Int J Adv Manuf Technol DOI 10.1007/s00170-015-8116-9
ORIGINAL ARTICLE
Temperature measurement and control of bobbin tool friction stir welding Shujin Chen 1 & Hao Li 1 & Sheng Lu 1 & Ruiyang Ni 1 & Jianghui Dong 2
Received: 1 July 2015 / Accepted: 12 November 2015 # Springer-Verlag London 2015
Abstract Bobbin tool friction stir welding (BTFSW) is a relatively new, solid-state welding technology, but its control is not the same as the conventional friction stir welding (FSW) due to the unique welding tool structure. In this paper, closedloop control system was developed and the Smith predictive proportional-integral-derivative (PID) control method was presented to assist the welding system in producing an appropriate interface temperature response. As it is difficult to accurately detect the temperature in full range of the welding zone, the tool-workpiece interface temperature is detected by thermocouple and wireless transmission technology. Initial experiments were conducted to derive a qualitative understanding of bobbin tool friction stir welding processes. Ziegler-Nichols setting method was adopted to determine parameters of the PID controller. While examining the capabilities of Smith predictive PID control in BTFSW, this paper focuses on the control effect of hysteretic characteristics of welding temperature during butt welding. A compensation strategy was setting gaps along the welding path, and the gap could affect the distribution of temperature. Through our experiments, we demonstrate that temperature control strategy is feasible, and the tensile properties of the weld are uniform along the welding direction. Keywords BTFSW . Temperature control . Smith predictive PID * Shujin Chen
[email protected] 1
School of Material Science and Engineer, Jiangsu University of Science and Technology, Zhenjiang 212003, China
2
School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, Australia
1 Introduction Bobbin tool friction stir welding (BTFSW) is a relatively new, solid-state welding technology where the material is joined through mechanical stirring via a rotating tool that traverses the joint line. Like conventional friction stir welding (FSW), BTFSW has several advantages over fusion welding methods such as lower thermal distortion, improved material properties in the weld joint, lower energy input, and a reduced environmental impact [1]. It uses friction and mechanical plastic deformation to heat and soften the workpiece material, allowing mechanical deformation mechanisms similar to extrusion and forging to form a strong joint [2]. Because of many advantages, effort was placed on how best to automate FSW process for further adoption. Both FSW and BTFSW machines are similar to a milling machine, thus computer numerically controlled milling machines could immediately be used for the welding process. However, most milling machines are not outfitted with temperature or force data acquisition devices. In order to realize the welding process monitoring and control, it is necessary to detect the process parameter, such as the axial force, the traversing force, the side force, and the torque. The most common measuring method involves using an industrial load cell that is mounted between the FSW tool and the head of machine, although there are creative alternatives that are discussed in the next section. Most load cells utilize strain gages or piezoelectric material as the sensing element. However, due to the relatively high cost of the load cells, some researchers opt to design and build custom low-cost process parameter measurement systems [3–7]. An indirect method is used to experimentally measure the tool torque, traverse force, and axial force simultaneously in real time through monitoring the output torques of the servo motors and spindle three-phase AC induction motor equipped inside the FSW machine [8], which proved
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to be economical and reliable. In either case, once FSW process forces can be measured, it can lend insight into weld characteristics, expanded control capabilities that make FSW more adaptable to industrial application. There are several control architectures for FSW, such as position control, axial force control, and torque control [9]. Position control is advantageous for FSW machines with high rigidity, which can withstand the large process forces and prevent tool deflection. In addition, the material being welded must also be located, supported, and securely clamped in a manner that promotes rigidity and positional consistency from part-to-part. But when these machine and part conditions are not achieved, position control does not always produce acceptable weld quality, however, downward axial force control just for this occasion. The axial force can provide the necessary forging pressure, which is critical to weld formation. Axial force control has successfully applied to lap weld joints [10]. This force can be controlled by adjusting the vertical position, the traverse speed, the rotation rate of the tool [11], or the shape of tool shoulder [12]. The position of tool is used as the controlling variable in general. But force control is more likely to become unstable than position control, which can result in welding flaws [9]. Due to the non-linear relationship between axial force and tool plunge depth and the fact that workpiece stiffness is determined by the temperature of the welding environment, the amount of shoulder contact of the tool with the workpiece is approximate proportional to the axial force. Thus, when the entire shoulder is submerged below the surface, any change in plunge depth does not produce a controllable change in axial force [9, 13], then the flash becomes serious when the tool’s shoulder digs into the workpiece. In particular, the unsuitable rapid plunging of the tool produces a large amplitude increase of the axial force that may lead to stability issues [14]. To avoid the large spikes in force when the tool is suddenly plunged deeper into the workpiece, or slightly withdrawn, the motion profile of the tool must be smooth so as to minimize the force fluctuation [15]. Besides, increasing the lead angle of the tool provides a larger range of plunge depths that result in stability. In addition, with an increased lead angle, the amplitude of the force spike is reduced. Compared with the above methods, torque control has lower cost and providing greater understanding of the welding process. Longhurst et al. [16] control the spindle torque by adjusting the vertical position of the tool. The tool moves in vertical direction to keep the spindle motor current within limited values. With this method, a constant welding torque is maintained during the welding operation. Related research shows that
welding torque is achieved by maintaining or adjusting the plunge depth similarly to how axial force is controlled [17]. Results have shown that welding torque is more sensitive to plunge depth, and the torque control method is more stable over a wider range of welding temperatures than force control [15]. When robots are being employed, deviation from the planned path will increase with increasing welding forces [18, 19]. It is however possible to compensate for these issues by monitoring process forces and correlating them to path deviations [20, 21] or by using a vision-based tracking system. Fleming et al. used axial force as the feedback signal, but a high amount of noise exits in the force signal [21], and then, the torque is introduced as a feedback signal for seam tracking of FSW lap weld joints. Lastly, as an important process parameter in welding process, temperature directly affects the dynamic recovery and recrystallization process; therefore, it is necessary to control the welding temperature. The surface temperature of workpiece and tool can be detected by infrared radiograph [22]. Thermocouples are used to measure the temperature histories at different locations on the workpiece [23]. However, the weld zone temperature cannot be measured directly in real time, especially the location close to the weld zone must be measured. Then, the thermocouple inserted into the tool was applied to detect the temperature in real time [24, 25]. The signal can be transferred to control unit by a wireless data acquisition system (DAQ). Backer et al. describe a temperature controller that modifies the spindle speed to maintain a constant welding temperature [26]. Fehrenbacher et al. develop a suitable temperature measurement strategy and a closed-loop control algorithm to maintain a constant weld zone temperature [27]; the results indicate that the closed-loop control of the tool-workpiece interface temperature is feasible. Could a similar situation be present BTFSW? As shown in Fig. 1, the tool of conventional FSW only has a shoulder, but the tool of BTFSW consists of a larger pin and two shoulders, all of them contact the material, so this process can produce more heat and potentially facilitates the joining of hollow component. In the current study, however, there is lack of related report of BTFSW torque feature. Like conventional FSW, there is no linear relationship among welding process parameters, such as the rotary speed, traveling speed, torque, and forward resistance, but they influence one another. Welding parameters of FSW and BTFSW have the same impact on welding quality [28], and BTFSW can even weld the sheet [29]. During the BTFSW welding process, high-speed rotation of the tool causes high temperature in the weld area, which leads to plastic deformation and migration of metal between two shoulders.
/ MPa
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T/K
(a) FSW
/ s-1
Fig. 2 Viscosity stress surface
During the friction stir welding process, welding torque comes from two parts: sliding friction and viscous friction. Although sliding friction is the main part, the proportion of viscous friction increases gradually as temperature increases when the welding zone is in high temperature (above 560 K)
(b) BTFSW Fig. 1 The principle of FSW and BTFSW. a FSW. b BTFSW
The research presented in this paper introduces and examines temperature as a controlled parameter instead of axial force. A temperature control architecture that varies rotary speed to maintain a desired temperature is implemented on a three-axis milling machine. The resulting performance of the temperature controller is analyzed, and relationships between temperature and torque are defined. Features are identified that make temperature control more attractive than torque control. Our work is accomplished on the hypothesis that uniform weld quality corresponds to a constant weld zone temperature, but it does not mean that the temperature is the only or best process parameter that may need to be controlled.
2 Materials and research background In this study, the material used in this experiment is 6061 aluminum ally (Al-0.6Si-1.0xMg) with a T6 heat treatment. The plates were machined with a consistent dimension of 230 × 80 × 8 mm to obtain comparable results. Table 1 is the chemical composition of 6061-T6. Table 1
Chemical composition of 6061-T6 aluminum
Chemical composition (wt%) Cu
Si
Fe
Mn
Mg
Zn
Cr
Ti
Al
0.15−0.4 0.4–0.8 0.7 0.15 0.8–1.2 0.25 0.04–0.35 0.15 Rest
Fig. 3 BTFSW machine and bobbin tool. a BTFSW machine. b Photograph of bobbin tool. c Bobbin tool
Int J Adv Manuf Technol Torque sensor
Rotary speed instruction Controller
Bobbin tool
Torque signal Welding zone temperature signal
Workpiece
Loading jig Travle speed output CNC machine tool
Fig. 6 Sketch of temperature closed-loop control system
thus, torque changes cannot be calculated theoretically. Therefore, this article intends to use sensors to detect the welding temperature and torque of the spindle and create an input of a closed-loop temperature controller.
[30]. As described in Eqs. (1) and (2), constitutive equations of 6061 aluminum are in line with the inverse hyperbolic sine function; viscosity stress σe may be expressed as: " 1 # 1 Z n −1 σe ¼ sinh ð1Þ α A Q • ð2Þ Z ¼ ε exp RT where T is welding temperature (K), and ε is strain (s−1). α, n, A, and Q are material constants [30] (α = 0.06 MPa−1, n = 6.966, A = 4.6867E9 s−1, Q = 276.8 KJ/mol); σe is the equivalent to the steady-state flow stress (MPa), R is the gas constant (R = 8.314 mol−1 K−1), and Z is Zener-Hollomon parameter. When the welding zone temperature T changes within 560~860 K and effective strain ε within 0.1~100 s−1, σe changes as shown in Fig. 2. Obviously, σe is influenced by welding zone temperature T and strain ε. Due to the high efficiency of sliding friction, welding zone metal is quickly softened to plastic state. After the plastic metal is removed by moving and rotating tool quickly, the lower temperature metal ahead of tool becomes new extrusion and friction zone rapidly, leading to dramatic change of viscous friction stress in temperature and effective strain. In the actual welding process, it is difficult to detect σe;
3 Experimental setup As shown in Fig. 3, the experiment is conducted on a modified milling machine which denoted x (perpendicular to the tool axis and in the direction of welding), y (perpendicular to the direction of welding), and z (parallel to the tool axis and in the plunge direction). The spindle is driven by an asynchronous spindle motor of 15 KW, with its spindle speed ranging 0~5000 rpm. The diameter of the shoulders is both 23 mm, and the pin diameter is 9 mm. There are two symmetrical threads and three platforms on the pin surface, as shown in Fig. 3b.
V / mm/min
Fig. 4 Photograph of torque sensor and tool holder
200 180 160 140 120 100 80 60 40 20 0
0
50
100 Time / s (a) Traveling speed variation curve
150
500
PC monitor
Wireless module
Wireless module
Transmitter
Senor
400 300 200 100 0
0
50
100
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Time / s
Battery Fig. 5 The principle of torque detection system
(b) Interface temperature curve
Fig. 7 Initial experiments with rotary 350 rpm. a Traveling speed variation curve. b Interface temperature curve
Int J Adv Manuf Technol 600
Temperature /
Figure 6 shows the sketch of BTFSW temperature closed-loop control system. Weld zone temperature (tool-workpiece interface temperature) and welding torque signal are transferred to the controller by DAQ system; the output of the controller is rotary speed.
400rpm 450rpm
500 400 300 200 100 0
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Time / s
4 Welding procedure and control system development
Fig. 8 Temperature curve values with different rotation speed
Due to the hysteretic characteristics of temperature signal, it is necessary to minimize the time delay associated with heat flow through the tool; therefore, it is desirable to place the thermocouples as close as possible to weld center and tool-workpiece interface. Fehrenbacher et al. made use of a through hole to enable direct contact of the thermocouple with the workpiece material [27]. In this work, we are also employing the through hole strategy to detect the temperature. A 1-mm-diameter hole was drilled into the tool shank so that it exits on the top shoulder, 4 mm from the root of the pin (Fig. 3c). One type armored K thermocouple was inserted into the through hole; its sheath is made of 304 stainless steel. After many experiments, the response time of the thermocouple was determined as 48 ms. The plastic metal is filled into the gap between the sheath and the inner wall of the hole, so the actual response time may be shorter. As shown in Fig. 4, the spindle is equipped with torque sensor capable of sensing torque up to 200 N.m. Spindle has a hollow channel which is used to connect the signal line, and then, torque sensor and thermocouple line is connected with sample circuit which has via analog-to-digital channel (5 v range and 12 bit) on a board based on microprocessor. All analog measurements (welding torque and temperature) are sampled at 500 Hz. After the treatment with the microprocessor, the torque signal is sent to the wireless transmission module. During the welding process, torque senor, thermocouple, sample circuit, battery, and corresponding wireless module have synchronous rotary speed. Through the wireless receiving module, the torque and temperature signal are fed into a computer, where signals are stored and displayed (Fig. 5). Fig. 9 The structure of conventional PID closed-loop control system
Initial experiments were conducted to derive a qualitative understanding of bobbin tool friction stir welding processes. In order to generate more energy to preheat the metal and produce enough plastic metal, the lower traveling speed and longer duration was adopted in the initial stage of welding. This approach also prevents pin from being broken easily. The welding process can be divided into different traveling speed (mm/min) segments as the following: 10, 20, 30, 50, 70, 90, 110, 130, 150, and 170. The speed segment durations were 35, 15, 3, 3, 3, 3, 3, 3, 3, and 3 s as seen in Fig. 7a. The dashed line is the boundary between the different traveling speeds. The rotation speed varied from 10 to 170 mm/min and from 350 to 700 rpm in all experimental studies. The raw torque data was filtered with a one-order Butterworth digital filter with cutoff frequency of 3 Hz. The cutoff frequency of 3 Hz provides sufficient bandwidth to detect the torque signal as the tool traverses the gap. The digital filter transfer function is: H ðZÞ ¼ 5:9609 10−4 ðz þ 1Þ=ð5:636−0:9988Þ
ð3Þ
where z is the discrete forward shift operator. The filtering substantially removed the inherent noise. As shown in Fig. 7b, when the rotating tool extruded into the workpiece with low traveling speed (10 mm/min), the value of the tool-workpiece interface temperature is very small and increased slowly. But after 50 s, they increased significantly due to the welding speed increased 20 mm/min every 3 s. But when the traveling speed remained constant 170 mm/ min, the interface temperature kept growing all the while. When the shoulders are close to the edge of the workpiece (135 s), the temperature increased rapidly due to heat accumulation, but it decreased quickly after the tool left from the workpiece.
Int J Adv Manuf Technol Step Response
Fig. 10 Step response curve of conventional PID control system. a Open-loop. b Closed-loop
Step Response
4 3.5
1 0.8
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Amplitude
Amplitude
3
2 1.5
0.6 0.4
1 0.2
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Time (sec)
(a)open-loop
BTFSW experiments are conducted in different welding parameters (the welding rotation speed is 400 and 450 rpm, with constant traveling speed of 170 mm/min), and BTFSW parameter detection system is used to collect process parameters. As shown in Fig. 8, when the welding rotation speed is 400 and 450 rpm, the corresponding welding zone peak temperature is 537.5 and 552.5 °C; the overall trend is that the welding zone peak temperature increases with the increase of the welding rotation speed, but its growth is slow. As shown in Fig. 8, there is some latency in interface temperature curve with different rotation speeds. This delay is due to the different heat accumulation in different rotation speed. In the welding process, the temperature of weld zone will exceed the optimum temperature (better quality of welded joint can be obtained by this temperature). Therefore, it is necessary to control the welding temperature so as to obtain suitable seam of BTFSW. Matlab is used to deduce the transfer function about the welding rotation speed and the welding zone temperature. Considering the heat accumulation and heat transfer inertia during the welding process, this transfer function contains a pure time delay. The transfer function with simple single input (rotation speed) and single output (weld zone temperature) was established as the following: GðsÞ ¼ Gp ðsÞ expð−T d sÞ K expð−T d sÞ ¼ ð1 þ T p1 sÞð1 þ T p2 sÞ ð4Þ here K = 0.1611, Tp1 = 0.945, Tp2 = 0.017, and Td = 1.1. Fig. 11 The structure of SmithPID control system
10000
12000
14000
0
0
10
20
30
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50
Time (sec)
(b)close-loop
Figure 9 shows the conventional proportional-integralderivative (PID) closed-loop control system structure. After comparing the reference input and the feedback signal, the error signal is used as the input of the controller. The output of the controller is rotation speed. Rotation speed commands are sent at 10 Hz and are accepted by machine in 1 % increment of the nominal rotation speed, i.e., for 400 rpm. To achieve a predetermined desired control effect, the proper P, I, D control parameters must be chosen. When the parameters of the controller are set and put into operation, it the requirements. If it is not satisfied, it is necessary to change the controller parameters or control structure. The response curve of the open-loop system is shown in Fig. 10a. It is obvious that the step response time is too long and needs to consider that the change of controlled parameter meets is due to the existence of temperature; the system has a larger inertia, in the case of open-loop control, and the larger overshoot easily appears. The Ziegler-Nichols setting method was adopted, so PID parameters (Kp, Ki, Kd) were set as the following: Kp=200, Ki = 1, and Kd = 2000. The closed-loop control effect of conventional PID is shown in Fig. 10b. The transition process time is significantly shortened, and the step response curve with 1.2 % overshoot quantity is obtained, but transition time is long, as the 20 s; it will seriously reduce the response speed of the system, so the conventional PID closed-loop control is not desirable.
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DðsÞ 1 þ DðsÞGp ðsÞð1−e−T d s Þ
D0 ðsÞ ¼
ð5Þ
After compensation, the transfer function of the system is as follows: ΦðsÞ ¼
DðsÞGp ðsÞ e−T d s 1 þ DðsÞGp ðsÞ
ð6Þ
The root locus diagram is shown in Fig. 12. Root locus locates in the left half plane when the gain is small. The system is always stable, in full compliance with the welding zone temperature control requirements of the stable BTFSW welding process. Bode diagram of the system is shown in Fig. 13, system gain margin is 36.4 dB, phase angle margin is 173°, and the system is stable. The step response of the Smith-PID system is shown in Fig. 14. With Smith-PID feedback control, the
Gm = 36.4 dB (at 26.4 rad/sec) , Pm = 173 deg (at 0.0384 rad/sec)
Magnitude (dB)
20 0 -20 -40 -60 -80 360
Phase (deg)
In order to solve the above problems, the appropriate algorithm should be established to realize the temperature control. Then, a Smith predictive control method which can resolve the problem of a long time delay is implemented. Smith predictive control method proposed a delay compensation model, and the principle is with PID controller and a compensation part; the compensation element becomes Smith predictor. Its control system is shown in Fig. 11. R(s) is reference input, C(s) is output, D/(s) is compensation circuit, and U(s) is the output of controller. The performance of the system with Smith-PID feedback control is analyzed and discussed in the following parts. Note that there are two feedback loops. The outer control loop feeds the output back to the input. However, this loop alone would not provide satisfactory control because of the delay; this loop is feeding back outdated information. Intuitively, the system is controlled by the inner loop which contains a predictor of what the (unobservable) output of the plant Gp currently is. In the dotted lines, D/(s) is composed of a controller D(s) and a Smith predictor P(s). The transfer function of D/(s) is as follows:
270 180 90 0 -90 -3 10
-2
-1
10
0
10
1
10
2
10
3
10
10
Frequency (rad/sec)
Fig. 13 Bode diagram of Smith-PID
system stability is good and no overshoot, and the transition process time is about 1 s.
5 Experimental procedure results and discussion The interface temperature was maintained at a constant value by PID closed-loop control system, which could real-time adjust the travel speed in this experiment. The constant value of temperature was set to 480 °C, and the rotation speed was set in the range of 250 to 600 rpm. The section will focus on analyzing the weld zone temperature, torque of BTFSW welding process under temperature control. As shown in Fig. 15, the red curve indicates the welding zone temperature of the rotation speed 350 rpm. At the beginning, the weld zone temperature rise slowly, then rise rapidly in the acceleration stage. When the weld has just entered a stable stage, the weld zone temperature is about 400 °C. The closed-loop control system take effect; the welding zone temperature continues to rise rapidly with the increase of the rotation speed. When the temperature is close to 480 °C, the rotation speed declines sharply, and the temperature slowly rises. When the weld zone temperature reaches 490 °C, this temperature has a slight upward trend, the peak temperature of weld zone lower than 500 °C, and the temperature control error in the range of 15 °C. Overall, the weld zone temperature Step Response
Root Locus 40 30
1 0.8
10
Amplitude
Imaginary Axis
20
0 -10
0.6 0.4
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0.2
-30 -40 -140
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Fig. 12 Root locus of Smith-PID
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0.6
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Time (sec)
Fig. 14 Step response curve of Smith-PID control
1.2
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Temperature /
600
Welding direction
Without control
400
Gap Rotation direction
With control
z
200
L
x y
0 0
50
100
150
Time / s
Fig. 15 The weld zone temperature curve
Fig. 17 Schematic diagram of gap position
In order to further verify the validity of the temperature control algorithm, two gaps (width is 0.4 mm, 0.6 mm) along the y direction were arranged in the welding path; the distance (L) between two gaps is 80 mm, and the length along the y direction is 24 mm, as shown in Fig. 17. After the implementation of temperature control, the torque and interface temperature are shown in Fig. 18. After welding, five tensile specimens are cut along the welding direction every 40 mm in normal welding zone. As shown in Fig. 19, although the tensile strength of the BTFSW joint is slightly increased along the welding direction, the tensile strength of joints showed the consistency. It can be seen that the tensile strength of earlier welding stage is a little low, mainly because that BTFSW welding has just entered the
80 pin contacts gap
Torque / N.m
is basically maintained at a constant value. Without temperature control, the weld zone temperature continues to rise slowly during stable welding phase. In contrast, the effect of closed-loop control system is significant. The weld zone temperature control curve increases slightly near the end of the welding. This is mainly because the high-speed rotating tool extrudes metal at the end of the welding. At the same time, the closed-loop control system is restricted by welding speed. It is unable to reduce the weld zone temperature by continuing to reduce the rotation speed. Eventually, this will lead to a slight rise in the weld zone temperature. So the effect of closed-loop control system is limited, and it still needs to improve mobility. In Fig. 16, after the interface temperature is controlled during the BTFSW process, the torque of stable welding stage is basically maintained at a constant value. With the rotation speed of 350 rpm, the torque is maintained in the range of 48 to 50 N.m. This is mainly due to the implementation of temperature control; the interface temperature can be well controlled and maintained at a constant value, so the metal of welding zone is fully plasticized and not overly soften. The friction stir of the tool and workpiece is relatively stable, and the whole welding process is stable. The torque of stable welding stage without controlling the temperature shows a slow downward trend. When the interface temperature is too high, the torque will appear abnormal oscillation, and average torque is small. The welding process is not stable and even cause a broken pin or test failure.
60 40 20 0
0
50
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150
Time / s (a) Torque curve 600
With control
40
Without control
20
Temperature /
Torque / N.m
60
400
200
0
0 0
0
50
100
150
Time / s 20
Fig. 16 Torque curve
40
60
80 100 120 140 160 Time / s
(b) Interface temperature curve
Fig. 18 The torque for BTFSW with different gap width. a Torque curve. b Interface temperature curve
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6 Tensile strength The elongation
177 Initial stage
Stable welding process
5.6
174
5.2
171
4.8
168
4.4
165 1
2
3 Sample number
4
Elongation / %
Tensile strength / Mpa
180
This reflects the stability of welding process in a certain extent. The average hardness of these specimens is 60 HV, and the minimum (56 HV) occurs at the later stage of welding. It is observed that the change in microhardness is slight and it may be due to the temperature stability of BTFSW process.
6 Conclusion
4 5
Fig. 19 Tensile test results (with control)
A welding zone temperature closed-loop control system is established in this research, and experimental results prove the validity of the algorithm. The results are as follows:
stable welding stage; corresponding interface temperature rises too late to produce enough plastic weld metal, so that the tensile strength is low. The elongation of joints also showed the same regularity, which reveals that the joint performance at this stage is relatively stable, reflecting better effect of temperature control. Microhardness and microstructure of stable welding process were analyzed. The region very close to the NZ (nugget zone) experiences temperature nearer to 500 °C which forces the secondary phase particles in the original phase matrix to dissolve, as is visible in Fig. 20. At the same time, the NZ is also influenced by the stirring of the pin, which produces fine grain structure in the NZ region in comparison with other regions. From Fig. 20, we can see that these four specimens have the similar grain size (mean value is about 14 μm) at NZ.
(1) Experiments prove the validity of the method that by using a through hole, the thermocouple sheath is enabled to be in direct contact with the workpiece material. Variation of the interface temperature within one rotation of the tool was experimentally observed for BTFSW for the first time. (2) After the implement of welding zone temperature control, characteristic parameters of BTFSW welding process are as follows: under the condition of 350 rpm, the steady-state temperature of weld zone is maintained at 495 °C (set temperature is 480 °C), temperature control error is about 15 °C, and the torque is maintained in the range of 48 to 50 N.m. (3) The tensile strength and elongation of joints are consistent along the welding direction. The effect of
Fig. 20 Optical microstructure of NZ of stable welding process. a Specimen 2. b Specimen 3. c Specimen 4. d Specimen 5
(a) specimen 2
(b) specimen 3
(c) specimen 4
(d) specimen 5
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temperature control is remarkable, resulting in improved uniformity and stability of BTFSW process. This control system has great potential for the application of BTFSW in manufacturing. The future work of this project is to develop an energy-monitoring control system for BTFSW. Acknowledgments This work was supported by the National Natural Science Foundation of China under Grant No. 51205175 and 51375218.
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