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J. Mater. Sci. Technol.  2019, Vol. 35 Issue (4): 491-498    DOI: 10.1016/j.jmst.2018.10.021
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Predicting recrystallized grain size in friction stir processed 304L stainless steel
M.P. Milesa*(), T.W. Nelsona, C. Guntera, F.C. Liua, L. Fourmentb, T. Mathisa
a Manufacturing Engineering Department, Brigham Young University, Provo, UT 84663, USA
b Centre de Mise en Forme des Materiaux, Mines ParisTech, 06904 Sophia-Antipolis, France
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A major dilemma faced in the nuclear industry is repair of stainless steel reactor components that have been exposed to neutron irradiation. When conventional fusion welding is used for repair, intergranular cracks develop in the heat-affected zone (HAZ). Friction stir processing (FSP), which operates at much lower peak temperatures than fusion welding, was studied as a crack repair method for irradiated 304L stainless steel. A numerical simulation of the FSP process in 304L was developed to predict temperatures and recrystallized grain size in the stir zone. The model employed an Eulerian finite element approach, where flow stresses for a large range of strain rates and temperatures inherent in FSP were used as input. Temperature predictions in three locations near the stir zone were accurate to within 4%, while prediction of welding power was accurate to within 5% of experimental measurements. The predicted recrystallized grain sizes ranged from 7.6 to 10.6 μm, while the experimentally measured grains sizes in the same locations ranged from 6.0 to 7.6 μm. The maximum error in predicted recrystallized grain size was about 39%, but the associated stir zone hardness from the predicted grain sizes was only different from the experiment by about 10%.

Key words:  Stainless steel      Numerical simulation      Friction stir welding      Recrystallized grain size     
Received:  01 May 2018     
Corresponding Authors:  Miles M.P.     E-mail:

Cite this article: 

M.P. Miles, T.W. Nelson, C. Gunter, F.C. Liu, L. Fourment, T. Mathis. Predicting recrystallized grain size in friction stir processed 304L stainless steel. J. Mater. Sci. Technol., 2019, 35(4): 491-498.

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Fig. 1.  304L stainless steel microstructure for as-received plate.
C Mn P S Si Cr Ni N Fe
0.08 2.00 0.045 0.030 0.75 18-20 8-12 0.10 Balance
Table 1  Composition of 304L stainless steel (wt%).
Fig. 2.  12 mm thick 304L plate with thermocouples embedded on one side of the intended stir zone path.
Fig. 3.  Flow stresses for 304L stainless steel as a function of strain and strain rate, at 900 °C (data provided by JMatPro [45]).
Fig. 4.  P1+/P1 element with piecewise, linear interpolation of both velocity and pressure.
Fig. 5.  FSP model with tool (green), tool holder (red disk), 304L plate (red), and carbon steel backing plate (yellow).
Tool 304L plate Backing plate
Density 3.12e-06 g/mm3 7.85e-06 g/mm3 7.85e-06 g/mm3
Heat Capacity 1.97e + 09 mm2/s2-K 7.78e + 08 mm2/s2-K 7.78e + 08 mm2/s2-K
Conductivity 1.3e + 05 W/mm2.K 28000 W/mm2.K 59000 W/mm2.K
Table 2  Data for thermal computation in FSP model.
Fig. 6.  (a) Top view of plate at steady-state, and (b) section view through center of tool at steady-state. The temperature scale is degrees Celsius.
Fig. 7.  Comparison of predicted and experimentally (dotted line) measured temperatures at steady state. Friction coefficients of 0.125 and 0.15 provided the best predictions.
Fig. 8.  Comparison of experimental and simulated temperatures taken from thermocouples and the FEA model respectively. Distances from the weld centerline are (a) 7 mm, (b) 9 mm, and (c) 11 mm.
Fig. 9.  Schematic showing three locations behind tool where grain sizes where measured after processing experiment. Each location was 800 μm behind the pin hole and 2 mm beneath the welding surface. Locations 1 and 3 were both at a 30° angle from the horizontal.
Fig. 10.  EBSD grain maps from friction stir processed 304L material at the locations (a) 1, (b) 2, and (c) 3 shown in Fig. 9.
Position Predicted temperature(°C) Predicted
strain rate (s-1)
Predicted grain size (μm) Measured grain size (μm) Error (%)
1 894 0.67 8.0 6.4 25
2 856 0.10 10.6 7.6 39
3 864 0.37 7.6 6.0 27
Table 3  Model predictions of grain size versus experiment.
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