- Email: [email protected]

ScienceDirect Materials Today: Proceedings 5 (2018) 12886–12900

www.materialstoday.com/proceedings

ICMMM - 2017

Finite element simulation and optimization of orbital welding process parameters Ruparekha patro a *, Sharad K. Pradhanb, b

a Research Scholar, National Institute of Technical Teachers’ Training and Research, Bhopal 462 002 (M.P.), India Associate Professor, Mechanical Engineering, National Institute of Technical Teachers’ Training and Research, Bhopal 462 002 (M.P.), India

Abstract Orbital pipe welding is a welding method where the welding head rotates around a fixed vertical or horizontal pipe in orbital welding with an arc is rotated mechanically through 360° around a static work piece such as a pipe in a continuous process. Welding parameters play a vital role in joining of work piece using welding. In this research effort, combination of experimental and numerical approach is used in order to simulate and estimate optimum process conditions for orbital pipe welding process. The motive of this research effort is to simulate Orbital Pipe welding process and to evaluate the effect of various welding process parameters such as current, voltage, orbital velocity on performance parameters like temperature distribution, residual stresses and Tensile strength of welded pipes. To estimate optimum process parameter by experimental method is quite complicated, therefore simulation technique is used to predict the behaviour of selected process and performance parameters. In this paper, Orbital welding process is simulated using Finite Element Approach with the help of ANSYS mechanical APDL 14.5 analysis software. A three-dimensional finite element model assuming a Gaussian distribution heat source has been used to investigate the temperature distribution and residual stress distribution. Selected welding process parameter i.e. values of temperature of both simulated and experimental approaches are compared to validate the simulated model. The effect of input parameters like welding current, voltage, welding speed, gas flow rate on performance parameter temperature has been investigated and found that the peak temperature sharply increases with the above input parameters. The result of this research effort indicates that the developed models are capable of predicting the responses with negligible errors. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

Keywords: finite element method, ANSYS, ANOVA, DOE, 304 stainless steel, Taguchi Method

1. Introduction Orbital welding was first used in the 1960's when the aerospace industry recognized the need for a superior joining technique for aerospace hydraulic lines. Orbital welding is a specialized area of welding whereby the arc is rotated mechanically through 360° (180 degrees in double up welding) around a static work piece, an object such as * Corresponding author. Mob: +91-896-408-1089. E-mail address: [email protected] 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

12887

a pipe, in a continuous process. The process is used specifically for high quality repeatable welding. The name orbital welding comes from the circular movement of the welding tool around the work piece. The main components of every orbital welding system are the power source and controller, the welding head and, where required, a wire feed mechanism. There are a large number of factors that can have an influence on the welding result. These aspects include the arc length, magnitude and pulse frequency of the welding current, welding speed, inert shielding gas, parent material, filler material, weld preparation, and thermal conductivity. The primary and preferred technique used in orbital welding is the TIG (Tungsten Inert Gas) method. It allows for the use of cold-wire feed and nonconsumable electrodes which are preferred in some forms of orbital welding. This method also allows for the use of many different types of metal to be welded such as carbon steels, nickel alloys, titanium, and copper among others. The equipment of orbital pipe welding comprises of Power supply, Weld head, Controller and Feed mechanism. 2. Methodology 2.1

Finite element method

Finite element method (FEM) is a numerical technique for finding approximate solutions to boundary value problems with the help of differential equations. It uses variational methods (the calculus of variations) to minimize an error function and produce a stable solution. Analogous to the idea that connecting many tiny straight lines can approximate a larger circle, FEM encompasses all the methods for connecting many simple element equations over many small subdomains, named finite elements, to approximate a more complex equation over a larger domain. Engineers, scientists, and mathematicians mostly use FEM to obtain solutions to the differential equations that describe, or approximately describe a wide variety of physical (and non-physical) problems. The method states that a complicated domain can be sub-divided into a series of smaller regions in which the differential equations are approximately solved. By assembling the set of equations for each region, the behavior over the entire problem domain is determined. Basic steps of finite element method are 1. Select suitable field variable and the type of element. 2. Disctritize the continua. 3. Select interpolation function. 4. Find the element properties. 5. Assemble element properties to get global properties. 6. Impose the boundary conditions. 7. Solve the system equations to get the nodal unknowns. 8. Make the additional calculation to get the required values 2.2 Design of experiment Design of Experiment is a statistical technique in which a series of tests occurs to define the purposeful changes made to the input variables of a system or process and the effects on response variables are noted. Experimental design is an effective tool for maximizing the amount of information gained from a study while minimizing the amount of data to be collected. Steps to apply DOE are 1. Experiment planning and problem formulation 2. Experiment layout 3. Data analysis 4. Interpretation of results 2. 3 Taguchi approach Taguchi is a Japanese statistician improves a robust design method is also called Taguchi method which greatly improves engineering process and productivity. Taguchi method is a concept developed base on the optimization through design of experiments, in which, experiment will be carried out and the value of quality is very much significant to discipline. The ideas of Dr. Taguchi also focused for quality improvement techniques upstream to the design phases of the product life cycle and as well as manufacturing phases of the product life cycle. Taguchi

12888

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

method uses fractional factorial experimental designs that use least number of experimental run. The experimental results are transformed into a signal to noise (S/N) ratio. Taguchi recommends the use of S/N ratio to measure the quality characteristics deviating from the desired values. The S/N ration for each level of process parameters is computed based on the Signal to Noise analysis. There are 3 Signal-to-Noise ratios used for optimization of Static Problems; Smaller is Better ( Where,

)

= −10log(

[1]

) (1

= ∑

)

[2]

Above formula is used to define the S/N ratio for all undesirable characteristics like defects etc. for which the ideal value is zero. Also, when an ideal value is finite and its maximum or minimum value is defined, then the difference between measured data and ideal value is expected to be as small as possible. Larger the Better )

( Where,

= −10log( = ∑

)

(1

[3]

) [4]

This case has been converted to smaller-the-better by taking the reciprocals of measured data. Normal is Best ( Where,

)

= −10log( = ∑

) (1

)

[5] [6]

This case arises when a specified value is MOST desired, meaning that neither a smaller nor a larger value is desirable. R = Number of repetition 2. 4 Analysis of variance (ANOVA) Sir Ronald Fisher introduced the Analysis of Variance (ANOVA) in the year 1930 and used it for agriculture experiment. ANOVA is a statistical based, objective decision-making tool used to interpret experimental data and make the necessary decisions as well as detecting any differences in average performance of groups of items tested. ANOVA is a mathematical technique which breaks total variation down into accountable sources. 2. 5 Orbital welding formula Amount of heat given to the work piece per unit time is: = ƞ Where, I= current intensity V = voltage Ƞ = Arc efficiency

[7]

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

Fourier’s Law The heat flux vector is defined by Fourier's law for isotropic material = − Where, k = temperature-dependent thermal conductivity matrix ∇T = temperature gradient Taking initial and final boundary condition, T=T0 and T=Tf

12889

[8]

Generalized equation for Thermal Analysis For a isotropic, conductive material with equal coefficient of conductivity kx, ky, kz (W/mK) in all three chosen orthogonal co-ordinates, equation given below shows the heat energy in the weld area with temperature, T (K) obtained both in spatial, x, y, z (m) and temporal, t(sec), Below eq gives 3 dimensional heat conduction equation

( )

+

Y

( )

T Y

+

Z

( )

T Z

+

=

( )

( )

T

[9]

Where, T = temperature, k(T) = thermal conductivity ρ (T) = specific mass Cp(T) = specific heat Qv = volumetric heat flux. The heat flow density for convection (qc) in the environment gas or liquid is given by Newton’s heat transfer law: gives heat convection equation. = ( )ℎ ( − ) [10] Gaussian Heat Distribution ƞ ( )= −( ) [11] ϭ ϭ Where, Q (r ) = surface flux at radius r Ƞ = efficiency coefficient/ Arc efficiency Ϭ = radial distance from centre FiniteElementFormulation ʃ wῥc ∂T dv + ∂ Ω ʃwῥLf1 dv + Ω ʃ k δw. δT dv + ∂Ωq ʃ wqds + ∂t ∂t ʃ wh (T – T ) ∂Ωc env env ds – Ω ʃ wq dv = 0 Where, W = weighting function. In the finite element context, T is approximated as a linear combination of interpolation functions Ni(x,y, z) = the shape functions T = vector of unknown nodal temperatures, C = capacity matrix, L = latent heat vector, K = conductivity (stiffness) matrix and F = external flux vector. F1 = Characteristic function of temperature Q = Heat input Ω = Discretization of the analyzed domain Ω

[12]

12890

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

3.0 Numerical study: 3D simulated environment for Orbital Pipe Welding Process FEM is used, to simulate the orbital pipe welding process assuming a Gaussian distribution heat source so as to investigate the temperature distribution and residual stress distribution. 3. 1 Material properties The 304 stainless steel is used as the weld material for the optimization of process parameter, elastic modulus E= 190 GPa,Poisson’s ratio, γ=0.33, mass density ρ= 7600 Kg/m3, thermal conductivity = 17 W/m2k Heat is applied at the pipe joint to perform the necessary weld joint. A Fusion process is used to simulate the welding process parameters. The chemical composition of AISI 304 stainless steel is given in table 1. Table 1: Chemical composition of AISI 304 stainless steel

Material AISI 304 stainless steel

C 3.28

Si 3.34

Cr 14.89

Mn 1.07

Fe 53.54

Ni 5.2

Tb 18.6

3. 2 Boundary Condition In these steps required loads and boundary condition are applied to achieve the required output. Boundary condition: fixed at both the end in Y and X axis, Convection: 25w/m2k and apply Heat flux value at the joint. 3. 3 Element Type Selection of suitable element types according to the material and design of welding are made to analyse the material correctness. Element type: 8 node brick 45. Solid 45 is used for 3D modelling of solid structure. 3..4 Mesh generation The basic principles of finite element analysis theory, the smaller mesh element size gives more accurate results of an analysis. If the mesh element sizes infinitely small, the theoretical model will approach the optimal solution. However, this is only a speculation. In the analysis process, when elements are too small, element meshing will generate too many elements, nodes and freedom for the model in general. This increases computational intensity, resulting in a model that is either too time-consuming to solve, or potential errors in values. Thus, reasonable mesh element size (number of elements) is a factor that should be considered in a finite element analysis. Meshing follows several steps like element selection, defining material property, mesh size to generate mesh in cad model. In the present analysis 12482 numbers of elements with 18517 nodes are used for the model and hexahedral mesh type is used as shown in figure 1

Figure 1 Meshing part of 3 -D structural model

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

12891

3.5 Thermal Analysis

To start the simulated study electrical input (Voltage, Current) should be converted into thermal input. To achieve this Gaussian heat distribution is assumed and calculated using following equation for 27 selected experimental sets.

Q(r)=ƞVIe-(r2/2ϭ2) 2Πϭ2

[13]

Where Q (r ) = surface flux at radius r Ƞ = efficiency coefficient/ Arc efficiency V = voltage I = current Ϭ = radial distance from center 3. 6 Post processing: viewing the results After defining the geometry, type of element, mesh size, boundary condition and solving and post processing (as shown in table 2) has been performed to get residual stresses and temperature plot for all the 27 cases, this gives the simulated residual stress and temperature value by applying Gaussian heat flux value. Simulated residual stress and temperature plot for different cases were shown in table 3 and table 4 respectively. Table 2: Post processing results Exp. No.

ANSYS temperature (K)

Analytical Heat flux (J/mm)

ANSYS residual stress (Mpa)

1

1388.1

566

250

2 3

1033.2 823.5

425 386

992 537

4

715.7

850

474

5

1537.4

637.5

535

6

900.04

579.5

575

7

2751.04

1133

1500

8

2041.5

850

1810

9

1191.72

772.7

709

10

2410.2

991.6

1320

11

1789.4

743.7

1610

12

1045.8

676.13

648

13

3602.9

1487.6

1950 2320

14

2671.6

1115.7

15

1556.32

1014.3

908

16

4795.5

1983.3

3010

17 18

3553.8 2066.7

1487.5 1352.2

2600 1150

19

3432.5

1416.66

1180

20

2545.6

1062.5

1020

21

1483.4

965.9

625

22

5136.32

2125

2790

23

3805.9

1593.7

1820

24

2212.6

1448.86

1220

25

6840.1

2833

5490

26

5066.26

2125

1020

27

2941.8

1931.8

1620

12892

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

Table 3: Residual stress plot from ANSYS

Case 1 Current: 100 A Voltage: 10 V Gas flow rate: 9 Speed: 1.5 m/sec Standoff distance:2mm Max. Residual Stress:992mpa Min. Residual Stress:110mpa

Case 2 Current :100A Voltage :10v Gas flow rate : 9 Speed :1.5 Standoff distance : 3 Max. Residual Stress:164mpa Min. Residual Stress:537mpa

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

Case 3 Current :100 Voltage :15 Gas flow rate : 11 Speed :2 Standoff distance : 1 Max. Residual Stress:137mpa Min. Residual Stress:474mpa

Case 4 Current :100 Voltage :15 Gas flow rate : 11 Speed :2 Standoff distance : 3 Max. Residual Stress:675mpa Min. Residual Stress:180mpa

12893

12894

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

Table 4: Temperature plot from ANSYS

Case 1 Current: 100A Voltage: 10v Gas flow rate: 9 Speed: 1.5 Standoff distance: 2 Max. Temperature:1388K Min. Temperature:24.59K

Case 2 Current:100A Voltage:10v Gas flow rate: 9 Speed:1.5 Standoff distance: 3 Max. Temperature:1033K Min. Temperature:137K

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

12895

Case 3 Current:100 Voltage:15 Gas flow rate: 11 Speed:2 Standoff distance: 1 Max. Temperature:1537K Min. Temperature:193K

Case 4 Current: 100 Voltage: 15 Gas flow rate: 11 Speed: 2 Stand off distance: 3 Max. Temperature:1789 Min. Temperature:221

4.0 Experimental study Using ‘Design of Experiments’ approach, significant sets of input variables are identified for experimentation. This section discusses about the procedure adopted to carry out the experiments using orbital welding process with 27 selected sets of input parameters. 4.1.

Orbital welding machine

Welding is carried out with ORBIWELD head 115 of “S’’ series. This machine is available in MECHZEAL INTERPHASING Pvt. Ltd., satellite area, Ahmedabad. They allowed doing all the necessary experiments. They also provided the testing facilities like tensile test on the welded specimen to find the tensile strength of welded piece. The complete setup during orbital pipe welding shown in figure 2

12896

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

Figure 2: Image during orbital welding of pipe

Specification of welding machine shown in table 5 and power source were selected as per the required input parameter. Table 5: Specification of open head orbital welding Design Tube outer diameter (mm) Weight of weld head (kg) Max Weight (kg) Type of power supply Voltage (volt) Current (Amp) Power on time Cooling system Cooling capacity (watt) Water tank capacity Speed (rotation/min)

4.2

ORBIWELD HEAD CA-75M 20-75 13 18 ORBITRON 6000 400v +/-15% 0-300A 40% of current Water cooled system 814 61 0.2-1

Selection of Input and Output variables

In this study L27 (35) orthogonal array and robust design are implemented in the experiment on the basis of TAGUCHI method as shown in table 6. After comprehensive study of literature available, internet suffering, industry feedback and discussion with practicing technicians it is found that five process parameters viz. welding current, voltage, welding speed, gas flow rate, and standoff distance are most dominating and affects the performance parameter of orbital welding process significantly. The selected input parameters with 3 levels ( [4] Dinesh et. al., [5] M. islam et. al., [13] lakshman et. al.) each are mentioned in. L27 orthogonal array.is used in the investigation to achieve an optimal performance parameters during welding operation. Table 6: Process parameters and their levels

Factors A B C D E

Process parameter Welding current (amp) Voltage (volt) Gas flow rate (ltr/min) Welding speed (m/sec) Standoff distance (mm)

I 100 10 9 1.5 1

Levels II 175 15 11 2 2

III 250 20 14 2.2 3

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

4.3

12897

Design of Experiments

The experimental runs are performed based on the L27 orthogonal array of TAGUCHI method. On the basis of literature reviewed and industrial feedback suitable process parameters and performance parameters are selected, experimentation is done using the closed head orbital welding set up. After completing the 27 experiments tensile test is performed on all the 27 welded pieces to find out the tensile strength of the welded pipe. All the experimental values are shown in table 7. Table 7: Experimental results based on L27 orthogonal array Experiment No.

Welding current (amp

Voltage (volt)

Orthogonal array L27

Output Parameters

Process parameters

Experimental Temperature (٠c)

Gas flow rate (ltr/min)

Welding speed (m/sec)

1 2 3 4 5 6 7

100 100 100 100 100 100 100

10 10 10 15 15 15 20

9 9 9 11 11 11 14

1.5 1.5 1.5 2 2 2 2.2

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

100 100 175 175 175 175 175 175 175 175 175 250 250 250 250 250 250 250 250 250

20 20 10 10 10 15 15 15 20 20 20 10 10 10 15 15 15 20 20 20

14 14 11 11 11 14 14 14 9 9 9 14 14 14 9 9 9 11 11 11

2.2 2.2 2.2 2.2 2.2 1.5 1.5 1.5 2 2 2 2 2 2 2.2 2.2 2.2 1.5 1.5 1.5

5.0

Standoff distance (mm) 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1479 1106 967 792 1461 1083 2674 1991 1041 2350 1627 1125 3705 2747 1421 4686 3408 1964 3461 2625 1326 5138 3980 2302 6895 4985 2964

Conclusion

The objective of this work is experimental and numerical investigations and optimization of the process parameters during Orbital pipe welding of AISI 304 stainless steel pipes. To achieve this in total 27 experiments are conducted according to Orthogonal Array (OA) L27 (35). Taguchi’s design of experiments has been employed for experiment design. Analysis of Variance (ANOVA) has been employed to see the level of significance of each process parameter on performance measures. Taguchi’s method has been employed as single-objective optimization technique to find optimal combinations of input parameters for each performance measures. Taguchi design and ANOVA both are performed with help of Minitab 17 and Design Expert Statically Software 9. In this work, two process parameters (Temperature and Tensile strength) are investigated by varying the five Process parameters (Welding current, Voltage, Gas flow rate, Welding speed, Stand-off distance) on AISI 304 stainless steel pipes. The optimum parameters value combination was found which would yield maximum temperature. To perform numerical simulation, Finite Element approach is performed to simulate the orbital pipe welding process with the help of

12898

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

ANSYS mechanical APDL 14.5 analysis software. A three dimensional finite element model assuming a Gaussian distribution heat source has been used to investigate the temperature distribution and residual stress distribution. In On the basis of experimental and numerical studies and results, the conclusions drawn for this work are presented below: The dominating factors that affect the temperature are Current and Welding speed. The optimum set of process parameters for higher temperature and performance parameters achieved at these optimal values of process parameters are illustrated in the table 8 and table 9 respectively. Table 8: Optimum set of process parameters Process Parameters

Optimal Values

Welding current (amp)

175

Voltage (volt)

10

Gas flow rate (ltr/min)

11

Welding speed (m/sec)

2.2

Standoff distance (mm)

2

Table 9: Performance parameters at optimal process parameters Performance Parameters

Achieved Values

Temperature (K)

1789.4

Table 10: Comparison of temperature of experimental and Stage-I numerical studies Exp. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

ANSYS temperature (K) 1388.1 1033.2 823.5 715.7 1537.4 900.04 2751.04 2041.5 1191.72 2410.2 1789.4 1045.8 3602.9 2671.6 1556.32 4795.5 3553.8 2066.7 3432.5 2545.6 1483.4 5136.32 3805.9 2212.6 6840.1 5066.26 2941.8

Experimental Temperature (K) 1479 1106 867 792 1461 1083 2674 1991 1041 2350 1767 1125 3705 2747 1421 4686 3508 1964 3461 2625 1326 5138 3980 2302 6895 4985 2964

Percentage Error 6.15 6.66 5.07 9.72 -5.20 16.89 -2.8 -2.5 -14.4 -2.5 -1.2 7.1 2.7 2.7 -9.5 -2.32 -1.2 -5.1 0.83 3.04 -11.84 0.3 4.3 3.9 0.79 -1.62 0.7

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900

12899

Experimental measurements are costly and require special equipment and also a time consuming process. To avoid such difficulty, finite element package are used for getting results under different boundary conditions. Comparison of Experimental & Simulated Temperature: In Stage-I, a three dimensional finite element model assuming a Gaussian distribution heat source has been used to investigate the temperature distribution and residual stress distribution. As seen the simulated temperatures are in match with the experimental temperature values and all the simulated temperature values are within the expected range of 1200-2000 (K) except few odd cases. This comparison is the basis of validation of our numerical model of Orbital welding prepared using ANSYS platform. On comparing temperature of both experimentation and numerical values the maximum average percentage error is 8% as shown in table 10 after neglecting few odd cases. The experiment results are consistent with the numerical simulation results and ensure the correctness of numerical simulation. The deviation is due to error in material properties, geometry, Gaussian heat assumption and mathematical model used. References [1]

N.I. S. Hussein, M. N. Ayof, S. Nordin [2016] “Tensile Strength of Orbital Welded

Mild Steel Tubes with Dissimilar Thickness”

International Journal of Materials, Mechanics and Manufacturing, Vol. 4, No. 1, February . [2]

Er Bhawandeep singh, Er Avtar simgh [2015] “Performance of activated TIG process in mild steel welds”, e-ISSN: 2278-1684,p-ISSN:

[3]

Abhishek B P, Anil kumar G, Madhusudhan.T [2015] “Experimental And Finite Element Analysis Of Thermally Induced Residual

2320-334X, Volume 12, Issue 2 Ver. IV (Mar - Apr.), PP 01-05,www.sciencedirect.com. Stresses For Stainless Steel 303grade Using Gmaw Process”, International Research Journal of Engineering and Technology (IRJET) ,Volume: 02 Issue: 02 | May. [4]

Dinesh Kumar.R, Elangovan.S, Siva Shanmugam.N [2014] “Parametric Optimisation Of Pulsed – Tig Welding Process In Butt Joining Of 304l Austenitic Stainless Steel Sheets”, IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163 | pISSN: 2321-7308, Volume: 03 Special Issue: 11 | NCAMESHE -. | .

[5]

M. Islama, A. Buijk, M. Rais-Rohani, K. Motoyama [2014] “Simulation-Based Numerical Optimization Of Arc Welding Process For Reduced Distortion In Welded Structures”, 0168-874X & Elsevier B.V.

[6]

Paulo Roberto de Freitas Teixeira1, Douglas Bezerra de Araújo, Luiz Antônio Bragança da Cunha [2014] “Study Of The Gaussian Distribution Heat Source Model Applied To Numerical Thermal Simulations Of Tig Welding Processes”, Ciência & Engenharia (Science & Engineering Journal ISSN 1983-4071 23 (1): 115 – 122, jan. – jun. ,115 .

[7]

Vishnu V.S, Nadeera M, Joy Varghese V.M [2014] “Numerical Analysis of Effect of Process Parameters on Residual Stress in a Double Side TIG Welded Low Carbon Steel Plate”, International Conference on Advances in Engineering & Technology – 2014 (ICAET-).

[8]

Shankar Sehgal, Harmesh Kumar [2014] “Structural Dynamic Finite Element Model Updating using Derringer’s Function: A Novel Technique”, E-ISSN: 2224-3429 11 Volume

[9]

A. Ravisankar, Satish Kumar Velaga, Gaurav Rajput, S. Venugopal, 2014, “Influence of welding speed and power on residual stress during gas tungsten arc welding (GTAW) of thin sections with constant heat input: A study using numerical simulation and experimental validation”, Journal of Manufacturing Processes, Volume 16, Issue 2, April 2014, Pages 200-211

[10]

Olivier Desmaison, Gildas Guillemot, Michel Bellet [2013] “Numerical Modelling Of Hybrid Arc/laser Welding: A Level Set Approach For Weld Bead Formation and Residual Stresses”, Proc. JOM17, Int. Conf. on Joining Materials, May 5-8, Helsingor, Denmark, International Institute of Welding

[11]

Dongho Kim, Sung Choi, Kyowoong Pee, Youngsik Cho, Seungwoo Jeong, Soo-Ho Kim [2013] “Development of Orbital TIG Welding Robot System for the Pipe”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol:7, No:12.

[12]

Vandewynckélea, E. Vaamondea, M. Fontána, P. Herwigb, A. Masciolettic [2013] “Laser Welding Head Tailored To Tube-Sheet Joint

[13]

Lakshman Singh, Vinay Shah, Naveen K.Singh [2013] “Study The Influence of TIG Welding Parameters On Weld Characteristics Of

Requirements For Heat Exchangers Manufacturing”, The Authors. Published by Elsevier B.V. 5083 Aluminum Alloy”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 5, September.

12900 [14]

Ruparekha patro1 and Sharad K. Pradhan/ Materials Today: Proceedings 5 (2018) 12886–12900 Mukesh*, Sanjeev Sharma [2013] “ Study of Mechanical Properties in Austenitic Stainless Steel Using Gas Tungsten Arc Welding (GTAW)” Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.547-553

[15]

Pablo Batista Guimarães, Paulo Marcelo Almeida Pedrosa, Yogendra Prasad Yadava, José Maria Andrade Barbosa, Aníbal Veras Siqueira Filho, Ricardo Artur Sanguinetti Ferreira [2013] “ Determination of Residual Stresses Numerically Obtained in ASTM AH36 Steel Welded by TIG Process” Materials Sciences and Applications, 2013, 4, 268-274 http://dx.doi.org/10.4236/msa.2013.44033, (http://www.scirp.org/journal/msa)

[16]

Dheeraj Singh, Vedansh Chaturvedi, JyotiVimal [2013] “Parametric optimization of TIG process parameters using Taguchi and Grey Taguchi analysis” Issue 3, Vol.4 (June-July 2013) http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149

[17]

M. A. Daha, G. A. Nassef, I. A. Abdallah, [2012] “Numerical Modeling of Heat Transfer and Fluid Flow In Keyhole Plasma Arc Welding Of Dissimilar Steel Joints”, ISSN: 0975-5462 Vol. 4 No.02.

[18]

Hesam

Pouraliakbar, E. Ranjbarnodeh, A. H. Kokabi [2012] “Finite Element Simulation of Carbide Precipitation in Austenitic

Stainless Steel 304”, International Journal of Mechanics and Applications, 2(6): 117-123. [19]

Chandrasekhar Neelamegam, Vishnuvardhan Sapineni, Vasudevan Muthukumaran, Jayakumar Tamanna [2012] “Hybrid Intelligent Modeling For Optimizing Welding Process Parameters For Reduced Activation Ferritic-Martensitic (RAFM) Steel”, Journal of Intelligent Learning Systems and Applications, 5, 39-47 http://dx.doi.org/10.4236/jilsa..51005 http://www.scirp.org/journal/jilsa.

[20]

R.Sathish1, B.Naveen, P.Nijanthan, K.Arun Vasantha GeethanVaddi Seshagiri Rao [2012] “Weldability and Process Parameter Optimization of Dissimilar Pipe Joints Using GTAW” Vol. 2, Issue 3, May-Jun 2012, pp.2525-2530

[21]

Huang Pengfei, Li Yan, Lu Yangyang, Lu Zhenyang [2011] “Numerical simulation of the temperaturbe filed in fixed-TIG welding pool”, International Conference on Modeling, Simulation and Control IPCSIT vol.10 © IACSIT Press.

[22]

E.G. Ternovoj, V.F. Shulym, A.R. Bulatsev, T.G. Solomijchuk, V.A. Kostin[2011] “Properties And Structure Of Circumferential Joints Of Tubes Made By Orbital Electron Beam Welding” © e.g. ternovoj, v.f. shulym, a.r. bulatsev, t.g. solomijchuk and v.a. kostin.

[23]

Andrés Anca, Alberto Cardona, José Risso, Víctor D, Fachinotti [2011] “Finite Element

Modeling Of Welding Processes”, 2010

Elsevier, A. Anca et al./Applied Mathematical Modelling 35 688–707. [24]

José A. Orlowski de Garcia, Gérson Luiz de Lima, Wilson D. Bocallão Pereira, ValdirAlves Guimarães, Carlos de Moura Neto, Ronaldo Pinheiro R. Paranhos [2010]“Characterization of titanium welded joints by the orbital gas tungsten arc welding process for aerospace application” J. Aerosp.Technol. Manag., São José dos Campos, Vol.2, No.2, pp. 211-218

[25]

George

Luiz Gomes de Oliveira, Hélio Cordeiro de Miranda, Jesualdo Pereira Farias [2010] “Residual Stress Evaluation In Small

Diameter Pipes Welded Using Orbital TIG Process”, Soldagem Insp. São Paulo, Vol. 14, No. 2, p.114-121. [26]

V Kumar, Bill Lucas, D Howse, G Melton, S Raghunathan, Louriel Vilarinho [2009] “Investigation Of The A-TIG Mechanism And The Productivity Benefits In TIG Welding”, International Conference on the Joining of Materials (JOM 15) and 6th International Conference on Education in Welding (ICEW 6) Helsingor, Denmark, 3-6.

[27]

M. Maalekian, E. Kozeschnik, H.P. Brantner, H. Cerjak [2008] “Finite Element Modeling of Orbital Friction Welding of Eutectoid Steel Bars”, The Minerals, Metals & Materials Society and ASM International .

[28]

Andreas lundback, Lulea Maj [2003] “Finite Element Modeling And Simulation Of Thesis, 2003:27 ISSN: 1402-1757, ISRN: LTU-LIC-03/27.

Welding Of Aerospace Component”, M.Tech