Ijrdtvlis26 1426106

International Journal For Research & Development in Technology
Volume: 2, Issue: 6, Dec -2014 ISSN (Online):- 2349-3585
Copyright 2014- IJRDT www.ijrdt.org
OPTIMIZATION OF WELD BEAD
GEOMETRY IN TIG WELDING OF
ALUMINIUM HYBRID COMPOSITE
USING RESPONSE SURFACE
METHODOLOGY
Gopinathan R1, Dinesh Kumar P2, Thamilpriya A3, Durga M4
1
Assistant Professor , 2 3 4
UG Scholar
1 2 3 4
SNS College of Technology,Coimbatore,India
Abstract -Aluminium based composites are widely used in
transport, aerospace and in many applications. This project
deals with welding of Aluminium matrix composite using
Gas Tungsten Arc Welding (GTAW).The process
parameters considered here include arc voltage (V),
welding current (I) and welding speed (S).The process
output characteristics include weld bead height (BH), bead
width (BW), and bead penetration (BP).The experiments
were conducted based on three levels such as (-1,0,+1) and
Box-behnken design with full replications technique. From
the experimental results, mathematical models were
developed to express in terms of three process parameters.
The adequacy of the model was evaluated using analysis of
variance (ANOVA) technique. The confirmatory tests were
also conducted to check the accuracy of the model. It was
observed that the percentage of error was in between 1 to 6
which indicates that the developed model was accurate.
Optimization using Response Surface Methodology has
been done to determine optimum process parameters to
obtain desirable weld bead geometry.
Keywords: TIG welding, Bead Geometry, Response
Surface Method.
INTRODUCTION
Composite materials are materials made from two or more
constituent materials with significantly different physical or
chemical properties which remain separate and distinct on a
macroscopic level within the finished structure. Composite
materials are formed by combining two or more materials that
have quite different properties. The different materials work
together to give the composite unique properties, but within
the composite you can easily tell the different materials apart
– they do not dissolve or blend into each other. Put more
technically, it has both good compressive strength and good
tensile strength. Composites exist in nature. Tungsten inert
gas (TIG) welding is a multi-objective and multi-factor metal
fabrication technique. Several process parameters interact
in a complex manner resulting direct or indirect
influence on weld bead geometry, mechanical
properties and metallurgical feature s of the weldment
as well as on the weld chemistry.
Basically, TIG weld quality is strongly characterized
by the weld bead geometry as shown in Figure 1.Bead
geometry variables; , bead width, bead height, penetration
are greatly influenced by welding process parameters; arc
voltage, welding current.
Fig. 1 Weld Bead Geometry
It is necessary to find an optional process condition capable of
producing desired weld quality. However, this optimization
should be performed in such a way that all the objectives
should fulfil simultaneously. Such an optimization technique is
called multi response optimization. Literature shows that
work has been explored on various aspects of modeling,
simulation and process optimization in TIG welding. In
this study, detailed analysis has been made to establish
relationships between welding parameters and weld bead
geometry and weld quality leading to an optimal process.
The Response Surface Method is very popular for
solving optimization problems in the field of
production engineering, The method utilizes a well-
balanced experimental design (allows a limited number of
experimental runs) called design matrix, , which serve the
objective function to be optimized (maximized) each factor
were selected. The RSM technique is then applied to
examine how the welding process factors influence the bead

Paper Title:- OPTIMIZATION OF WELD BEAD GEOMETRY IN TIG WELDING OF ALUMINIUM HYBRID COMPOSITE USING
RESPONSE SURFACE METHODOLOGY (Vol.2, Issue-6) ISSN(O):- 2349-3585
geometry; bead width (BW), bead height (BH),
penetration (P). An o p ti mal p ar amet er co mb i nat io n
w a s t h e n obtained. Through analyzing the design matrix, the
most influential factors for individual quality targets of
TIG welding process can be identified. Additionally,
the analysis of variance (ANOVA) was also utilized to examine
the most significant factors for the bead geometry in TIG
welding process.
RESPONSE SURFACE METHODOLOGY
Engineers often wish to determine the values of the process
input parameters at which the responses reach their optimum.
RSM is one of the optimization techniques currently in
widespread use in describing the performance of the welding
process and finding the optimum of the responses of interest.
When all independent variables are measurable, controllable
and continuous in the experiments, with negligible error, the
response surface can be expressed by
y = f (x1, x2… xk) ______________________(1)
k is the number of independent variables. To optimize the
response „„y‟‟, it is necessary to find an appropriate
approximation for the true functional relationship between the
independent variables and the response surface. Usually a
second-order polynomial Eq. (2) is used in RSM
. y = bo +∑biXi +∑biiX2ii+∑bijXiXj+ --------------- (2)
The test was designed based on a three factors-three levels
central box-behnken design with full replication. The TIG
welding input variables are arc voltage (V), welding current
(I), welding speed (S) as shown below in Table 1.
Table 1 Parameters and level
Parameter Notation Units
Factor levels
-1 0 1
Arc
Voltage V Volts 25 30 35
Welding
Current I Amps 90 120 150
Welding
Speed S Cm/min 24 36 48
A Box-Behnken design matrix shown in table 2 consisting of15
sets of coded conditions was selected to conduct the
experiments.
Table 2 Box-Behnken design matrix
Exp.No.
Arc
Voltage
(V)
Welding
Current
(A)
Welding
Speed
(m/min)
1 -1 -1 0
2 1 -1 0
3 -1 1 0
4 1 1 0
5 -1 0 -1
6 1 0 -1
7 -1 0 1
8 1 0 1
9 0 -1 -1
10 0 1 -1
11 0 -1 1
12 0 1 1
13 0 0 0
14 0 0 0
15 0 0 0
EXPERIMENTAL SETUP
In this project Al 2024 matrix composite, Aluminium alloy
2024 consists of mainly copper and magnesium as the
alloying elements. It is used in applications requiring high
strength to weight ratio, as well as good fatigue resistance. It is
not weldable, and has average machinability. Due to poor
corrosion resistance, it is often clad with aluminium or Al-
1Zn for protection, although this may reduce the fatigue
strength For welding, the material was made into specimens
with the dimensions of 140×25×6mm
RESULT AND DISCUSSION
The objective of the present study is to establish
relationships between the process parameters (inputs) and
process responses (outputs) in GTAW welding; using
the statistical regression analysis carried out on the data
collected as per Response surface method design of
experiments (DOE). The most important process
parameters in GTAW are the arc voltage (V); welding
current (I); and welding speed (S). The process response
characteristics considered are bead height (BH), Bead
Width (BW), Bead Penetration (BP). The levels for each
of the input parameters are given in Table 1. Therefore,
15 combinations of input process parameter are shown.

Table 3 Experimental design matrix and results
Exp.No. Arc Voltage
(V)
Welding Current
(A)
Welding Speed
(m/min)
Bead Width
(BW)
(mm)
Bead Height
(BH)
(mm)
Bead Penetration
(BP)
(mm)
1 25 90 0.36 5.843 1.193 3.284
2 35 90 0.36 8.178 1.856 3.375
3 25 150 0.36 8.760 1.870 3.196
4 35 150 0.36 8.146 1.874 3.210
5 25 120 0.24 7.236 1.437 2.986
6 35 120 0.24 9.660 2.179 2.680
7 25 120 0.48 5.894 1.582 3.125
8 35 120 0.48 6.265 1.101 3.653
9 30 90 0.24 9.147 1.815 3.394
10 30 150 0.24 7.580 1.455 3.128
11 30 90 0.48 6.168 1.308 3.720
12 30 150 0.48 8.946 1.657 2.607
13 30 120 0.36 6.480 1.749 2.990
14 30 120 0.36 6.125 1.535 2.721
15 30 120 0.36 6.192 1.712 2.680

Different regression functions (Linear, Linear plus
square and quadratic model) are fitted to the above data
a n d t h e coefficients values are calculated using
regression analysis. The best model is the most
fitted function to the experimental data. Such a model can
accurately represent the actual GTAW process. Therefore
In this research, the adequacies of various functions
have been evaluated using analysis of variance (ANOVA)
technique.
The model adequacy checking includes test
for significance of the regression model and test
for significance on model coefficients .The ANOVA
results recommend that the curvilinear model is the best fit
in this case. The Stepwise elimination process removes the
insignificant terms to adjust the fitted quadratic model.
The final proposed models are presented below:
The associated P-value for this model is lower than
0.05; i.e. α=0.05 or 95% confidence level. This illustrates
that the model is statistically significant.
Quadratic regression equations for bead width, bead
height, bead penetration are given below:
Bead Width,
BW = 25.987 + 0.08715 × (V) 0.2324 × (I) - 47.82 × (S) +
0.0153 × (V2) + 0.0012 × (I2) + 42.589 × (S2 ) - 0.00491 × (V)
× (I) - 0.855 × (V) × (S) + 0.3017 × (I )× (S)
Bead Height,
BH = -6.258 + 0.279 × (V) + 0.0158 × (I) + 13.841 × (S) +
0.000978× (V2) + 0.000009 × (I2) - 7.989 × (S2 ) -
0.00109× (V) × (I) - 0.509 × (V) × (S) + 0.0492 × (I )×(S)
Bead Penetration,
BP = 16.1319- 0.54312 × (V) - 0.05784 × (I) – 8.911 × (S) +
0.00736 × (V2) + 0.000316 × (I2) + 9.0277 × (S2 ) - 0.000128
× (V) × (I) + 0.3475× (V) ×(S) - 0.0588 × (I )× (S)
From the above equation,
V = Arc Voltage
I = Welding Current
S = Welding Speed
Correlation factor (R2) for each term of the three models.
Based on ANOVA, the values of R2 in quadratic model are
over 75% for all weld bead characteristics. This means that
this model provides an excellent representation of the actual
process in terms of BH, BW and BP responses equation.
Figure 2 shows TIG welding specimen. After welding,
specimen cut into required dimensions of 50 x 10 x 6 mm. for
analysis of weld bead geometry using profile projector
(Mitutoyo PJ-A3000). Weld bead specimen shows in Fig 3.
Fig. 2 TIG Welding specimen
Fig. 3 After welded specimen

Paper Title:- OPTIMIZATION OF WELD BEAD GEOMETRY IN TIG WELDING OF ALUMINIUM HYBRID COMPOSITE USING RESPONSE
SURFACE METHODOLOGY (Vol.2, Issue-6) ISSN(O):- 2349-3585
Table 4 Experimental Results & Predicted Results
Exp.No.
Bead Width
(mm)
Predicted Bead
Width (mm)
Bead Height
BH
(mm)
Predicted Bead
Height (mm)
Bead Penetration
BP
(mm)
Predicted Bead
Penetration
(mm)
1 5.843 5.918 1.193 1.332 3.284 3.410
2 8.178 8.522 1.856 1.893 3.375 3.530
3 8.760 8.417 1.870 1.832 3.196 3.040
4 8.146 8.071 1.874 1.735 3.210 3.083
5 7.236 6.979 1.437 1.307 2.986 3.164
6 9.660 9.135 2.179 2.151 2.680 2.828
7 5.894 6.418 1.582 1.609 3.125 2.976
8 6.265 6.521 1.101 1.230 3.653 3.475
9 9.147 9.328 1.815 1.805 3.394 3.089
10 7.580 8.179 1.455 1.621 3.128 3.105
11 6.168 5.568 1.308 1.141 3.720 3.742
13 6.480 6.265 1.749 1.665 2.990 2.797
14 6.125 6.265 1.535 1.665 2.721 2.797
15 6.192 6.265 1.712 1.665 2.680 2.797

Paper Title:- OPTIMIZATION OF WELD BEAD GEOMETRY IN TIG WELDING OF ALUMINIUM HYBRID COMPOSITE USING RESPONSE
SURFACE METHODOLOGY (Vol.2, Issue-6) ISSN(O):- 2349-3585
R-Sq(adj) 0.624 0.584
Fratio (calculated) 16.06 3.37
Fratio (from table)
(3,2,0.05) 19.16 19.16 19.16
Whether the model
is adequate?
Yes
Bead
width
(BW)
First-order terms
Sum of
9.6867 0.3577 0.4514
Degrees of
freedom (dof)
Mean square (MS) 3.2289 0.1192 0.1504
Second-order terms
Sum of
13.5681 0.662 0.7875
Degrees of
freedom (dof)
Mean square (MS) 2.2614 0.1103 0.1312
Error terms
Sum of
0.07172 0.02616 0.05671
Degrees of
freedom (dof)
Mean square (MS) 0.0356 0.01308 0.02836
Lack of fit
Sum of
1.7144 0.1322 0.3737
Degrees of
freedom (dof)
Mean square (MS) 0.5715 0.04407 0.1246
0.929 0.866 0.742
ANALYSIS OF VARIANCE
The purpose of analysis of Variance is to investigate
which welding parameters significantly affect the
performance characteristics. This is accomplished by
separating the total variability of the grey relational
grades, which is measured by the sum of the squared
deviations from the total mean of the grey relational
grade, into contributions by each welding parameters
and the error.
In addition, the F test was used to determine which
welding parameters have a significant effect on the
performance characteristic. Usually, the change of the
welding parameter has a significant effect on the
performance characteristic when the F value is large.
ANOVA results for overall weld bead geometry is shown in Table
5
Table 5 ANOVA test results
Result of the ANOVA indicates that the welding
speed is the most effective parameter on the responses
under the multi criteria optimization (higher penetration,
lower bead width, bead height). The percent contributions of
other parameters are arc voltage and current not more effective
compare than welding speed.
For illustrative purpose, the distributions of real data
around regression lines for quadratic model are illustrated in
Fig. to. These figures demonstrate a good conformability of
the developed models t the real process.
Fig. 4 Predicted values for BW vs. actual values

Bead Width
6.422 mm
Bead Height 1.238 mm
Penetration 3.464 mm
Fig. 5 Predicted values for BH vs. actual values
Fig. 6 Predicted values for BP vs. actual values
OPTIMIZED PARAMETER
From the above design of experiment results using
MINITAB software and the experimental values of the
response (Bead geometry characteristics) the optimized
parameter can be shown in Table 5 and 6.
Table 6 Optimized process parameter
Arc Voltage 35 Volts
Welding
Current
120 Amps
Welding Speed 48 c/min
Table 7 Optimized bead parameter
The optimized parameter given above can be the best
parameter condition for TIG welding of copper matrix
composite.
CONCLUSION
Response surface method (RSM) can be effectively used
to find optimum condition for TIG welding copper matrix
composite. Essential requirements for all types of welding are
deep penetration, lower bead width and bead height for
reducing weld metal consumption. This study has
concentrated on the application of response surface method
for solving multi criteria optimization problem in the field of
tungsten inert gas welding process.
The optimum condition of process parameter is found to
be V=35 Volts, I=120 Amps, S=48 cm/min.
The optimum condition of weld bead parameter is found
to be BW=6.422 mm, BH=1.238mm, BP= 3.464 mm.
REFERENCES
[1]. Jiang Qjnglei, Li Yajiang “Microstructure
characteristics in TIG welded joint of Mo-Cu composite and
18-8 stainless steel” Int. Journal of Refractory Metals & Hard
Materials 28(2010), Page no.(429-433).
[2]. Wang Xi-he, Niu Ji-tai “Investigation on TIG
welding of SICp-reinforced aluminium-matrix composite
using mixed shielding gas and Al-Si filler” Materials science
and Engineering A 499 (2009), Page no.(106-110).
[3]. Ugur Esme , Melih Bayramoglu “Optimization of
weld bead geometyin TIG welding process using grey
relation analysis and taguchi method” Original scientific
article MTAEC9 ,43(3) (2009), Page no. (143-149).
[4]. A.Kokangul,M.Bayramoglu “Mathematical
modeling for prediction and optimization of TIG welding
pool geometry” METABK 48(2) (2009), Page no. (109-112).
[5]. Manoj Singla, Dharinder Singh “Parametric
optimization of gas metal arc welding processes bu using
factorial design approach ” Journal of Minerals & Materials
Characterization & Engineering, Vol.9,No.4, (2010), Page
no. (353-363).
[6]. P.K.Palani, N.Murugan “Selection of parameters of
pulsed current gas metal arc welding”, Journal of Materia;
Processing Technology, Vol.No. 172, (2006), Page no.(1-10)
[7]. Jianjun WU, Guobin LI “Copper Matrix Composites
Reinforced with Nanometer Alumina Particle” Journal of
Materials science and Technology, Vol.15 No.2 (1999) Page
no. (143-146).

[8]. JIANG Long-tao, WU Gao-hui “Fabrication and
characterization of high dense Mo/Cu composites for
electronic packaging applications” Trans.Nonferrous
Met.Soc. China 17(2007) Page no. (580-583).
[9]. A. Włodarczyk-Fligier, L.A. Dobrzański
“Manufacturing of aluminium matrix composite materials
reinforced by Al2O3 particles” Journal of Achievements in
Materials and Manufacturing Engineering, Vol.27 (2008)
Page no. (99-102).
[10]. Viseslava Rajkovic, Dusan Bozic “Effect of copper
and Al2O3 particles on characteristics of Cu-Al2O3
composites” Materials and Design 31(2010) Page no. (1962-
1970)

Ijrdtvlis26 1426106

Ijrdtvlis26 1426106

Ijrdtvlis26 1426106

Recommandé

Contenu connexe

Tendances

Tendances(17)

En vedette

En vedette(18)

Similaire à Ijrdtvlis26 1426106

Similaire à Ijrdtvlis26 1426106(20)

Plus de Ijrdt Journal

Plus de Ijrdt Journal(18)

Dernier

Dernier(20)

Ijrdtvlis26 1426106