Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks

Journal of Ocean Engineering and Technology.
2014.
Oct,
28(5):
452-465

- Received : September 29, 2014
- Accepted : October 24, 2014
- Published : October 31, 2014

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The application of friction stir welding (FSW) technology has been extended to all industries, including shipbuilding. A heat transfer analysis evaluates the weldability of a welded work piece, and elasto-plastic analysis predicts the residual stress and deformation after welding. A thermal elasto-plastic analysis based on the heat transfer analysis results is most frequently used today. However, its application to large objects such as offshore structures and hulls is impractical owing to its long computational time. This paper proposes a new method, namely an equivalent strain method using the inherent strain, to overcome the disadvantages of the extended analysis time. In the present study, a residual stress analysis of FSW was performed using this equivalent strain method. Additionally, in order to reflect the external constraints in FSW, the reaction force was predicted using a neural network, Finally, the approach was verified by comparing the experimental results and thermal elasto-plastic analysis results for the calculated residual stress distribution.
Chemical composition of aluminum alloy 6061-T6
To calculate the temperature field, material nonlinear and thermal conductivity. These material properties are plotted in
Fig. 1
(
Chao and Qi, 1998
).
Temperature dependent material properties for heat transfer analysis
The temperature dependent yield stress, Young's modulus and coefficient of thermal expansion are applied to the structural analysis as shown in
Fig. 2
(
Chao and Qi, 1998
).
Temperature dependent material properties for structural analysis
The work piece and the tool dimensions are listed in
Table 2
, and the welding parameters are provided in
Table 3
. The specified thickness 6mm in
Table 3
for aluminum alloy 6061-T6 has a good weldability during FSW.
Work piece and tool dimensions for experiments
Welding parameters of experiments
A detailed experimental schematic diagram is shown in
Fig. 3
.
Configuration of butt FSW experiment
After finishing the FSW, the residual stress is measured by X-ray diffraction methods. The X-ray diffraction equipment is the XSTRESS3000 model(
Stresstech Group, 2007
) as shown in
Fig. 4
.
X-ray diffraction equipment for measurement of residual stress
In order to investigate the asymmetric effect of FSW, the residual stress is measured at the advancing and retreating sides. The measured line is perpendicular to the weld centerline, which is located 100 mm far from the starting point of welding in the y-direction. The measured points of residual stresses are located at 0 - 20 mm with 1 mm intervals, 20 - 50 mm with 5 mm intervals, and 50 - 100 mm with 10 mm intervals relative to the weld centerline. Also, the residual stress is measured in the longitudinal and lateral directions of the weld.
where
ε
is total strain,
ε
_{e}
is elastic strain,
ε
_{p}
is plastic strain,
ε
_{th }
is thermal strain,
ε
_{pt}
is phase transformation strain, and
ε
^{*}
is inherent strain.
Restraining elements for the each load direction
Three dimension solid-spring model is assumed to be axis symmetric. Because coefficients of thermal expansion has the same values in all directions. To prevent a rigid body motion without disturbing the intended structural behavior, one side of each axis is fixed, and the other side is allowed to move along the axis as shown in
Fig. 6
.
Boundary condition of the core element
The elastic modulus of the periphery element to induce the temperature changes and restraint is determined through the following process(
Kim, 2014
). The elastic modulus and stresses of core and periphery elements are defined as depicted in
Fig. 7
. Where core element is subjected to restraint by the periphery elements. The degree of restraint is Eq. (3):
Definition of elastic modulus and stresses
where
k
is degree of restraint,
ε
_{0}
is strain of a core element without restraint, and
ε
′ is strain of a core element subjected to restraint by the periphery.
The unit stress 𝜎
_{0}
is applied to the model, the unknown stresses are defined such as 𝜎
_{x2}
, 𝜎
_{x3}
, 𝜎
_{y1}
, ⋯, 𝜎
_{z2}
. Thus the equation of equilibrium is Eq. (4):
where 𝜎
_{x}
, 𝜎
_{y}
, 𝜎
_{z}
are stresses of a core element and 𝜎
_{x2}
, 𝜎
_{x3}
, 𝜎
_{y1}
, ⋯, 𝜎
_{z2}
are stresses of periphery elements. The indices 1, 2 and 3 in the subscript indicate the positions of restraining elements that are attached perpendicular to the x, y, and z-axes, respectively. The characters x, y, and z represent the direction of stress of each element. Strain in a triaxial stress state is defined as Eq. (5) by Hooke's law.
Eq. (5) can be expressed as Eq. (6) in a free body state. Each of the axial stresses has the same value.
Eq.(7) can be expressed by using Eqs.(3), (5), and (6).
Since the strain of the core element is the same as those of periphery elements, as explained by the definition of the restraint model, the following equations are derived.
The elastic modulus of the periphery element is calculated follows by Eqs.(7)-(8).
where
TG
; there are three temperature gradients, namely
TG
_{x}
,
TG
_{y}
, and
TG
_{z}
, where each represents the temperature gradients of the respective directions.
TG
is a value that is greater than 0 and less than or equal to 1. For example, if there is no temperature gradient, then
TG
= 1 . The temperature gradient is in inverse proportion to the
TG
value(
Kim, 2014
).
Kim(2014)
assumed that
TG
_{y}
is 1, because the temperature gradient of the welding direction (
TG
_{y}
) will be almost zero. Also, the temperature gradient of the plate thickness direction (
TG
_{z}
) is almost 1 in FSW.
Fig. 8
shows the sectional temperature distribution of FSW (the gray color indicates temperature over 300℃).
Sectional temperature distribution of FSW
There is almost no temperature gradient between the top and bottom surfaces (during FSW, heat is applied not only to the top surface but to the bottom surface). Thus, there is almost no angular distortion due to the temperature gradient of the top and bottom surfaces. The work piece expands laterally during heating, and then contracts during cooling. Thus,
TG
_{z}
was assumed to be 0.98 in this study.
In the previous study by
Kim(2014)
, the value of the external constraint was not defined. In the present study, the value of the external constraint is defined as the distance from the weld centerline, as shown in
Fig. 9
.
External constraint and quantification value
Generally, the external constraint is applied until the work piece temperature reaches room temperature. It prevents movement of the work piece and welding deformation. At this time, reaction force arises in the external constraint. Thus, inherent strain is calculated for the existing external constraint in the heating and cooling stages. In the vicinity of the joint region, an x-direction compressive and tensile loads are added during the heating and cooling stage of FSW, respectively. After the temperature of the work piece reaches room temperature, the x-direction load is removed. Residual strain at this stage is the final inherent strain.
The reaction force is different during heating and cooling. Generally, it greatly affects inherent strain during the cooling stage. However, there is a serious problem in calculating reaction force, because it is calculated by thermal elasto-plastic analysis. Thus, an alternative method for prediction of reaction force is required. Before predicting the reaction force however, its effect on the residual stress distribution during heating and cooling is determined by thermal elasto-plastic analysis.
Finite element model for structural analysis
Steady state heat transfer analysis is carried out in Eulerian description, and then the results are transferred to the FE model, for elasto-plastic analysis. Transient heat transfer analysis is performed in the FE model, and the temperature is assumed as quasi-steady state during the welding process. At this time, the temperature data is extracted in heat source region with 1 mm interval, and transferred to the FE model nodes. The definition of heat source region in CFD model is referred to previous literature(
Kang et al., 2014
)
The relevant welding conditions and parameters are listed in
Table. 4
. The most serious three cases (No. 1, No. 25, and No. 47) were chose to compare differences of the residual stress distribution between thermal elasto-plastic analysis and the inherent strain method. The case of No. 1 shows the largest reaction force difference between heating and cooling. The cases of No. 25 and No. 47 shows small and large reaction forces, respectively.
Welding conditions and parameters for thermal elasto-plastic analysis
The reaction forces during heating and cooling in the No. 1, No. 25, and No. 47 cases are plotted in
Fig. 11
.
Reaction forces in three different cases
In this study, three different methods are employed to calculate inherent strain. The reaction force during heating and cooling is assumed as
A
(negative value), and
B
(positive value), respectively. CASE III corresponded to actual situation, whereas CASE I and CASE II were used to represent values A and B, respectively, in the inherent strain calculation. Three different inherent strain values are calculated using the three different methods, as shown in
Fig. 12
. These inherent strain values are then inputted into an FE model.
Effect of reaction force during heating and cooling on residual stress distribution
Maximum residual stress locations at advancing side of top surface for No. 1 - 10
Residual stress distribution in y-direction
Actually, the
TG
_{x }
value is different in each of the nodes, as shown in
Fig. 14
. However, the difference is slight, hence a representative value could be used. Thus, in the 300℃ exceedence regions, the representative value of
TG
_{x }
is used to calculate the inherent strain. Furthermore, the inherent strain value is also different in each node, because the maximum temperature is different in each node. However, this difference little affects the inherent strain value. Thus, in the present study, we apply a representative value of
TG
_{x }
using the maximum temperature and 300℃, as well as a representative value of inherent strain and the maximum temperature. Additionally, external constraint is considered in calculating the inherent strain. Also, a structural analysis is carried out using the calculated inherent strain, which is used as input data in the location of 300℃.
Representative value of TG _{x } and maximum temperature used to replace each node
In the previous study by
Kim(2014)
, the welding analysis model was divided into two regions, that is, the 1st and 2nd heat-affected zones, depending on the level of the temperature variation. Then, the
TG
was determined according to the average temperature gradient of each region, as shown in
Fig. 15(a)
. The inherent strain value is assigned along twice of leg length, and was applied in conventional fusion welding. However, this method cannot adequately reflect the characteristics of FSW. In this study then,
TG
_{x }
is defined as 300℃ divided by the maximum temperature at the top and bottom surfaces, as shown in
Fig. 15(b)
.
TG
_{z }
is defined as 0.98.
Calculation of TG _{x } in conventional fusion welding and friction stir welding
Inherent strain is calculated using the maximum temperature,
TG
, and the reaction force. This value is inputted in the location of 300℃. The inherent strain value is not defined for the region between 300℃ on the advancing side and 300℃ on the retreating side. During FSW, there is an empty space occurred in work piece, which is replaced by the tool. This empty space is not suitable for inherent strain method. Thus, the tool region is difficult to define. The temperature and residual stress distribution is in an 'M'-shape, as shown in
Fig. 16
.
Temperature and residual stress in y-direction distribution
The ratio between the center and edge of the tool is defined as α, as shown in
Fig. 16(a)
, and is multiplied by the inherent strain. This fixed inherent strain is defined as
ε
^{*′}
, and is inputted in the center of the FE model. The other nodes, located between the center and 300℃, are linearly interpolated as shown in
Fig. 17
.
Interpolation of inherent strain value in center region
Residual stress distributions according to different reaction forces
In
Fig. 18
, the inherent strain during cooling is more important than the heating process. The CASE II and CASE III results are almost the same, which means that the inherent strain is produced during cooling. This is why the yield stress during the cooling stage is higher. Finally, we decide to use of the reaction force during the cooling stage as representative one.
Factors affecting reaction force
In order to quantify the temperature gradient, it is approximated using a third-order polynomial function. However, in consideration of the maximum temperature, it is difficult to approximate temperature gradient data by a trend line. Maximum temperature is already considered as a factor in calculating the reaction force using neural networks. Thus, maximum temperature is no more necessary in the temperature distribution graph. Taking the natural logarithm on the temperature distribution data, a shown in
Fig. 20
, the slope of the graph changed less stiff.
Temperature distribution of No. 1
Structure of neural networks for prediction of reaction force during cooling stage
In the figure, the number of nodes in the input layer, in the output layer, and in the hidden layer are 7, 1, and 9, respectively. In general, the number of nodes in the hidden layer is equal to or greater than the sum of the nodes in input and output layers(
Priddy and Keller, 2005
).
A thermal elasto-plastic analysis are carried out for 61 cases in total in order to make a neural networks function. The relevant welding parameters are summarized in
Table 4
. Then, the input data and output data are arranged as shown in
Fig. 22
.
Input and target databases for derivation of neural networks function
Verification of neural networks
Comparison of thermal elasto-plastic analysis and neural networks results for reaction force during cooling stage
As shown in
Table 6
, the prediction of the reaction forces during the cooling stage using the neural networks is suitable.
Structure of neural networks function for reaction force during cooling
The weight and bias matrices for reaction force during the cooling stage are listed in
Table. 7
.
Weight and bias matrices for reaction force during cooling
When using this neural networks function, the input and output data have to be normalized.
Table 8
lists the normalized minimum and maximum values of input data used. Most of the general FSW experimental conditions are included in the table.
Minimum and maximum values of input data for 61 databases according to thermal elasto-plastic analysis results
However, the new input and output data should be generated for the new FSW experimental conditions, it is alternatively possible to utilize
Fig. 24
,
Table 7
and
Table 8
to predict the reaction force during cooling, when we have no data from thermal elasto-plastic analysis.
TG
_{x}
,
TG
_{y}
, and
TG
_{z}
. As mentioned before,
TG
_{y}
is 1, and
TG
_{z}
is almost 1, as shown in
Fig. 8
. In this study,
TG
_{z}
is assumed to be 0.98. The maximum temperature is increased from 300℃ to 600℃ with 50℃ intervals. The melting point of aluminum alloy 6061-T6 is about 580℃. When the maximum temperature is not reached with 300℃, the weldability is bad.The temperature gradient of the x-direction (
TG
_{z}
) is 0.5 to 1.0 with 0.1 intervals. The compressive load during heating and the tensile load during cooling in the x-direction were both 0 N to 3000 N at 100 N intervals. The example of inherent strain charts is depicted in
Fig. 25
.
Example of inherent strain charts
Comparison of residual stress distributions in y-direction
In
Fig. 26 (d)
, the experimental residual stress data is obtained from a previous study(
Feng et al., 2004
). According to the results, it is possible to verify the position and value of the residual stress distribution in the y-direction when compared with experimental data, thermal elasto-plastic analysis, and inherent strain method using neural networks are almost identical. Additionally, the alpha was introduced to calculate the residual stress distribution in the y-direction of the featured 'M-shape'. Roughly, the inherent strain method using neural networks and the experimental results are consistent in the vicinity of the weld. However, there is a limitation to the prediction of the residual stress in the peripheral region of the tool by the introduction of alpha. This is why in the weld, empty space occurs in work piece when the tool has passed over to the stirring. Furthermore, downward force is applied to the base material during FSW. Therefore, some error is incurred when comparing the analysis results with the experimental results. Nevertheless, the experimental residual stress distribution y-direction results and other simulation results show good agreement in each case. Therefore, it is verified that the proposed FSW residual stress analysis based on the inherent strain method using neural networks is a reasonable method.
Using this method, the maximum residual stress values in the y-direction in 61 cases in
Table 4
are compared in
Table 9
.
Maximum residual stress in y-direction
In
Table 9
, 52 cases have an error of 10% or less, 8 cases have an error of 10 - 20%, and only 1 case had an error of over 20%. In the case of the over-20% error, the maximum tensile residual stress is located at about 250℃. However, for consistency, it is considered that the inherent strain should be inputted in the 300℃ position. For this reason, a large error occur between the thermal elasto-plastic analysis and the inherent strain method. Overall though, the residual stress results of the elasto-plastic analysis and inherent strain method show fairly good agreement.
TG
), and reaction force during cooling stage. The temperature gradient in the y and z-directions is assumed to be 1.0 and 0.98, respectively. The factors affecting reaction force during cooling are maximum temperature, temperature gradient, thickness of work piece, and external constraint. The temperature gradient is approximated using a third-order polynomial function. Subsequently, the reaction force during cooling stage value is predicted using a trained neural networks function. The computational times of the structural analysis of the inherent strain method using neural networks and thermal elasto-plastic analysis are 10 minutes and 8 hours, respectively. Thus, computational time for a huge structure can be saved.
Finally, the experimental and thermal elasto-plastic analysis results are compared with that of the inherent strain method. Significantly, the residual stress distribution show fairly good agreement.

Friction stir welding
;
Inherent strain
;
Equivalent strain method
;
Residual stress
;
External constraint
;
Neural networks

1. Introduction

Friction stir welding (FSW) was invented in 1991 at The Welding Institute, and nowadays is widely utilized in the shipbuilding, aerospace, rolling stock, automobile, construction, electrical, machinery and equipment industries(
Thomas et al., 1991
;
Thomas et al., 1993
;
ESAB, 2011
). FSW has many advantages, a discussion of which is available in the literature(
Kang and Jang, 2014
). With the extension of the application of the FSW technique across multiple industries, several problems have begun to arise. One of them is related to residual stress. FSW being solid-state welding, the heat input is small; thus, compared with conventional welding, residual stress and welding deformation are also small. Since angular distortion is negligible, the temperature gradient of the upper and lower surfaces, correspondingly, is small as well. Only transverse shrinkage occurs, but this is also small. However, unlike the case of welding deformation, residual stress is not negligible, because the weld zone temperature is about 80-90% of the FSW melting point. In the relevant previous studies, residual stresses were measured at about 60-98.5% of the yield stress(
Feng et al., 2004
;
Han et al., 2011
;
Steuwer et al., 2006
;
Lemmen et al., 2010
). Additionally, the problems related to residual stress were reported in other studies.(
Chen and Kovacevic, 2006
;
Chuan and Xiang, 2013
;
Kumar et al., 2013
). For prediction and control of welding deformation and residual stress, the thermal elasto-plastic analysis method is widely employed. The best advantage of this method is the accuracy of its result. However, it requires huge computational time as the number of elements increases, owing to its non-linear analysis.
In order to overcome the problem of huge computational time, several simplified assessment techniques such as the equivalent load method and the equivalent strain method, based on inherent strain, have been proposed since the 1980s. The equivalent load method(
Kim et al., 2012
;
Lee, 2010
;
Ha et al., 2007
;
Kim and Jang, 2003
) and the equivalent strain method(
Kim, 2010
;
Kim, 2014
) have been mainly applied to steel. Using the equivalent load method,
Jang and Jang(2010)
and
Mun and Seo(2013)
carried out FSW deformation analyses. However, as noted above, the welding deformation is negligible, whereas the residual stress is significant. In the present study, FSW residual stress is calculated using the equivalent strain method and compared with thermal elasto-plastic analysis and experimental results. In FSW, a reaction force is generated by external constraint, which is one of the important input to inherent strain method. The reaction force can be calculated through elasto-plastic analysis, but normally it costs too much computation time and dose not satisfy the original purpose of inherent strain method. Therefore, this study proposes another simple method to predict the reaction force. In the present study, reaction force is predicted using neural networks(
Priddy and Keller, 2005
). Neural networks solve problems in a manner similar to the human brain: To process a signal, neurons are connected to the neuronal tissue that is part of the basic structure of the brain; likewise, a neural network is a mathematical model of neuronal connections. In order to distinguish this mathematical neural network from the natural biological one in the brain, it is referred to as an artificial neural network. This, as an interconnected network of simple processing elements, is a powerful data modeling tool for capturing and representing complex input and output relationships. Finally in the present study, the reaction force is predicted, and its effect in the unit inherent strain analysis model is considered.
This paper unfolds as follows. In section 2, details of the experiment condition are explained. Section 3 provides the equivalent strain method using inherent strain procedure. In section 4, detailed prediction of reaction force using neural networks is provided. Section 5 provides a comparative study results to verify the proposed method. Conclusion is laid in section 6.
2. Experiment

In this study, butt FSW experiments are carried out using aluminum alloy 6061-T6. The chemical constituents of this alloy are listed in
Table 1
.
Chemical composition of aluminum alloy 6061-T6

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Work piece and tool dimensions for experiments

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Welding parameters of experiments

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3. Equivalent strain method using inherent strain

- 3.1 Definition of inherent strain

During the welding process, the total strain is divided into four kinds of strains: elastic strain, plastic strain, thermal strain, and phase transformation strain. The inherent strain is the difference between the total strain and the elastic strain. Thus, it can be expressed as the following Eqs. (1)-(2).
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- 3.2 Three dimension solid-spring model

A weld joint region expands or shrinks due to welding process. At this time, periphery is also effected by weld joint region. Mainly restraining elements against the expansion of core element in each of three directions are depicted in
Fig. 5
.
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- 3.3 Factors affecting inherent strain

Inherent strain is affected by several factors including maximum temperature, temperature gradient in three directions, restraint, external constraint, etc.
Kim(2010)
considered maximum temperature and restraint in calculating inherent strain, whereas
Kim(2014)
suggested that maximum temperature, temperature gradient in three directions, and external constraint are the most important factors in calculating inherent strain. Restraint has a small effect on inherent strain, whereas temperature gradient in three directions and external constraint have large effects(
Kim, 2014
).
A heat transfer analysis is carried out using Fluent(
Fluent Inc., 2006
), a commercial computational fluid dynamics program. After the heat transfer analysis, the maximum temperature and temperature gradient are calculated. The temperature gradient is represented by an index named
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- 3.4 Parametric study for three different cases of reaction force using thermal elasto-plastic analysis result

61 times different thermal elasto-plastic analyses are carried out in total using ANSYS (
ANSYS Inc., 2011
). The work piece and the tool dimensions are listed in
Table 2
and finite element(FE) model is shown in
Fig. 10
.
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Welding conditions and parameters for thermal elasto-plastic analysis

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- 3.5 Location of inherent strain input

In
Table 5
, the locations of maximum tension residual stress in the y-direction are presented for No.1 – No.10 cases. As a result, the location of maximum tension residual stresses are found near 300℃, as indicated in
Fig. 13
and
Table 5
.
Maximum residual stress locations at advancing side of top surface for No. 1 - 10

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- 3.6 Residual stress distribution according to reaction force in thermal elasto-plastic analysis

The residual stress distribution is calculated by elastic structural analysis with the inherent strains for No. 1, No. 25, and No. 47. The maximum tensile residual stresses in the y-direction are compared in
Fig. 18
.
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4. Prediction of reaction force using neural networks

- 4.1 Factors affecting reaction force

In this study, welding speed, rotation speed of tool, and tool dimensions are represented by the maximum temperature and temperature gradient. Thus, the input data can be summarized as shown in
Fig. 19
.
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- 4.2 Structure of neural networks

The neural network structure is used to predict the reaction force during the cooling stage, as shown in
Fig. 21
.
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- 4.3 Verification of neural networks function

Of the databases constructed, 55 cases are randomly selected for training of the neural network. Then, using the data of the remaining six cases, the function of the neural networks is verified. This procedure is repeated five times, as shown in
Fig. 23
. Finally, it is confirmed that the input valueis appropriate for prediction of the reaction force during the cooling stage using the neural networks. The verified results are listed in
Table 6
.
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Comparison of thermal elasto-plastic analysis and neural networks results for reaction force during cooling stage

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- 4.4 Neural networks function

In order to implement the neural networks, 'Neural Network Toolbox in MATLAB' is used(
Demuth and Beale, 1998
). After completing the verification of effectiveness of the neural networks, the final neural network function is trained using the 61 databases.
Maximum temperature, thickness, external constraint, and temperature gradient (coefficients of third-order polynomial expression) are needed for input data. Reaction force during cooling (output data) is predicted by using the neural networks function shown below. Thus, thermal elasto-plastic analysis can be replaced by neural network prediction of reaction force. The final neural networks structure is shown in
Fig. 24
.
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Weight and bias matrices for reaction force during cooling

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Minimum and maximum values of input data for 61 databases according to thermal elasto-plastic analysis results

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- 4.5 Inherent strain charts

Inherent strain charts considering external constraint are calculated using maximum temperature, temperature gradient, and reaction force. The temperature gradient is divided into
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5. Analysis results and discussion

Four cases of the calculated residual stress in y-direction are compared in
Fig. 26
. with different ways, i.e. experimental data, thermal elasto-plastic analysis, and inherent strain method using the neural networks, respectively.
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Maximum residual stress in y-direction

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6. Conclusions

In the present study, an FSW analysis based on the equivalent strain method to cover external constraint is carried out according to the computational fluid dynamics analysis result.
The factors affecting inherent strain values are maximum temperature, temperature gradient (
Acknowledgements

This research is supported by the New & Renewable Energy of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 20123010020090)

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Citing 'Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks
'

@article{ HOGHC7_2014_v28n5_452}
,title={Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks}
,volume={5}
, url={http://dx.doi.org/10.5574/KSOE.2014.28.5.452}, DOI={10.5574/KSOE.2014.28.5.452}
, number= {5}
, journal={Journal of Ocean Engineering and Technology}
, publisher={Korean Society of Ocean Engineers}
, author={Kang, Sung-Wook
and
Jang, Beom-Seon}
, year={2014}
, month={Oct}