한국해양공학회지 제25권 제6호, pp 1-8, 2011년 12월 (ISSN 1225-0767)
Development of Digital Gas Metal Arc Welding System and Welding
Current Control Using Self-tuning Fuzzy PID
Phuc Thinh Doan*, Pandu Sandi Pratama*, Suk Yoel Kim*, Hak Kyeong Kim*,
Hwang Yeong Yeun**, Gi Sig Byun** and Sang Bong Kim*
*Department of Mechanical Eng., College of Eng., Pukyong National University
**Department of Control and Instrumentation Eng., College of Eng., Pukyong National University
KEY WORDS: GMAW, Gas Metal Arc Welding, Fuzzy, PID controller
ABSTRACT: This paper describes a new method for a digital gas metal arc welding (GMAW) system. The GMAW system is an arc welding
process that incorporates the GMAW power source (PS-GMAW) with a wire feed unit (WFU). The PS-GMAW requires an electric power of
constant voltage. A constant magnitude is maintained for the arc current by controlling the wire-feed speed of the WFU. A mathematical model is
derived, and a self-tuning fuzzy proportional–integral–derivative (PID) controller is designed and applied to control the welding current. The
electrode wire feeding mechanism with this controller is driven by a DC motor, which can compensate for both the molten part of the electrode and
undesirable fluctuations in the arc length during the welding process. By accurately maintaining the output welding current and welding voltage
at constant values during the welding process, excellent welding results can be obtained. Simulation and experimental results are shown to prove
the effectiveness of the proposed controller.
1. Introduction
voltage with uncertainties in the linearized GMAW model,
along with the system parameters. However, their studies
usually required large time-consuming calculations.
To overcome the above problem, various GMAW systems
have been developed. Because of the revolution in integrated
circuit technology, which now permits high quality, small,
quick response circuits, it is reasonable to use an IC in the
design of PS-GMAW. The PS-GMAW controller incorporates
both a microcontroller and IC TL494. The PS-GMAW is a
constant voltage power supply. Most gas metal arc welding
systems use a constant voltage power supply because of their
ability to self-regulate the arc length (Naidu et al., 2003). Furthermore, a constant welding current is achieved by controlling the
wire-feed speed. It is possible to use a conventional PID to
control the wire-feed speed of the WFU. However, conventional PID controllers do not yield reasonable performance
over a wide range of operating conditions because of the fixed
gains used. Thus, the PID parameters need to be automatically adjusted by a fuzzy set (Truong and Aha, 2009).
This paper presents a new closed-loop control method
based on a self-tuning fuzzy PID controller. The developed
digital GMAW system permits the output welding current
and voltage to track any set values easily and quickly. The
developed GMAW system allows welding voltage settings of
16 VDC to 36 VDC and welding currents from 50 A to 220 A.
The simulation and experimental results are shown to prove
the effectiveness of the proposed system and controllers.
Arc welding is one of the key processes in industrial manufacturing such as the automotive, ship building, and construction industries. Among the various types of weldin GMAW is
extensively used because of its high speed and
suitability for both manual and automatic modes of welding for
a wide range of ferrous and nonferrous metal parts (Kam et al.,
2002; Naidu et al., 2003).
Both arc voltage and welding current are mainly controllable parameters in GMAW process. Many qualitative features of
welding are affected by process parameters such as weld
geometry, weld metallurgical characteristics, transfer mode of
melting droplets, weld stability, weld defects, and strength
(Mohammad and Mohammad 2011). Some control approaches have
been introduced to control these variables. Naidu et al. (2003)
designed two PI controllers to control the process. Abdelrahman
(1998) used feedback linearization to control the current and
arc length in the GMAW system. However, those methods have
limitations because of the nonlinear characteristics and inaccuracies of the GMAW process model. Ngo et al. (2007) applied a
sliding mode to control the wire-feeding rate to obtain a
desired constant welding current. The adaptive control was also
implemented to design the adaptive predictive control system for
welding process by Zhang and Liu (2003). Another method was
introduced by Mohammad and Mohammad (2011), who used
an ARMarkov-based MPC to control the welding current and arc
Corresponding author Sang Bong Kim: 365, Sinseon-ro, Nam-Gu, Busan, Korea, +82-51-629-6158, kimsb@pknu.ac.kr.
1
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Phuc Thinh Doan, Pandu Sandi Pratama, Suk Yoel Kim, Hak Kyeong Kim, Hwang Yeong Yeun, Gi Sig Byun and Sang Bong Kim
2. System description and principles of operation
Fig. 1 shows the configuration of fundamental equipments
necessary for the GMAW system. The wire feed speed of the
WFU has a range of about 1.9 to 30 m/min. It consists of a DC
motor, gearbox, guide tube clamp, and feed rolls. The
welding electrode wire is fed by the WFU to compensate for
the electrode melting-rate in order to continuously maintain a
welding arc during the welding process. The variable electrode feed-rate makes the output welding current change. The
WFU must be controlled in order to achieve the set welding
current and obtain a clean positive arc with each strike.
Furthermore, it must minimize stubbing, skipping, and
spatter, and also maintain steady wire feeding during the
welding process.
Fig. 2 Relationship between the electrode feed-rate and welding
current
Fig. 1 Configuration of fundamental equipments necessary for
GMAW system: (1) shielding gas; (2) DC motor and gear
box; (3) welding electrode roll; (4) WFU; (5) PS-GMAW; (6)
control box; (7) welding gun; (8) workpiece; (9) control cable
To clearly illustrate this concept, Fig. 2 shows the relationship between the welding current and electrode feed-rate
(Lincoln Electric Company, 1997) for an aluminum wire positive electrode.
The PS-GMAW is a constant voltage power supply. As a result,
any change in arc length (which is directly related to voltage)
results in a large change in heat input and current. A shorter
arc length produces a greater heat input. This makes the wire
electrode melt more quickly and thereby restores the original
arc length. This helps operators keep the arc length constant.
This effect has been called self regulation. Fig. 3 shows the voltampere characteristics of a typical constant-voltage power
supply, along with the arc voltage curves (Naidu et al., 2003).
Fig. 3 Self-regulation of arc voltage
3. PS-GMAW design
Fig. 4 explains the typical block diagram of the PS-GMAW
used for the GMAW system (American Welding Society Inc.,
1997). The incoming three-phase or single phase 50/60 Hz, AC
power is converted into DC by the full wave rectifier. This
DC is applied to the inverter using semiconductor switches,
and the inverter inverts the DC into high-frequency square
wave AC. This high-frequency voltage allows the use of a
smaller step down transformer. After being transformed, the
AC is rectified to DC for welding.
Developmet of Digital Gas Metel Arc Welding System and Welding Current Control Using Self-tuning Fuzzy PID
3
respectively.
The purpose of controlling the WFU is to change the
electrode feed-rate to track the set value, Is. In the GMAW
process, the electrode feed-rate must be equal to the electrode
melting-rate to maintain a stable arc length.
W f Wm
(2)
where Wm is the electrode melting-rate.
The electrode melting-rate, Wm, can be expressed as a
function of welding current, Iw, and welding voltage, Uw, as
follows (Chu and Tung 2005; Ngo et al., 2007):
Fig. 4 Block diagram of PS-GMAW
Wm K c I w K vU w
The PS-GMAW schematic is shown in Fig. 5. The controller
incorporates both a microcontroller and IC TL494. The TL494
is a single monolithic chip designed for power supply
control. The microcontroller is connected to a user interface
so that welders can set all of the parameters for welding
such as the welding wire diameter, protection gas mixture,
and welding voltage.
(3)
where Kc and Kv are the coefficient ratios of the melting- rate
to the welding current and welding voltage, respectively.
From Eqs. (2) and (3), the electrode feed-rate can be
expressed as:
W f ( s ) K c I w K vU w
(4)
Therefore, the welding current can be expressed as:
Iw
W f ( s)
Kc
K vU w W f ( s ) G
Kc
Kc
Kc
(5)
where △G denotes KvUw.
In fact, the welding current influences the electrode feedrate much more than does the welding voltage. If the welding
voltage is considered as a disturbance in Eq. (4), the block
diagram for an open loop transfer function of the WFU is
shown in Fig. 6.
Fig. 5 Schematic circuit of PS-GMAW
4. WFU controller design
Fig. 6 Block diagram of open loop of WFU
4.1 Model of WFU
In this system, a DC servomotor is utilized to control the
If △G is considered to be a disturbance of the system,
from Eqs. (1) and (5), the transfer function, G(s), of the WFU
between Vm(s) and Iw can be obtained as follows:
DC motor of the WFU, which controls the electrode feed-rate to
maintain the welding arc. The dynamic relationship between
the electrode feed-rate and the voltage applied to the DC motor
G(s)
I w (s)
1 W f ( s ) G ( s )
Vm ( s ) K c Vm ( s ) Vm ( s )
(6)
of the WFU can be expressed as a second-order dynamic
equation as follows:
G(s)
I w (s)
1
b0
Gd
2
Vm ( s ) K c ( s a1s a0 )
(7)
Gm ( s )
W f ( s)
Vm ( s)
b0
s a1s a0
2
where △Gd denotes △G(s)/Vm(s).
(1)
where Wf(s) and Vm(s) represent the electrode feed-rate and
DC voltage applied to the DC motor of the WFU,
4.2 Controller design for WFU
PID controllers are the most widely used type of controller in
4
Phuc Thinh Doan, Pandu Sandi Pratama, Suk Yoel Kim, Hak Kyeong Kim, Hwang Yeong Yeun, Gi Sig Byun and Sang Bong Kim
modern industry because of their simple control structure.
Nonetheless, the conventional PID controllers do not yield
reasonable performance over a wide range of operating
Fig. 8 shows the proposed self-tuning fuzzy PID controller.
Ke and K△e are scaling gains that make the inputs satisfy the
operational ranges.
conditions because of their fixed gains. To solve this problem, the
PID parameters need to be adjusted automatically. A selftuning fuzzy PID controller is a powerful combination that
allows a system to improve the control performance with fast
response, minimal overshoot, and greater stability.
Fig. 7 shows a conventional PID controller. The controller
signal can be expressed in the time domain as:
u K p e K i edt K d
de
dt
(8)
e Is Iw
(9)
K i K p Ti
(10)
K d K pTd
(11)
Fig. 8 Self-tuning fuzzy PID controller
The general structure of fuzzy logic control is represented
in Fig. 9.
where e is the tracking error; Ti and Td are the integral time
coefficient and derivative time coefficient, respectively; and
Kp, Ki, Kd are the proportional gain, integral gain, and
derivative gain, respectively.
Fig. 9 Fuzzy logic control structure
The inputs range from -1 to 1. For each input variable, five
membership functions are used. NB, NS, ZE, PS, and PB are
Negative Big, Negative Small, Zero, Positive Small, and
Positive Big, respectively. The membership functions of the
fuzzy inputs are shown in Fig. 10.
Fig. 7 Conventional PID controller
This paper proposes a self-tuning fuzzy PID controller with
two inputs and three outputs. The error, e, and its derivative,
ė, are used as inputs. The tuning-gains, Ktp, Kti, and Ktd, are
the outputs. The fuzzy controller is added to the conventional
PID controller to adjust the parameters of the PID controller.
The new controller is given as follows:
u K pf e K if edt K df
de
dt
K pf 2 K tp K p
K if 2 K ti K i
K df 2 K td K d
(12)
The outputs range from 0 to 1. For each output variable, seven
membership functions are used. ZE, MS, S, M, B, MB, and
(13)
where Kpf, Kif, and Kdf denote the changeable gains of the
new controller; Kp, Ki, and Kd are the fixed values from the
conventional PID controller; and Ktp, Kti, and Ktd are the
tuning-gains from the fuzzy controller.
Fig. 10 Membership functions of input (e, ė)
VB are Zero, Medium small, Small, Medium, Big, Medium
Big and Very Big, respectively. The membership functions of
the fuzzy outputs are shown in Fig. 11.
Using the above fuzzy sets of input and output variables,
fuzzy rules are composed as follows:
Developmet of Digital Gas Metel Arc Welding System and Welding Current Control Using Self-tuning Fuzzy PID
5
Fig. 11 Membership functions of output (Ktp, Kti and Ktd)
Rule ith: If e is Ai and ė is Bi, then Ktp is Ci, Kti is Di, and
Ktd is Ei, where Ai and Bi are the linguistic label inputs and
Ci, Di, and Ei are the linguistic label outputs. Tables (1)-(3)
show the control rules used for the proposed self-tuning
fuzzy PID controller.
Table 1 Rule bases for determining gain Ktp
Fig. 12 Rule surface views: (a) Ktp, (b) Kti, and (c) Ktd
5. Simulation and experimental results
e e
NB
NS
ZE
PS
PB
5.1 Configuration of hardware
The developed fully automatic GMAW system is presented
NB
VB
VB
VB
VB
VB
in Fig. 13.
NS
B
B
B
MB
VB
ZE
ZE
ZE
MS
S
S
PS
B
B
B
MB
VB
PB
VB
VB
VB
VB
VB
Table 2 Rule bases for determining gain Kti
e e
NB
NS
ZE
PS
PB
NB
M
M
M
M
M
NS
S
S
S
S
S
ZE
MS
MS
ZE
MS
MS
PS
S
S
S
S
S
PB
M
M
M
M
M
Fig. 13 Developed GMAW system.
Table 3 Rule bases for determining gain Ktd
e e
NB
NB
ZE
NS
S
ZE
M
MB
MB
VB
VB
PS
B
VB
VB
VB
VB
PB
VB
VB
VB
VB
VB
NS
ZE
PS
PB
S
M
MB
VB
B
MB
VB
VB
It includes the GMAW and a travel beam. The travel beam
moves the welding torch along the welding line at a constant
velocity. The PS-GMAW has an LCD display and analog
display to show the welding current and welding voltage,
making it easy to compare the digital and analog values.
5.2 Identification of parameters for transfer function
The transfer function in Eq. (1) can be rewritten as follows:
In this paper, the “centroid” method is used for defuzzification to obtain Ktp, Kti, and Ktd. As a result, the rule sets
are established and shown on the surfaces in Fig. 12.
Gm
Wf
Vm
motor b '
Vm
b ' kT
Lam s Ram J m s bm ke kT
(14)
6
Phuc Thinh Doan, Pandu Sandi Pratama, Suk Yoel Kim, Hak Kyeong Kim, Hwang Yeong Yeun, Gi Sig Byun and Sang Bong Kim
Gm
b 'kT
b0
Lam J m s ( Lambm Ram J m ) s ( Rambm ke kT ) s 2 a1s a0
2
(15)
where kT, ke, Lam, and Jm are the motor torque constant, back
electromotive force constant, armature inductance of DC
motor, armature resistance, and moment of inertia, respectively. The parameters of the DC motor are given in Table 4.
Table 4 Numerical values of DC motor of WFU
Parameters
Values
Units
ke
57.3×10-3
Vs/rad
-2
kT
4.8×10
N×m/A
Ram
1.1
-3
Lam
0.9×10
-4
GMAW system shown in Fig. 13. A comparison is presented
of the conventional PID and self-tuning fuzzy PID for
controlling the wire feed speed of the WFU. Fig. 14 shows
the simulation responses for the step type of current for both
controllers without disturbance (△Gd=0). The parameter
values set for the conventional PID (Kp=2, Ki=0.1, and Kd=0.8)
and the scaling gains (Ke=2 and K△e=0.1) were obtained from
experiments with the real model. The control performance of
the system using the self-tuning fuzzy PID was better than
that using the conventional PID with regard to, not only the
rising time, but also the overshoot, settling time, and steady
error. Fig. 15 shows how the tuning-gains, Ktp, Kti and Ktd,
vary online with the output of the system. After 2 s, the
tracking error goes to zero; therefore, the tuning-gains
become constant.
H
Jm
0.157×10
kgm2
bm
0.1
Nms
For the specific WFU, gear box rate Kgear=1/34, while feed
roll diameter Droll=40×10-3m. The ratio between the electrode
feed-rate and angular velocity of the DC motor is given as:
b
Wf
motor
Droll
K gear 60 0.0353
2
(16)
The parameters of transfer function Gm(s) can be calculated
as follows:
b 'kT
141,542
Lam J m
(17)
L b Ram J m
a1 am m
7,077
Lam J m
(18)
b0
a0
Rambm ke kT
7,979, 476
Lam J m
Fig. 14 Simulation response on step type of current in both controllers
(19)
The nominal mathematical model of transfer function Gm(s)
is as follows:
Gm ( s )
141,542
s 2 7,077 s 7,979, 476
(20)
The value of Ki depends on the diameter of the welding
electrode. Based on the experimental results, the gain, Ki, can
be approximated as falling in the range of 0.041~0.046 for a 1.2
mm aluminum electrode. In this paper, Ki=0.043 is chosen.
¡
5.3 Simulation and experimental results
To verify the effectiveness of the proposed controllers, a
simulation and experiment were performed for the developed
Fig. 15 Tuning-gains, Ktp, Kti and Ktd
Next, to prove the effectiveness of the proposed controller,
a disturbance scheme is included. The disturbance generated
in this case is given as:
Developmet of Digital Gas Metel Arc Welding System and Welding Current Control Using Self-tuning Fuzzy PID
G A sin(t ) Rnd (t )
7
(21)
where A=2, =2/8, and Rnd(t) is the white noise signal.
Fig. 18 Tuning-gains, Ktp, Kti and Ktd, with disturbance.
The experimental results for the welding current and
voltage are shown in Fig. 19. These results show that the
output values are stable and track the setting values very
well during the welding process. Fig. 20 (a) and Fig. 20 (b)
show welding results for the proposed system and the
previous system (Chu and Tung 2005), respectively. These
results show that the proposed system’s performance is better
than the previous system’s.
Fig. 16 Disturbance applied to GMAW system.
Fig. 16 shows the disturbance signal, and Fig. 17 shows the
output responses in a comparison between the self-tuning
fuzzy PID controller and the conventional PID controller. In
Fig. 17, the current output is bounded at around 120 ± 5 A
with the self-tuning fuzzy PID controller and around 120 ±
20 A with the conventional PID controller. It is obvious that
the proposed controller achieves the better tracking response.
Fig. 18 shows the tuning-gains, Ktp, Kti and Ktd, considering
disturbance. After 2 s, the tracking error is bounded at
around zero but is not equal to zero; therefore, the
Fig. 19 Experimental results for developed GMAW.
tuning-gains still vary.
160
Fuzzy
PID
140
120
100
(a) Proposed system result
80
60
40
20
0
0
2
4
6
8
10
12
14
16
18
20
Time(s)
Fig. 17 Simulation results for developed GMAW with disturbance.
(b) Previous paper result (Chu and Tung 2005)
Fig. 20 Welding results for GMAW.
8
Phuc Thinh Doan, Pandu Sandi Pratama, Suk Yoel Kim, Hak Kyeong Kim, Hwang Yeong Yeun, Gi Sig Byun and Sang Bong Kim
6. CONCLUSION
This paper presented a new method for developing a
digital GMAW system. The PS-GMAW is a constant voltage
power supply. The schematic of the PS-GMAW was given. Its
controller was based on the incorporation of a microcontroller
and IC TL494. A constant arc current was maintained by
controlling the wire-feed speed. A self-tuning fuzzy PID controller was developed and successfully applied to the WFU. A
simulation and experiment were carried out to evaluate the
effectiveness of the proposed control method for the GMAW
system. The simulation and experimental results showed that
the self-tuning fuzzy PID could achieve good tracking in
comparison with the conventional PID controller.
7. ACKNOWLEDGEMENT
This work is a result of a “Human Resource Development
Center for Economic Region Leading Industry” project, supported by the Ministry of Education, Science & Technology
(MEST) and the Nation Research Foundation of Korea(NRF).
8. REFERENCES
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Abdelrahman M.A. (1998). “Feedback Linearization Control of
Current and Arc Length in GMAW Systems,” In
proceedings of the American control conference, Vol 3,
pp 1757-1761.
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Height Control of GMAW by Short Circuit Time Ratio,”
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Lincoln Electric Company (1997). MIG/MAG Welding Guide
for Gas Metal Arc Welding (GMAW), Third ed.
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