report122

HALDIA INSTITUTE OF
TECHNOLOGY
Project report on…
OPTIMIZATION OF TIG WELDING POOL GEOMETRY
A computer simulated accurately predicts optimization of TIG welding
GUIDED BY… BY:
Md.Sahariat Hossain SUJAY KUMAR PATAR , ARUNANSHU BASU , MINTU RAJAK,,
OM PRAKASH DAS , YOGESH KUMAR JAIN
DEPAETMENT OF MECHANICAL ENGINEERING
HALDIA INSTITUTE OF TECHNOLOGY,HALDIA
PURBA MEDINIPUR-721657,WEST BENGAL
2014-2015

INTRODUCTION
1.1 Different type of welding process
1.What is Welding

INTRODUCTION-TIG
In this work, nonlinear and multi-objective mathematical models were developed to determine the
process parameters
corresponding to optimum weld pool geometry. The objectives of the developed mathematical
models
are to maximize tensile load (TL), penetration (P), area of penetration (AP) and/or minimize heat
affected
zone (HAZ), upper width (UW) and upper height (UH) depending upon the requirements.
TIG welding is an arc-welding process that produces
coalescence of metals by heating them with an arc
between a non-consumable tungsten electrode and the
base Metal. TIG weld quality is strongly characterized by
the weld pool geometry as shown in Figure. This is
because the weld pool geometry plays an important role in
determining the mechanical properties of the weld.

CLASSIDFICATION TREE OF WELDING

TIG welding is a highly non-linear, strongly coupled,
multivariable process . The weld pool geometry
and, hence, the quality of TIG welded joints are
greatly dependent on the selection of input control variables
such as welding speed (V), welding current (I),
shielding gas flow rate (F) and gap distance (G).
The independently controllable parameters affecting weld pool geometry and the quality of
the weld pool V, I, F and G were selected as input control variables. It is possible to present
the quality of welding geometry with the TL, P, AP, HAZ, UW and UH. These parameters are
important weld quality parameters and all of them are considered in this study.

let us maximize TL under the upper and lover limit of the input control variables
indicated with “U” and “L” indices, respectively.
Maximizing TL
Constraints:
VL<= V <= VU
IL<= I <= IU
FL<=F<=FU
GL<=G<=GU
By the same way four models are constructed for the rest of the weld pool quality
parameters. In same cases the engineer would like to consider a few objectives
simultaneously. Let’s in the next model maximize TL and minimize HAZ
simultaneously. To construct the necessary multi-objective model the following
procedures are applied:
Step 1.Find the maximum level of TL (TLopt).
Step 2.Find the minimum level of HAZ (HAZopt) .

The design matrix should be depending on the upper and the lower limits of the predetermined input
control variables. The selected design matrix is a four-level, four-factor, central composite rotatable
response surface design consisting of 90 sets of design matrix. It comprises response surface design
(RSD) plus 18 center points. All welding variables at their intermediate level (0) constitute the center
points. The upper limit of a variable was coded as +2 and the lower limit as –2. The coded values for
intermediate values were calculated from the rotatable central composite design of Design Expert 7.0
as given in Table1
There is a problem of actually conducting experiment due to arranging the setup so we, belive in site
www.millerwelds.com. There is welding calculator, we chose stainless steel
Generally AISI type 304 stainless steel plates of 4.8 mm(3/16 inch)

MATHEMATICAL RELATIONSHIPS
The suitable mathematical relationship such as a second- degree response surface quadratic
model (seen below) should be selected for each of the process quality parameters according to
the experimental results.
Y = b0 + b1V + b2I + b3F + b4G + b11V2+ b22I2 + b33F2 + b44G2+b12VI + b13VF
+b14VG+b23IF+b24IG+b34FG
where “b” values are the coefficients of the models. These values can be calculated using
Design Expert 7.0software

OUTPUT RESULT
HAZ=4,2573-2,2532V+0,0781I+0,027766F + 0,1975G+0,3520V2-0,000124I2– 0,001384F2-0,021331G2
-0,006771VI–0,001035VF+0,000108VG-0,000127IF+0,001067IG-0,004653FG
TL=9,80665+238,03487V+8,1026I+25,05345F+4,03510G-75,10057V2-0,039932I2-1,78092F2
-7,09131G2-0,059916VI+3,34043VF+0,73102VG-0,041339IF+0,04571IG+1,17361FG
UW=3,30265+0,44806V+0,089617I+0,20048F+0,074331G-0,21720V2–0,000128I2-0,015592F2–0,004656G2-
0,011222VI+0,008071VF-0,027623VG+0,000131IF+ 0,000763IG+0,010625FG
UH=0,083160-0,14708V+0,004271I+0,026577F-0,039542G+0,029239V2–0,000001237I2
-0,00090246F2+0,0029819G2-0,00076736VI-0,0011008VF+0,00603239VG-0,00008549IF–
0,0001254IG+0,001736FG
P=0,64397+0,066087V+0,006967I+0,018256F+0,087894G-0,019590V2–0,000039I2-0,000551F2
-0,014174G2+0,000502VI-0,003323VF-0,020984VG+0,000052IF+ 0,000333IG-0,001458FG
AP=3,94256-0,19505V+0,084155I+0,21323F+0,46969G-0,019710V2
–0,000126I2-0,012660F2-0,10079G2-0,013011VI-0,020638VF-0,048323VG+0,0005887IF+0,000453IG
+0,012111FG

The constructed nonlinear mathematical model (maximizing TL (1) under 2-5 constraints) can
be constructed as follows using the determined upper and lower limits of the input control
variables (as seen in Figure 1).
Maximizing TL,
1,07 ≤ V ≤ 3,55
20 ≤ I ≤ 150
8 ≤ F ≤ 12
1 ≤ G ≤ 4

Objective Input process control variables Optimal Values
V
mm/s
I
A
F
I/min
G
mm
TL
N
HAZ
mm
UW
mm
UH
mm
P
mm
AP
mm
2
Max TL 1,73 96,59 8 1 12963,2 6,93 10,02 0,27 1,26 9,67
Min HAZ 3,41 20 12 1 8666,5 1,92 3,92 0,04 0,81 3,22
Min UW 3,55 20 12 1 8331,0 1,93 3,75 0,05 0,79 3,11
Min UH 2,81 20 8 2,04 9721,1 2,21 4,95 0,02 0,86 4,47
Max P 1,07 117,3 12 3,07 11688,6 9,42 12,08 0,30 1,34 11,43
Max AP 1,07 150 12 1 11036,2 10,27 13,08 0,39 1,21 12,86
Table 4. Results of mathematical models
Multi objective nonlinear models
Max TL
Min HAZ
2,41 20 9,41 1 10375,6 2,30 5,15 0,05 0,91 4,81
Max TL
Min HAZ,UW
2,72 20 12 1 9901,0 2,09 4,64 0,03 0,88 4,03
Max TL
Min HAZ, UW,
UH
2,99 20 12 1,17 9488,6 2 4,40 0,03 0,85 3,70
Max TL, P
Min HAZ, UW,
UH,
2,69 20 8 1,62 9969,5 2,22 5,03 0,03 0,88 4,61
Max TL, P, AP
Min HAZ,UW,
UH,
2,49 21,02 8 2,19 10162,5 2,45 5,31 0,03 0,90 4,94

TIG WELDING MECHANISM PRESENTED BY
DIAGRAM

CONCLUSIONS
A systematic approach has been ships between input control variabledeveloped and
employed in this study for the optimization problem of the TIG welding process parameters.
The mathematical relations and weld pool quality parameters are obtained using the
results of experiments. Six nonlinear and five multi-objective mathematical models are
constructed and solved under the predetermined limits of the input control variables using
the obtained mathematical relationships as objective functions. This developed systematic
approach can also be adopted for other type of arc welding processes.

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