflect the actual surface temperature.

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boundary migration model from [5]): 6t(T )δD0 b2 μθ exp(−Q/RT ) (26) LkT For aluminum, the boundary layer thickness, δ, was assumed to be 0.5 nm, the grain boundary diffusivity, D0 , was 0.025 cm2 /s, Burgers vector, b, was 0.286 nm, the shear modulus, μ, was 26 GPa, and the activation energy, Q, was 142 kJ/mol. The subgrain diameter, L, was assumed to be 0.1 ␮m, and the average misorientation angle, θ, was assumed to be 5◦ (both values taken from Hines and Vecchio [6]). The material property data for aluminum was obtained from Lundy and Murdok [7]. The thermocouple readings were obtained twice every second, so the time that the stirred material remained at a given temperature was assumed to be 0.5 s. For each temperature reading, the final grain diameter for each temperature, S, was calculated, and these values were summed to obtain the final grain diameter in the sample based on the cooling rate. Fig. 16 shows the cooling curves from four stirring experiments, where the data shown is the actual thermocouple data taken from the bottom of the sample. Two of the curves were done with plunging and then stirring, one in air and the other submerged. The other two curves are both SFSP samples that were done with rapid axial feed rates. From Fig. 16, it is obvious that removing the plunging step and stirring the samples submerged, cools the stirred region much faster than conventional stirring techniques in air. Fig. 17 shows the grain growth model results based on the adjusted cooling curves of the temperature profiles seen in Fig. 16. For the grain growth model to be accurate, the cooling curves need to represent the heat experienced by the stirred S=

Fig. 16. Actual heat flow curves from bottom of sample corresponding with four stirring tests, two with plunging and two without plunging.

Fig. 17. Predicted grain size based on Derby–Ashby model [5] for the four stir processing experiments shown in Fig. 16.

material, not the bottom of the plate, so they were adjusted in a similar manner described previously and shown in Fig. 15. With the temperature dependant adjustment, the final grain size predicted from FSP in air with plunging is 5.6 ␮m. This grain size is in good agreement with typical literature values for FSW in Al and is very similar to what is observed from the TEM analysis of these samples, shown in Fig. 18. The grain growth model was next applied to the SFSP sample where plunging was done, and the final grain size was calculated to be 4.1 ␮m. This grain size matches well with experimental observations of this sample given in Fig. 19, where the grain size is shown

Fig. 18. Single pass FSP in Al-6061-T6 at 1000 rpm and 1.27 mm/s feed showing a region of 1–2 ␮m grains within a larger grain structure approximately 5 ␮m in size.

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Fig. 19. Single pass SFSP in Al-6061-T6 at 1000 rpm and 0.42 mm/s feed showing overall grain size of approximately 2–3 ␮m.

to be approximately 2–3 ␮m. Fig. 17 also shows the calculated final grain size from the two submerged samples where the plunging was removed. For the submerged FSP at an axial feed rate of 1.27 mm/s, the model predicts a final grain size of 134 nm, whereas the observed grain size was ∼200 nm, as shown in Fig. 20. The other SFSP experiment, executed at an axial feed rate of 1.69 mm/s, was cooled sufficiently fast that there was not enough time for the conduction of heat to reach the thermocouples. The model predicted less than 1 nm of grain growth and yet the final grain size observed for such a sample is between 100 and 200 nm. The discrepancy between the observed grain size and the predicted grain size can be attributed to the thermal conductivity being too slow to keep pace with the stir feed rate and not to the breakdown of the Derby–Ashby model [5]. Fig. 21 shows a plot of grain diameter versus time for the two experiments done where the plunging action was implemented. The higher curve, where a final grain size of 5.6 ␮m is calculated, represents a typical FSP experiment reported in the literature [2,3,8–26]. The lower curve in Fig. 21 demonstrates that under virtually the same stirring conditions, SFSP can be used as an alternative to FSP, and an approximately 20% smaller grain size can be obtained. Fig. 22 demonstrates that if the plunging action is removed from submerged FSP and the axial feed is increased, the grain growth can be slowed such that the final grain size is in the submicron range. Fig. 20 shows TEM micrographs of material processed in this manner revealing the submicron grain structure. For grains sizes in the submicron range, the initial size of the recrystallized grains plays an important role in the final grain size, as predicted by the Derby–Ashby model [5]. When the stir processing is done in air, and the resulting grain size is 2–6 ␮m, the initial grain size, on the nanometer scale, does not significantly affect the final calculated grain size. However, when the cooling curves predict submicron grain sizes, the initial grain size must be taken into account. This initial grain size approximation must be done because the value S, in the Derby–Ashby

Fig. 20. (a and b) TEM micrographs of single pass SFSP in Al-6061-T6 at 1000 rpm and 1.27 mm/s showing grain size on the order of 200–300 nm. The increased feed rate allows the water to quench the sample’s surface more rapidly, slowing the grain growth.

model [5], is the distance that a migrating grain boundary will sweep out after a given time. Thus, it represents the increment of grain growth from an assumed initial grain nucleus size, and not the final grain size for a given cooling curve. Based on the work of Rhodes et al. [23], the spinning FSP bit refines the original grain structure down to the ∼50 nm scale, and the new recrystallized grain nuclei that form from this structure are at sizes between 25 and 40 nm. Thus, in aluminum alloys, it is unlikely that a grain size less than 50 nm can be obtained through the plastic deformation imposed by FSP, and this serves as the minimum size from which grain growth proceeds. It is possible to tailor the conditions for SFSP to produce a cooling response fast enough to limit grain growth, yielding grain structures in the nanometer scale. Fig. 23 shows the actual cooling curve from a SFSP test (adjusted to represent the surface temperature) that produced ∼200 nm scale grains, and a calculated cooling curve needed to obtain a total grain growth of 50 nm. Fig. 23 demonstrates that if the feed rate is increased and

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Fig. 21. Derby–Ashby model [5] for grain growth applied to two FSP experiments where the plunging action was completed.

the water temperature is decreased, the grain growth can be limited to 50 nm. Fig. 24 shows the difference between the observed grain growth in an actual SFSP sample and a calculated grain growth needed for 50 nm. The models and the cooling curves imply that nanometer scale grains should be achievable through SFSP. However, at present, this has not been demonstrated with SFSP in aluminum due to continued grain growth in the stirred material, between the completion of the stirring process and the preparation and examination of the TEM samples. In other metals with slower recrystallization kinetics, the SFSP methods

Fig. 23. By increasing the slope of the cooling curve and decreasing the water temperature in a SFSP test, the Derby–Ashby model [5] predicts a grain size less than 50 nm.

Fig. 24. The difference between the observed grain growth in a SFSP sample and a theoretical grain growth of approximately 50 nm.

should yield nanometer scale grains, and additional experiments to demonstrate this are planned. References

Fig. 22. Derby–Ashby model [5] for grain growth applied to a SFSP experiment where the plunging action was done prior to the test.

[1] H. Schmidt, J. Hattel, J. Wert, Model. Stimul. Mater. Sci. Eng. 12 (2004) 143–157. [2] M. Russel, H. Shercliff, Proceedings of the First International Symposium on Friction Stir Welding, Thousand Oaks, CA, 1999. [3] M. Song, R. Kovacevic, Int. J. Mach. Tools Manuf. 43 (2003) 605–615.

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