1. INTERNATIONAL6359(Online)Engineering and 2, March - April ENGINEERING
International Journal of Mechanical
6340(Print), ISSN 0976 –
JOURNAL OF4,MECHANICAL (2013) ISSN 0976 –
Volume Issue
Technology (IJMET),
© IAEME
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online) IJMET
Volume 4, Issue 2, March - April (2013), pp. 79-85
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EFFECT OF THE WELDING PROCESS PARAMETER IN MMAW
FOR JOINING OF DISSIMILAR METALS AND PARAMETER
OPTIMIZATION USING ARTIFICIAL NEURAL FUZZY INTERFACE
SYSTEM
U.S.Patil1, M.S.Kadam2
1
(PG Student, Mechanical Engineering Department, Jawaharlal Nehru Engineering College,
Aurangabad, India)
2
(Professor and Head of Mechanical Engineering Department, Jawaharlal Nehru Engineering
College, Aurangabad, India)
ABSTRACT
In this research work, the optimization of welding input process parameters for
obtaining greater weld strength with optimum metal deposition rate welding of dissimilar
metals like stainless steel and Mild steel is done. The process used for welding is Manual
Metal Arc welding and dissimilar metal used are low carbon steel and Stainless steel.
Welding speed, voltage, current, electrode angle are taken as controlling variables. The weld
strength (N/mm2) and Metal deposition rate (gms) are obtained through series of experiments
according to Central Composite Design to develop the equation. Experimental results are
analyzed through the Artificial Neural Fuzzy Interface System and the method is adopted to
analyze the effect of each welding process parameter on the weld strength and Metal
Deposition Rate, and the optimal process parameters are obtained to achieve greater weld
strength. Validation of results obtained by Artificial Neural Fuzzy Interface System is done
by using Experimental method.
Keywords: Artificial Neural Fuzzy Interface System, Metal Deposition rate, Manual Metal
Arc Welding, Response Surface Methodology, Weld strength.
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
I INTRODUCTION
In high pressure boilers, alloy materials are used for making the super heater
and economizer. The cost of alloy steel is very high and hence, in order to reduce the
cost, the alloy steels may be combined with carbon steel. Hence, cost reduction is the
main objective together with a better quality weld, so we use dissimilar metals
welding. A better quality weld in dissimilar metal welding is obtained by optimizing the
process parameters because they play a vital role in deciding the weld strength. Some
important parameters are welding current, welding voltage, welding speed, arc length, type
of electrode etc. These parameters can be selected based on screening experiments.
Sivakumar et al [1] [8] .-proposed the optimization of the process parameters for MMA
welding of stainless steel and low carbon steel with greater weld strength has been reported.
The higher-the-better quality characteristic is considered in the weld strength prediction. The
Taguchi method is adopted to solve this problem. The experimental result shows that the
weld strength is greatly improved by using input parameters welding speed (353 mm /min),
current (100 amps), voltage (30 volts). Mukhtar et al [2] – developed experimental work and
developed ANN model for prediction of weld bead geometry in gas tungsten arc (GTA)
welding confirm that ANN tool can be fruitfully applied in modeling and predicting complex
and nonlinear manufacturing processes with fair deal of accuracy. The effects of weld current
and weld speed are highly significant on bead geometry parameters. The effect of welding
voltage is moderately significant, while that of gas flow rate in insignificant. Mustafa et al [3]
-describes prediction of weld penetration as influenced .by FCAW process parameters of
welding current, arc voltage, nozzle-to-plate distance, electrode-to - work angle and welding
speed. Optimization of these parameters to maximize weld penetration is also investigated.
The optimization result also shows that weld penetration attains its maximum value when
welding current, arc voltage, nozzle-to-plate distance and electrode-to-work angle are
maximum and welding speed is minimum Srinivasa Rao et al [4] -focuses on studying the
influence of various Micro Plasma Arc Welding process parameters like peak current, back
current, pulse and pulse width on the weld quality characteristics like weld pool geometry,
microstructure, grain size, hardness and tensile properties. The results reveals that the usage
of pulsing current, grain refinement has taken place in weld fusion zone, because of which
improvement in weld quality characteristics have been observed. Rati Saluja et al [5] - deals
with the application of Factorial design approach for optimizing four submerged arc welding
parameters viz. welding current, arc voltage, welding speed and electrode stick out by
developing a mathematical model for sound quality bead width, bead penetration and weld
reinforcement on butt joint. Kumanan et al [6] - details the application of Taguchi Technique
and regression analysis to determine the optimal process parameters for submerged Arc
welding (SAW). Multiple regression analysis is conducted by using statistical package
software and mathematical model is build to predict the bead geometry for any given welding
conditions. Result shows welding current and arc voltage are significant welding process
parameters that affect the bead width. Saurav Datta et al [7] - considers four process control
parameters viz. voltage (OCV), wire feed rate, and traverse speed and electrode stick-out.
The selected weld quality characteristics related to features of bead geometry are depth of
penetration, reinforcement and bead width. This model was optimized finally within the
experimental domain using PSO (Particle Swarm Optimization) algorithm. The weld quality
improvement is treated as a multi-factor, multi-objective optimization problem.
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3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
II ARTIFICIAL NEURAL FUZZY INTERFACE SYSTEM METHOD OF
OPTIMIZATION
Artificial Neural Fuzzy Interface system is integration both neural networks and fuzzy
logic principles, it has potential to capture the benefits of both in a single framework Neural
framework.
network in general is a highly interconnected network of a large number of processing
elements called neurons in an architecture inspired by the brain”, as shown in figure 1. Neural
architecture
networks exhibits characteristics such as mapping capabilities or pattern association,
generalization robustness, fault tolerance and parallel and high speed information processing.
Neural networks learn by examples, they can, therefore be trained with known examples of a
known
problem to acquire knowledge about it, once appropriately trained, the network can be put to
effective use in solving unknown and or untrained instances of the problem. Neural networks
adopt various learning techniques of which supervised learning and unsupervised learning
Figure -1 Neuron and Artificial Neuron
Neural network is composed of a large number of highly interconnected processing
elements (neurons) working in unison to solve specific problems, information sharing takes
place across the synapses. Neural networks process information in a similar way the human
brain does. The disadvantage is that because the network finds out how to solve the problem
by itself, its operation can be unpredictable. On basis of this neural network, concept of
twork,
artificial neural network is introduced which mainly consist of inputs, weights, threshold or
summation and output neurons, model is introduced by scientist McCullough-Pitts
Pitts.
III DISSIMILAR METALS JOINING BY MMA WELDING PROCESS
In the manual metal arc (MMA) welding process, a 3.15 mm diameter consumable
he
stainless steel 309 L Grade electrode is used to strike an electric arc with the base metal. The
heat generated by the electric arc is used to melt and join the base metal. In this st study an
MMA welding machine is used to weld the base plates of 304 Stainless Steel and Mild Steel.
The chemical composition of Mild steel is given in Table 1 and for Stainless steels given in
Table 2. Two plates of size 150mm x 63 mm x 5 mm are tacked together to form a weld pad
mm
of 300 mm x 63 mm x 5mm .Welding is carried out in the down hand position and beads are
laid along the weld pad centerline to form a butt joint. The plates are allowed to cool to room
temperature, after the completion of welding. As shown in figure 2
tion
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4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
Table 1 Chemical Composition of Mild Steel
Composition Carbon Manganese Silicon Sulphur Phosphorous Aluminium
% 0.16 0.30 0.25 0.030 0.030 0.02
Table 2 Chemical Composition of Stainless Steel 304
Composition Carbon Manganese Silicon Sulphur Phosphorous Aluminium
% 0.0195 1.7153 0.2884 0.00086 0.0282 0.006
A measurement of the tensile strength is performed by using an ultimate t
str tensile testing
(UTM) machine. Metal deposition rate is measured by measuring weight of work piece
ne.
before welding and after welding.
SS 304 ELECTRODE 309 L
PLATE
150X63X5
DOUBLE BUTT
WELDED JOINT MS 150X
63X5 mm
Figure 2 Manual metal arc welding set up
The independently controllable process parameters affecting the weld strength and
Metal deposition rate were identified to enable the carrying out of experimental work and
developing the mathematical model. These are welding current (I), weldin speed (S),
welding
welding voltage (V), electrode angle (A). The Design of experiment is done by using
Response surface Method. Experimental results are analyzed through the Artificial Neural
Fuzzy Interface system. Factor and their operating level are shown in Table 3
Table 3 Factor And Operating Level
S. Level
Factor Unit
no. Low High
1 Welding Current Amp 80 120
2 Welding Voltage Volt 360 420
3 Welding Speed mm/min 120 240
4 Electrode Angle Degree 30 150
Experimental runs are planned by using DOE table of RSM using central composite method.
Total numbers runs are 31 for 4 factors with 5 operating levels.
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5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
IV MATHEMATICAL MODELING
Mathematical modeling is done by using Regression for each response (Metal deposition
rate and welding strength) The regression equation is
Metal Deposition (Gms) = 12.0 + 0.175 Welding Current (Amp)- 0.0167 Welding Voltage
(volts) - 0.0562 Welding Speed (mm/min) + 0.0167 Electrode Angle
Predictor Coef SE Coef T P
Constant 11.978 5.546 2.16 0.040
Current 0.17500 0.02514 6.96 0.000
Voltage -0.01667 0.01257 -1.33 0.196
Speed -0.056250 0.006284 -8.95 0.000
Angle 0.016667 0.008379 1.99 0.057
S = 1.231 R-Sq = 83.8% R-Sq(adj) = 81.3%
Welding Strength (N/mm2) = - 76.6 + 4.71 Welding Current (Amp) + 0.103 Welding
Voltage (volts) - 0.316 Welding Speed (mm/min)
- 0.102 Electrode Angle
Predictor Coef SE Coef T P
Constant -76.60 47.40 -1.62 0.118
Current 4.7067 0.2148 21.91 0.000
Voltage 0.1033 0.1074 0.96 0.345
Speed -0.31583 0.05371 -5.88 0.000
Angle -0.10222 0.07161 -1.43 0.165
S = 10.52 R-Sq = 95.2% R-Sq (adj) = 94.5%
V OPTIMIZATION
Optimization of process parameter is done by using Artificial Neural Fuzzy Interface
System tool box in Matlab. Network in trained by using 31 data set obtained from experimental
work and testing is done by using 15 data sets.
Figure 3 Training and Testing of network Figure 4 – Network for Metal Deposition Rate
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6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
Figure 5 Rules for prediction of metal deposition rate
Figure 6 Rules for prediction of weld strength
VI RESULTS AND DISCUSSION
Mathematical modeling for Metal deposition rate (Gms) is done by using Regression
with Minitab 14 software and result gives R2 value as 84% indicating significance of model.
For determining Metal deposition rate, welding current, welding speed and electrode angle are
most significant (as p < 0.05) while welding voltage is less significant. Similarly
mathematical modeling of welding strength gives R2 value as 95% indicating significance of
model. Welding strength is significantly affected by welding current and welding speed
While doing optimization of process parameter by using ANFIS method, once the network is
trained by using training data, network is tested by using testing data set. Figure 3 show the
training and testing of network. Artificial neural network is architected by using Matlab 7,
shown in figure 4. On building network, rules for predicting output is developed by system,
figure 5 and 6 respectively shows the rules for predicting the metal deposition rate and weld
strength. Results obtained by using ANFIS are validated by doing the experimental runs.
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7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
CONCLUSION
In this paper, the optimization of the process parameters for MMA welding of stainless
steel and mild steel with greater weld strength and optimum metal deposition has been
reported. The higher-the-better quality characteristic is considered in the weld strength
prediction. The Artificial Neural Fuzzy Interface system is used to solve this problem. The
experimental result shows that the weld strength can be controlled, according to demand by
setting the input value predicted by ANFIS system.
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