METHOD FOR CHARACTERIZATION OF TEMPERATURE IN WELD ZONE OF FRICTION STIR WELDING BASED ON INFRARED THERMAL IMAGER

Abstract

The present invention belongs to the field of friction stir welding (FSW) temperature detection, and relates to a temperature characterization method of FSW weld zone based on infrared thermal imager. The invention combines theory with experiment. A temperature field simulation model of FSW is established based on DEFORM. The data sets of temperature of surface feature points, the maximum and minimum temperatures in weld zone are obtained according to the simulation model result. Then, Support Vector Regression (SVR) is used to establish a temperature characterization model, which represents the correlation between the temperature of surface feature points and the maximum and minimum temperatures in weld zone. In the actual welding process, an infrared camera is used to measure the temperature of the surface feature point in real-time. Combined with the built temperature characterization model, the characterization of the maximum and minimum temperatures in weld zone is achieved.

Claims

1. A method for temperature characterization of weld zone in friction stir welding (FSW) based on infrared thermal imager, the characteristic is, comprising the following steps: step 1. a temperature field simulation model of FSW is established; step 2. the data sets of temperature of surface feature points, the maximum and minimum temperatures in weld zone are extracted according to the simulation model result; step 3. to design support vector regression machine algorithm model; the usual starting point is a sample dataset (x.sub.1, y.sub.1), . . . , (x.sub.N, y.sub.N), where x.sub.i (i=1, . . . , N) is the surface characteristic point temperature of the weldment, which is a one-dimensional input; y.sub.i (i=1, . . . , N) is the weld zone temperature including the maximum temperature and the minimum temperature, which is a one-dimensional target; the basic idea of SVR is to map the data in the original input space into a high-dimensional feature space through a nonlinear transformation φ(x); in this high-dimensional feature space, the ε-insensitive loss function is used for linear regression to obtain the nonlinear regression relationship between the target quantity and the input quantity in the original space; the relationship between the surface temperature of weldment and the temperature of weld zone is obtained; the optimal linear function constructed in high dimensional feature space:
f(x)=w.sup.T*ϕ(x)+b  (1) where w is the weight vector, b is the bias term; the ε-insensitive loss function is defined as: $\begin{array}{cc}{L}_{\epsilon }\left(z\right)=\{\begin{array}{cc}0,& .Math.z.Math.\le \epsilon \\ .Math.z.Math.-\epsilon ,& \mathrm{otherwi}se\end{array}& \left(2\right)\end{array}$ the objective function of SVR optimization is then as follows: $\begin{array}{cc}\begin{array}{c}\min \left\{\frac{1}{2}{.Math.w.Math.}^2+C\underset{i=1}{\overset{N}{.Math.}}{L}_{\epsilon }\left(f\left(x_i\right)-y_i\right)\right\}\\ s.t.y_i-\left(w^T.Math.\phi \left(x_i\right)+b\right)\le \epsilon +{\xi }_i^{*}\\ w^T.Math.\phi \left(x_i\right)+b-y_i\le \epsilon +{\xi }_i\end{array}& \left(3\right)\end{array}$ where C>0 is the penalty coefficient, considering the possible errors and introducing two slack variables ξ.sub.i, ξ*.sub.i, the optimization function is then as follows: $\begin{array}{cc}\begin{array}{c}\min \left\{\frac{1}{2}{.Math.w.Math.}^2+C\underset{i=1}{\overset{N}{.Math.}}\left({\xi }_i+{\xi }_i^{*}\right)\right\}\\ s.t.y_i-\left(w^T.Math.\phi \left(x_i\right)+b\right)\le \epsilon +{\xi }_i^{*}\\ w^T.Math.\phi \left(x_i\right)+b-y_i\le \epsilon +{\xi }_i\\ {\xi }_i,{\xi }_i^{*}\ge 0\end{array}& \left(4\right)\end{array}$ to solve formula (4), the Lagrange function is introduced: $\begin{array}{cc}L\left(w,b;\alpha ,{\alpha }^{*}\right)=\frac{1}{2}{.Math.w.Math.}^2-\underset{i=1}{\overset{N}{.Math.}}{\alpha }_i\left(\epsilon +y_i-\left(w.Math.\phi \left(x_i\right)+b\right)\right)-\underset{i=1}{\overset{N}{.Math.}}{\alpha }_i^{*}\left(\epsilon -y_i+\left(w.Math.\phi \left(x_i\right)+b\right)\right)& \left(5\right)\end{array}$ where, α=[α.sub.1, . . . , α.sub.N].sup.T≥0, α*=[α*.sub.1 . . . , α*.sub.N].sup.T≥0 is the Lagrange multiplier; taking partial differential of w and b respectively and making them equal to 0, the dual problem of Equation (5) is: $\begin{array}{cc}\begin{array}{c}\min \frac{1}{2}\underset{i,j=1}{\overset{N}{.Math.}}\left({\alpha }_i-{\alpha }_i^{*}\right)\left({\alpha }_j-{\alpha }_j^{*}\right)k\left(x_i,x_j\right)+\epsilon \underset{i=1}{\overset{N}{.Math.}}\left({\alpha }_i+{\alpha }_i^{*}\right)-\underset{i=1}{\overset{N}{.Math.}}{y}_i\left({\alpha }_i-{\alpha }_i^{*}\right)\\ s.t.\underset{i=1}{\overset{N}{.Math.}}\left({\alpha }_i-{\alpha }_i^{*}\right)=0\\ 0\le {\alpha }_i^{*}\le C,0\le {\alpha }_i\le C,i=1,.Math.,N\end{array}& \left(6\right)\end{array}$ where k(x.sub.i,x.sub.j) is the kernel function, RBF kernel function is selected, which has the advantages of good smoothness, radial symmetry and simple parameters; the function is as follows: $\begin{array}{cc}k\left(x_i,x_j\right)=e^{\frac{{.Math.x_i-x_j.Math.}^2}{2{\sigma }^2}}& \left(7\right)\end{array}$ where σ>0 is an adjustable nuclear parameter; solve Equation (6) to obtain a regression estimate of SVR as follows: $\begin{array}{cc}f\left(x\right)=\underset{i,j=1}{\overset{N}{.Math.}}\left({\alpha }_i-{\alpha }_i^{*}\right)k\left(x_i,x_j\right)+b& \left(8\right)\end{array}$ step 4. setting the parameters and kernel function of the support vector regression algorithm model; the divided training and test temperature data sets of the feature point and weld zone are inputted in the model; step 5. taking the root mean square error as the evaluation standard, the support vector regression machine algorithm model in step 3 is trained and tested; step 6. the infrared thermal imager is arranged in front of the FSW machine, and the friction stir welding experiments are carried out; the temperature of the surface feature point on the weldment during the welding is obtained by the infrared thermal imager; taking the temperature of the feature points on the weldment as input to predict the maximum and minimum temperatures of the weld zone during the welding.

Description

DESCRIPTION OF DRAWINGS

[0021] FIG. 1 is a schematic diagram of a three-dimensional simulation model after assembly.

[0022] FIG. 2 is a schematic diagram of temperature-dependent material properties of 2219 aluminum alloy

[0023] FIG. 3 is a schematic diagram of the simulation model after meshing

[0024] FIG. 4 is a schematic diagram of the maximum temperature extraction in the weld zone

[0025] FIG. 5 is a schematic diagram of the advancing side feature point

[0026] FIG. 6 is a schematic diagram of the minimum temperature extraction in the weld zone

[0027] FIG. 7 is a schematic diagram of the retreating side feature point

[0028] FIG. 8 is a schematic diagram of the maximum and minimum temperatures in the weld zone predicted by SVR.

DETAILED DESCRIPTION

[0029] Specific embodiments of the present invention are further described below in combination with the drawings and the technical solutions.

[0030] In this embodiment, 2219 aluminum alloy is used as welding material. 2219 aluminum alloy has good high temperature mechanical properties, welding performance and stress corrosion resistance, and is widely used in aerospace field.

[0031] The data sets of temperature of surface feature points, the maximum and minimum temperatures in weld zone are obtained according to the simulation model result. Then, Support Vector Regression (SVR) is used to establish a correlation between the temperature of surface feature points and the maximum and minimum temperatures in weld zone. Combined with surface temperature of FSW weldments based on infrared camera, the characterization of the maximum and minimum temperatures in weld zone is achieved.

[0032] Step 1. Establishment of the temperature field simulation model of FSW

[0033] Geometrical Model: SolidWorks was used to establish the model of weldment and tool and imported into DEFORM software for assembly. The parameters of the tool are shown in the following table 1. The welding material is 2219 aluminum alloy and the size is 100 mm×150 mm×18 mm. The three-dimensional simulation model after assembly in DEFORM is shown in FIG. 1.

TABLE-US-00001 TABLE 1 Parameters of the tool Radius of the Shoulder Stir pin root Stir pin top Length of shoulder concave angle diameter diameter stir pin 32 mm 4° 15 mm 7 mm 17.8 mm

[0034] Material Properties: The chemical composition of 2219 aluminum alloy is shown in Table 2. The temperature-dependent material properties of 2219 aluminum alloy are obtained by utilizing JmatPro software, as shown in FIG. 2.

TABLE-US-00002 TABLE 2 Chemical composition of 2219 aluminum alloy Cu Mn Fe Si Zn V Ti Zr Mg Al 6.21 0.29 0.12 0.15 0.06 0.08 0.03 0.12 0.02 Bal

[0035] Johnson-Cook constitutive model is used to describe the effect of material flow stress on temperature and strain rate. Johnson-Cook constitutive equation is written as:

[00008]$\begin{array}{cc}\sigma =\left(A+B{\epsilon }^n\right)\left(1+C\ln {\dot{\epsilon }}^{*}\right)\left(1-{\left(T^{*}\right)}^m\right)& \left(9\right)\end{array}$$\begin{array}{cc}{T}^{*}=\{\begin{array}{cc}0& T<T_{room}\\ \left(\frac{T-T_{room}}{T_{\mathrm{melt}}-T_{room}}\right)& T_{room}\le T\le T_{\mathrm{melt}}\\ 1& T>T_{\mathrm{melt}}\end{array}& \left(10\right)\end{array}$

[0036] Where ε presents the effective plastic strain; {dot over (ε)}* presents the relative plastic strain rate, {dot over (ε)}*={dot over (ε)}/{dot over (ε)}.sub.0; {dot over (ε)} presents the effective plastic strain rate; {dot over (ε)}.sub.0 presents the reference plastic strain rate. T.sub.room is the indoor temperature, T.sub.melt is the melting point of the material. The constants of the constitutive equation of 2219 aluminium alloy are shown in Table 3.

TABLE-US-00003 TABLE 3 Material constants for the Johnson-Cook model A (MPa) B (MPa) n C m 170 228 0.31 0.028 2.75

[0037] The material of the tool is H13 tool steel. The material constitutive equation uses the data in the material library of DEFORM software. Other material parameters are shown in Table 4.

TABLE-US-00004 TABLE 4 material parameters of H13 Thermal Thermal expansion Young's Temperature conductivity Heat capacity Density coefficient modulus (° C.) (N/s*° C.) (N/mm{circumflex over ( )}2*° C.) (Ton/mm{circumflex over ( )}3) (1/° C.) (MPa) 20 25.0 3.6 7.81e−9 1.10 215000 499 27.7 4.2 7.64e−9 1.15 176000 593 30.4 4.5 7.64e−9 1.24 165000

[0038] Boundary conditions: Boundary conditions are divided into mechanical boundary conditions and thermal boundary conditions. In the setting of mechanical boundary conditions, the degree of freedom of movement in the Z direction of the weldment bottom surface is limited, and the degree of freedom of movement in the X and Y directions of the weldment side is limited to avoid the displacement of the weldment during the simulation process. Heat convection coefficient between the bottom surface of the weldment and air is set as 5 N/mm.Math.s.Math.° C., and heat convection coefficient between the remaining surface of the weldment, the surface of the tool, and air is set as 0.025 N/mm.Math.s.Math.° C.

[0039] Frictional mode: During the welding process, the temperature of the contact area between the weldment and tool increases, the surface of the weldment material with lower strength is partially sheared. Under the action of friction, part of the weldment material will be stick to the surface of the tool. To describe the state of the contact area between the weldment and tool during the welding process accurately, a shear friction model that changes with temperature is adopted. The equation is expressed as:

τ=mk  (11)

[0040] Where τ is the contact stress at the interface of the weldment and tool; m is the shear factor; k is the shear strength. The coefficients of friction are temperature-dependent, as shown in Table 5.

TABLE-US-00005 TABLE 5 Temperature-dependent coefficients of friction Temperature (° C.) 25 100 200 300 400 500 m 0.61 0.51 0.21 0.07 0.47 0.01

[0041] Grid division: The choice of mesh type and size is a very important factor affecting the accuracy of finite element analysis. The model needs mesh generation of the tool and weldment. The number of grids affect the simulation calculation time. The contact area between the weldment and the tool is refined, and other areas use coarse grids to reduce the simulation time. The simulation model completed by meshing is shown in FIG. 3.

[0042] Based on the established simulation model, the simulation of the rotation speed of the tool is 500 r/min, the welding speed is 75 mm/min, the pressing speed is 15 mm/min, the tool tilt angle is 2.5°, and the pressing amount is 0.2 mm. The temperature field under this set of welding process parameters is obtained.

[0043] step 2. To extract the data sets of temperature of surface feature point and temperatures in weld zone according to the simulation model result.

[0044] The maximum temperature of the weld zone is distributed below the tool shoulder as shown in FIG. 4. Based on the FSW finite element simulation model established by the above steps, the temperature of the feature point on the advancing side (AS) of the weldment surface and the maximum temperature of the weld zone are extracted. The data sets are shown in Table 6, and the position of the feature point on the AS is shown in FIG. 5. The minimum temperature of the weld zone is distributed at the bottom of the weldment as shown in FIG. 6. The temperature of the feature point on the retreating side (RS) of the weldment and the minimum temperature data sets of the core area are extracted from the simulation model as shown in Table 6. The position of the feature point on the RS is shown in FIG. 7.

TABLE-US-00006 TABLE 6 Temperature of surface feature point and the temperature in wed zone (° C.) No. T.sub.AS T.sub.RS T.sub.max T.sub.min 1 290.8 264.2 493.6 422.5 2 316.8 240.7 503.8 411.5 3 296.9 255.6 496.7 416.8 4 305.3 244.5 498.6 413.3 5 319.0 245.4 503.9 413.8 6 313.0 281.4 500.3 436.9 7 326.0 249.9 506.6 414.4 8 307.4 251.7 497.8 416.4 9 307.0 252.4 498.8 416.2 10 335.2 248.4 508.1 416.0 11 327.8 254.2 506.9 416.5 12 308.9 256.7 499.9 419.0 13 301.2 257.8 496.6 418.1 14 285.4 259.0 490.6 418.6 15 300.5 259.8 497.8 419.5 16 292.3 278.5 494.6 433.3 17 297.6 263.7 496.0 420.5 18 313.5 238.8 501.8 410.0 19 296.2 265.5 495.7 423.1 20 322.1 242.5 504.9 411.9 21 302.8 283.3 497.6 439.8 22 323.3 269.9 505.5 426.4 23 309.8 266.2 499.4 425.2 24 328.6 273.0 505.2 429.1 25 312.0 239.1 500.0 412.7 26 303.2 240.3 497.8 413.6 27 313.6 243.6 501.5 413.4 28 330.9 245.4 507.3 414.1 29 298.5 246.9 496.9 415.8 30 310.3 247.1 500.2 413.8 31 319.7 253.4 504.5 417.3 32 320.8 249.6 505.2 416.5 33 325.2 255.2 506.3 419.8 34 304.9 264.4 498.4 423.9 35 308.0 256.5 498.7 419.9 36 321.1 257.5 503.5 420.8 37 318.1 258.7 502.9 419.8 38 340.6 260.5 510.0 420.3 39 312.4 261.7 500.7 421.2 40 299.2 253.7 497.5 418.8 41 329.8 270.3 507.1 425.7 42 288.9 267.5 493.5 427.0 43 317.0 278.4 502.3 434.7 44 287.5 252.3 492.5 416.8 45 289.6 275.3 492.7 432.1 46 304.3 252.5 497.6 417.3 47 311.6 266.6 501.2 424.2 48 286.7 258.4 491.5 418.5 T.sub.AS represents the temperature of the surface feature point on AS, T.sub.RS represents the temperature of the surface feature point on RS, T.sub.max represents the maximum temperature in weld zone, T.sub.min represents the minimum temperature in weld zone.

[0045] step 3. FSW experiments are carried out, and the temperatures of the weld zone and the surface temperature of the weldment are measured by thermocouple and infrared thermal imager respectively.

[0046] The maximum and minimum temperatures of the weld zone during FSW are obtained by thermocouple to test the characterization accuracy of this method. The K-type thermocouple is selected as the temperature measuring element, and the temperature transmitter performs nonlinear correction on the temperature signal. The PCI-1747U data acquisition card collects the temperature signal and transmits it to the host computer. The thermocouples are arranged at the feature points of 1.5 mm from the upper surface, 115 mm from the weld center, 1.5 mm from the lower surface, and 119 mm from the weld center. The AS and RS are symmetrically arranged.

[0047] In the experiment, a 2219 aluminum alloy welding plate with a size of 300 mm×120 mm×18 mm is used, and a hole was drilled on the side of the welding plate to embed thermocouples. The thermal imager is arranged in front of the weldment, and the angle with the spindle was 30°. The FSW experiment is carried out. The rotation speed of the tool is 500 r/min, the welding speed is 75 mm/min, the pressing speed is 15 mm/min, and the tool tilt angle is 2.5°. In the experiment, the feed is completed by the movement of the workbench. The relative position of the machine tool spindle and the thermal imager remains unchanged. The temperature of the characteristic points on the surface of the weldment during FSW is obtained as follows:

TABLE-US-00007 TABLE 7 Temperature of the surface feature point on AS No. 1 2 3 4 5 6 7 8 9 10 Temp. 303.2 310.6 320.9 314.2 309.4 325.0 313.1 328.2 310.9 321.9

TABLE-US-00008 TABLE 8 Temperature of the surface feature point on RS No. 1 2 3 4 5 6 7 8 9 10 Temp. 255.3 263.8 245.6 273.0 262.2 255.9 267.7 247.8 251.4 277.8

[0048] step 4. The characterization model between the surface temperature and weld zone temperature is established, and the characterization of temperature in weld zone during FSW is realized by combining the surface temperature measured by thermal imager.

[0049] The SVR algorithm is as follows:

[0050] According to the temperature of the weldment's surface feature point on AS and the maximum temperature of the weld zone given in Table 6, the temperature of the weldment's surface feature point on AS is taken as input, and the maximum temperature of the weld zone is taken as target. The SVR algorithm is used to establish a correlation between the temperature of the weldment's surface feature point on AS and the maximum temperature in weld zone. Combined with the surface temperature of FSW weldment on AS based on infrared camera, the characterization of the maximum temperature in the weld zone is achieved. Similarly, the SVR algorithm is used to establish a correlation between the temperature of the weldment's surface feature point on RS and the minimum temperature in weld zone. Combined with the surface temperature of FSW weldment on RS based on infrared camera, the characterization of the minimum temperature in the weld zone is achieved. The maximum and minimum temperatures of the weld zone obtained by this method are shown in FIG. 8. The maximum and minimum temperatures of the weld zone measured by the thermocouple in the experiment and the temperature results predicted by the SVR algorithm are shown in Table 9, and the prediction accuracy in the weld zone of this method is shown in Table 10.

TABLE-US-00009 TABLE 9 Prediction of temperatures in weld zone (° C.) Predicted Measured Predicted Measured maximum maximum minimum minimum No. temperature temperature temperature temperature 1 501.8 508.0 413.9 434.1 2 505.3 513.4 421.3 442.3

TABLE-US-00010 TABLE 10 Prediction accuracy of maximum and minimum temperatures in weldment weld zone (° C.) Mean relative Maximum relative Root mean percentage error percentage error square error Maximum 1.40% 1.58% 7.21 temperature Minimum 4.70% 4.75% 20.60 temperature

[0051] Aiming at the FSW of 2219 aluminum alloy, a finite element simulation model of FSW is established based on DEFORM. The data sets of temperature of surface feature points, the maximum and minimum temperatures in weld zone are extracted from the simulation result. Then, the SVR algorithm was used to establish the correlation between the surface temperature and the maximum and minimum temperatures of weld zone. Finally, a friction stir welding experiment is carried out. Combined with the surface temperature of the weldment measured by thermal imager, the characterization of the maximum and minimum temperatures in weld zone is achieved during FSW. Compared with the temperature results measured by thermocouples, the error of this method is less than 5%, and the accuracy is high. The effectivity of the characterization method of temperatures in weld zone of FSW based on infrared thermal imager is effective.