Optimization of tensile strength in TIG welding using the Taguchi method and analysis of variance (ANOVA)

Accepted Manuscript Optimization of Tensile Strength in TIG Welding Using Taguchi Method and Analysis of Variance (ANOVA) A. Balaram Naik, A. Chennakeshava Reddy PII: DOI: Reference:

S2451-9049(17)30219-6 https://doi.org/10.1016/j.tsep.2018.08.005 TSEP 215

To appear in:

Thermal Science and Engineering Progress

Received Date: Revised Date: Accepted Date:

26 July 2017 4 August 2018 7 August 2018

Please cite this article as: A. Balaram Naik, A. Chennakeshava Reddy, Optimization of Tensile Strength in TIG Welding Using Taguchi Method and Analysis of Variance (ANOVA), Thermal Science and Engineering Progress (2018), doi: https://doi.org/10.1016/j.tsep.2018.08.005

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Optimization of Tensile Strength in TIG Welding Using Taguchi Method and Analysis of Variance (ANOVA) *

Balaram Naik A and 1Chennakeshava Reddy A

*

Senior Assistant Professor, Department of Mechanical Engineering, Jawaharlal Nehru Technological University Hyderabad, Kukatpally, Telangana, India. E-mail: [email protected] 1 Professor, Department of Mechanical Engineering, Jawaharlal Nehru Technological University Hyderabad, Kukatpally, Telangana, India.

Abstract: The main criteria discussed on this paper about the welding optimization parameters and tensile strength of duplex stainless steel 2205 by tungsten inert gas welding based on Taguchi method and analysis of variance. Taguchi method of orthogonal L9 design experiment is carried out using orthogonal array for defining the problem occur on welding process and to reduce the error occurred in the neural network for the prediction of output. The neural network is a mathematical prediction model for the optimization process using back propagation algorithm. Analysis of Variance (ANOVA) is a decision tool for detecting the variation of process parameters, it is a statistical technique for find out the optimal level of factors for the verification of the optimal design parameters through confirmation experiments. The purpose of this paper to increase the tensile strength, hardness and depth of weld by varying the parameters such as current, time, speed, variation of oxide fluxes, electrode diameter and gas flow rate. The Mat lab software is used for analyzing results and it shows that neural network coupled with taguchi method and Anova is an effective method for optimizing the weld quality of material. Keywords: TIG Welding, Taguchi Method, Analysis of Variance (ANOVA), Orthogonal Array, Design Optimization. 1. INTRODUCTION Metal joining plays a significant role in modern fabrication technology. Production of materials increased by day to day and competition of manufacturer is also increasing. Recent technologies in production such as metal joining, metal welding, strengthening, material quality and durability. Welding is the process of joining two metals by fusing the base metal and by adding a filler material over the surface of molten metal to form a strong bonding on metals. In tungsten arc welding the tungsten electrode with constant weld power supply is used to generate electric arc between the electrode and the workpiece which create resultant heat to form the weld [1]. Welding is an efficient and economic method for permanent joining of metals. Different number of welding process are followed in manufacturing which are implemented in short time. Welding process are differ from one another by usage and type of equipment used. The tungsten inert gas arc welding is one of the most popular welding process followed for the material production. In tungsten inert gas arc welding an electric power supply will create arc which melts base metal to form a molten pool [2]. The filler material is manually added for TIG welding and the molten metal is allowed to cool.

1

Stainless Steels: Stainless steels are mostly used in industries and in commercial field. Stainless steels are used to produce automobile parts, vehicles and in rail coach manufacturing. Stainless steels combine with composites like nickel, chromium and magnesium to form alloys. Stainless steels are possessing good corrosion resistance and low productivity cost. Stainless steels are classified in to three types austenitic, ferritic and martensitic. The austenitic stainless steel is face centered cubic crystalline in structure. The chemical composition consists of 0.15% carbon, 16% nickel and chromium to structure the austenitic. The ferritic stainless steel has good mechanical properties than austenitic steels. It has low corrosion resistance due to low composite mix of nickel and chromium. It is a body centered cubic structure having a composition of 10.5 to 27% chromium with low amount of nickel content. Martensitic stainless steel are extremely tough and highly machined to any shape and sizes but it is not corrosion resistance as every stainless steels. It has a composition of 12-14% chromium, 0.2-1% molybdenum, 2% nickel and 0.1-1% carbon. Tungsten Inert Gas Arc Welding (TIGAW): In tungsten inert gas arc welding the arc is produced by the electric supply which forms between the tungsten electrode and the base metal. Though other welding process the electrode melt to form weld but in gas arc welding point of base metal where weld is carried out is transformed in to weld pool by the arc. The thin filler metal is fed manually in to the pool where it melts. The tungsten is very sensitive to oxygen in atmosphere, so it is recommended working with tungsten need good shielding with oxygen free gas is required. During solidification the inert gas provides a stable inert atmosphere for the protection of weld pool [5]. In TIG welding the fluxes are not used in weld because it form slag inclusion and form corrosion on weld metals. Tungsten has high temperature on melting and possess good electrical conductivity thus it make the electrode non-usable [7]. The temperature of arc is typically around 11,000° F and similar shielding gases used are Ar, He, N or a mixture of two. The tungsten inert gas welding is generally performed on stainless steel, magnesium, alloys to aluminium, copper, brass, nickel, titanium etc. However TIG welding is one of the slower method in arc welding. The weld quality can affect the overall property of the metal, thus an optimized method to find out the error and minimize to least possible. Taguchi method and analysis of variance is used to optimize the weld metal property by finding errors and choosing best value from its weld parameters. As a result the improved optimization technique can improve the DSS 2205 tensile and mechanical property by using Taguchi method and analysis of variance is discussed in the paper. The rest of the paper includes related work at section 2, proposed methodology in section 3, experimentation result, performance analysis and discussion is detailed at section 4 and conclusion part at section 5. 2. RELATED WORK The welding is practiced in the production field for the past years. Welding is a production process to improve productivity of higher quality material. Welding process are practiced numerously which could affect the weld quality, productivity and cost of manufacture of a product. Many welding techniques are proved with various welding metals such as titanium, aluminium and stainless steels Ericsson. M and Sandstorm. R et al. [1]. Tungsten inert gas arc welding play an important role in acceptance of aluminium for quality and structural application. TIG welding is most accurate mode of welding in stainless steel and many researches are carried out experiments to optimize the properties of the metal. The activated tungsten inert gas welding process is used for the welding of duplex stainless steel alloy 2205 with the welding parameters is discussed here.

2

Stainless steels is widely used in many in product fabrication such as automotive, vehicles bodies and in commercial fields etc., due to its high corrosion resistance, fire resistance and better weight ratio. Stainless steel is generally a composition of metal alloys such as chromium, carbon, nickel and molybdenum. Stainless steel has more amount of chromium so it differs from carbon steels and carbon steels rust easily when it is exposed to air and moisture Rizvi, Saadat Ali. S. P. Tewari et al. [2]. Welding is a process of joining two metals by using heat and electricity. Tungsten inert gas arc welding is a joining process and it is most effective welding process in welding stainless steel Deva Kumar. D, and D. B. Jabaraj et al. [3]. In TIG welding process the weld arc is formed in between the workpiece and the electrode with an inert gas atmosphere of argon or helium. For quality welding small intense arc is formed by the electrode is used L.S. Kim, C.E. Park, and Y.K. Jeong et al. [11]. The TIG welding is operated in either DC or AC, a constant current power source is essential to avoid high current when electrode is short circuited on the workpiece surface Ladislav Grad, Janez Grum et al. [4]. Welding arc is started by scratching the workpiece surface forms a short circuit. These short circuit will break and main weld current will flow Fude Wang, Stewart Williams et al. [13]. There is a risk the electrode may stick to the surface and cause a tungsten inclusion in the weld, so it can be minimized by using lift arc technique where short circuit current formed at very low current level H.G. Fan, H.L. Tsai et al. [5]. Electrodes for DC welding generally used are tungsten with 1 to 4% thoria to improve for the formation of weld arc. Other alternate method for improve weld are lanthanum oxide and cerium oxide are used for obtain better performance on welding. The shielding gas used in TIG welding are argon, argon with 2 to 5% H2 and helium and helium argon mixtures J.Tusek, M.Suban et al. [6]. The TIG welding is applied for the top side infrared sensing technique for the closed loop control of weld penetration in welding S. Mukhopadhyay, T.K. Pal et al. [10]. The heat transfer on electrode may differ during welding and different current, workpieces sizes and electrode tip angle are arranged in the experiments to validate current adjustments for the heat variation and electrode wear A.K. Lakshminarayanan, K. Shanmugam et al. [12]. The shielding gas composition is used for the welding of high strength low alloy steels with flux cored arc welding wires which can improve the quality of weld and properties of the welded metals Hussain, Ahmed Khalid et al. [7]. Gas metal arc welding is mainly used for welding ferrous and non-ferrous metals for the increase of production and quality of welding metals. In industries the finite element method is employed for the monitor robotic welding to control the bad geometry of welding in real time. Metal properties of welded metals of extra high strength steels has to be found out for the optimization of tensile properties Balasubramanian. M, V. Jayabalan et al. [17] Y.S. Tarng, H.L. Tsai, S.S. Yeh et al. [8]. The thermal cycle generates on the weld during cooling forms strains in joints due to thermal expansion which cause tensile stress on weld metals. Tensile failures generally occurred by the improper joints, power supply and gas used for shielding H.E. Beardsley, Y.M. Zhang et al. [9]. During welding cracks are obtained due to the overheating of metal or loss of strength in metals. To find out the crack in welded metal local stress approach is applied to predict the crack on the welded joints P. Johan Singh, D.R.G. Achar et al. [15]. Welding current is another factor affecting the welding lack of power supply can affect the fusion and weld metals. The welding fusion need power supply from either DC or AC power supply to create arc for the weld Nilanjan, Suman Chakraborty et al. [16] A. Farzadi, S. Serajzadeh, and A.H. Kokabi et al. [14]. Welding fusion zones on metals lead to mechanical and metallurgical properties. The pulsed current can

3

beneficiary to grain refinement on weld zone and is controlled by pulse current and frequency Kishore. K, P.V Gopal Krishna et al. [18]. Taguchi method is a powerful tool for improving productivity in welding process. This method develop high quality products at low cost at short time period. Taguchi method is also very important tool for the robust design Anoop. C.A, Pawan Kumar et al. [19]. Taguchi method uses orthogonal array for analyze the parameter with least experiments. By Taguchi L9 orthogonal array nine experiments is carried out to obtain output. The artificial neural network with taguchi method is used for the optimization where the weights are given by back propagation algorithm for the optimization of welding parameters Kalaiselvan.K, Elango.A et al. [22]. To find out the most significant factor analysis of variance is applied and to classify weld quality in TIG welding Korra, Nanda Naik, M. Vasudevan et al. [20]. Other methods like fuzzy pattern recognition technique to classify the weld quality of metal in tungsten inert gas welding Tarng.Y.S, S.S.Yeh, and S.C. Juang et al. [21]. 3. PROPOSED METHODOLOGY Welding of stainless steels are followed in the production field very earlier and many composite mixture metals are also introduced to joining process. Defects are occurred when the weld metal does not met the mechanical and metallurgical properties of the metal design. Improper of design can affect the strength and load applied on it. The flow of gas, current and fusion heat are some of the defects occurs on the weld. During welding the difference of input current, gas flow rate, speed and depth parameter will make the weld metal properties lower. Tensile strength is one of the defects occur during welding. The tensile strength is lower when the weld metal joint has poor loading capacity and weld quality. If the weld of metal is high and toughness can possess better tensile strength. In this paper the duplex stainless steel 2205 is taken for the welding process. The duplex stainless steels are strong and good weldable and possess good corrosion resistance to atmosphere and chemicals. These steels are a composite mixture of chromium, molybdenum and nitrogen which forms corrosion resistance to certain chemicals like chloride. Welding technique used for welding DSS 2205 is tungsten inert gas arc welding. The tungsten inert gas arc welding uses consumable tungsten electrode for welding. The welding area is protected from the atmosphere contamination by shielding gases like argon or helium. The arc is generated by a constant power supply by either AC or DC. The main objective on this paper is to optimize the tensile strength and hardness on metal by maximizing/minimizing the welding parameters like gas flow rate, current, oxide fluxes etc. During welding the error obtained on metal is analyzed by Taguchi method and artificial neural network. In the welding process the artificial neural network coupled with taguchi method of orthogonal array is also used to find the optimized value in neural network design. The Taguchi method optimize the error on neural network by adjusting the network weights for the outputs. The orthogonal L9 array is an evaluation design to evaluate the problem by conducting tests. A set of nine experiments conducted to get an optimized results. A decision tool analysis of variance (ANOVA) for detect the variance in the process and it is a statistical technique for finding of optimal results through confirmation experiments. Data Flow:

4

Fig: 1: Data flow for the Optimization of DSS 2205 in TIG welding In figure 1, a pictorial representation of the data flow for the DSS 2205 material optimization using TIG welding is shown. The diagram consists of steps such as material acquisition, process input parameters details, artificial neural network, outputs and the test validation step. The optimization process is carried out using Taguchi method of orthogonal array. Design of Experiment and Data Collection The design experiment starts with the analysis of welding of duplex stainless steels 2205 by tungsten inert gas welding. After the welding the base metal is subjected to testing where it forms irregularities on the weld like roughness, lower tensile strength. To validating the error taguchi method of orthogonal array is used to conduct the test for find out the best result. Taguchi has derived a new method to perform test on design of experiments. This method uses a set of orthogonal arrays for conducting minimum number of experiments that give brief details on factors affecting performance parameters. The mode of orthogonal array is based on signal, noise and control factors. The experiment conducted on duplex stainless steel grade 2205 of Avesta type of dimensions 200  50  3mm cross section plates are used. The TIG welding is done on base metal with filler material made of ER316L of diameter 2.4mm with ceramic nozzle of diameter 9.5mm is used for the experiment. During the welding process the constant arc is maintained at a gap of 3mm and the electrode is placed perpendicular to base metal. The joint of weld will be good if 5mm plate with arc gap is not exceed to 0.7mm and 0.3mm for 3mm plates. The base metal is made of duplex stainless steels of grade 2205 which has higher mechanical and chemical properties which offers the metal to good weldability. This grade metal is generally used in production due to its properties. The filler material or electrode is made of ER316L contains 2-3% molybdenum which can improve the pitting corrosion on welded metals. The use of Molybdenum in filler can increase corrosion resistance. These type of filler material is generally used in sheet metal working corresponding to stainless steels base metals. Due to its high corrosion resistance on atmospheric contaminants it is widely used for weld high pressure piping and tubing. Duplex Steels: Duplex stainless steel are a composite mixture of austenitic and ferritic. It has high amount of chromium, nitrogen and molybdenum. These steels are high strength, high resistance to corrosion and possess good weldability. Duplex stainless steel 2205 is a composite 5

steel mixture of austenite and ferrite and more resistance to stress corrosion. These steels have higher amount of chromium, molybdenum and nitrogen possessing the crevice corrosion resistance in the presence of chlorides. The SS2205 steels are mainly used in marine environments, oil and gas extraction due to its high serviceability in environments contain chemicals like chlorides and hydrogen. These steels work long time on heat at a temperature of 300 C and it extend up to 950 C . After the extend temperature the metal may embrittle and lower its corrosion resistance. Chemical Composition of Base Metal and Filler Metal Table 1: Chemical Composition of Base metal and Filler metal

S.No 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Chemical Composition Chromium Nickel Carbon Silicon Magnesium Phosphorus Sulphur Molybdenum Copper Iron

% Composition of base metal DSS2205 22.821 5.75 0.024 0.457 1.72 0.019 0.017 3.22 0.73 Remaining

% Composition of Filler Material ER316L 18.5 11.5 0.03 0.45 1.75 0.03 0.03 2.8 0.75 Remaining

In table 1, the chemical composition of the base material and the filler material used is provided. The base material used is DS2205 and filler material used is ER316L. From the table it is clear that these two materials are having common elements in their composition and the major difference in material composition is seen for elements such as chromium and nickel. Step 1: Parameters Influence on Welding Arc Welding: Electric arc welding is one of the type of fusion welding process followed in manufacturing. The weld is performed by the metal is heated by an electric arc between an electrode and workpiece. The electric arc is produced where the electrode is made to contact with the metal with a short distance which is equal to 2mm. The machine operates at low voltage with high current which create arc for weld by thermionic emission from electrode (cathode) to workpiece (anode) Deva Kumar. D, and D. B. Jabaraj et al. [3]. The arc is constant by the presence of thermally ionized column of gas. During welding the arc produced at a temperature of 5500°C or higher. By this high temperature weld a pool of molten metal (workpiece and filler metal) formed on the welding zone Ladislav Grad, Janez Grum et al. [4]. The electrode is placed in perpendicular zig-zag motion. The molten metal pool is solidified to form strong welded joint and the movement of electrode in weld is either manually or automatically by the type of machine used. Selection of the welding process: Welding process is selected based on the particular work carried out. Selection of welding is based on the shape and size of component to be manufactured. Each welding process is differ from its type of welding equipment, heat fusion and protection of 6

weld. Welding equipments are available based on the weld and material to be weld. For commercial usage welding machines are powered by DC and AC power supply. Some of the common factors governing the selection of welding process are availability of equipment, repetitiveness of the operation, quality of metal, type of joint, time, cost etc. Flux: Flux is used in weld to prevent oxidation of hot metal. The flux converts the gases (oxides and nitrides) to slag that can be removed from welded metal easily. The formation of slag can make the weld weak thus flux material are used for removing slag from the metal. Fluxes are different for different metal welding. For welding of metals like copper and its alloys the sodium carbonates are used as flux. For welding of aluminium and it alloys the fluxes like sodium chloride, potassium, lithium and barium are used. Power Source: In welding AC and DC power supplies are used for the operation. For welding ferrous metals AC supply is used. For welding non-ferrous and mixture of alloys such as aluminium, stainless steel etc. Dc supply is used. The welding machine generally outputs low voltage with high current using a transformer in the machine. The power supply needed for the AC welding is 80 to 110 volt with 50 to 80 ampere constant current is established and power factor is kept low. In case of DC welding power supply is generally 8 to 25 volt with 50 ampere current and the polarity is constant factor. DC welding generally has two types of polarities. They are straight polarity and reversed polarity. In straight polarity the positive and negative terminals are connected to work material and electrode. In reversed polarity the terminals connected is reversed to straight polarity. The heat is generated at positive pole which is one third at negative pole. Polarity is generated according to requirement of heat at either poles. Welding Electrodes: Electrodes are used in arc welding for fusing workpiece by conducting current through the workpiece. The electrode is either consumable or non-consumable based on the process and type of machine used. In case of tungsten inert gas arc welding non-consumable electrode is used. Electrodes is classified in to coated electrodes and bare electrodes. Bare electrodes are simple electrodes with no coating over them. Non-consumable Welding Electrodes: These electrodes are made of tungsten or carbon and during welding these electrode not melts due to low depletion rate. The flux material is additionally needed for the bare electrode and these electrodes are rarely used. Non-consumable bare electrodes are used in gas shielded welding process such as MIG and TIG. Coated Consumable Welding Electrodes: In arc welding coated consumable electrodes are generally used and flux and filler material are not added additionally for the welding. These electrodes have a core of mild steel and flux is coated over the surface of the electrode. Coating over the electrode reduce atmosphere, prevent oxidation and removable slag from metal surfaces. Shielding Gas: The shielding gas used in TIG welding for the protection of welding area from the atmospheric gases which can defects during fusion of metals, porosity, weld metal ductility when they come contact with the arc or the welding metal. Shielding gas also transfers heat from electrode to the metal and helps to start a sub arc [6]. Some of shielding gas used in TIG welding are Argon: It is most commonly used in a wide variety of metals such as steels, stainless steels, aluminium and titanium. Argon with 2 to 5% H2: Argon is mixed with hydrogen to make the gas slightly reducing to weld the metal without oxidation. Disadvantage is when weld metal hydrogen cracking risk to carbon steels is to forms porous in metal alloys like aluminium.

7

Helium and Argon-Helium Mixtures: Helium is added to argon to form high temperature arc which offers high welding speeds and deeper weld penetration. The only disadvantage is these gas mixtures are high in cost and difficult to start the arc. Step 2: Orthogonal Array In orthogonal array the independent variable has a combination of level setting and are equal number of times. The L9 orthogonal is used for the testing of parameters in level 1, 2, 3 at variable 4. It is called balancing property of orthogonal array. The experiment is conducted with all these independent variables and experiment 1 cannot conduct with variable 1, level 2 and experiment 2 with variable 1 and level 1. This is due to the array of each factor column are orthogonally to other level of column values. The inner product of vector corresponds to weight is zero and the above 3 levels are normalized between -1, 0, 1. Therefore the weighing factor of inner product corresponds to independent variable is 1 and the independent variable 3 will be  1  1  1  0  1 1 + 0  0  0 1  0  1 + 1  0  1 1  1  1 =0. The parameters used for the experiment of orthogonal array is taken from the below table. The table shows the TIG welding of DSS2205 is carried out by different oxide fluxes with varying time, current and electrode diameter. Minimum experiments to be conducted by Taguchi method can be calculated which is based on degree of freedom approach

 L  1 NV

N Where,

L

i

Taguchi

1

i 1

i

(1)

is sum of orthogonal array used in the taguchi method, NV is number of generation

carried out in taguchi method. Step 3: Signal to Noise Ratio The quality of the product can be quantified in terms of response to noise factor and signal factor. The product quality can be improved by maximizing the signal to noise ratio for the respective product. The computation of signal to noise ratio can be done by Taguchi robust design of experimental design module. The S/N ratio for any design operation can be exewelded by resulting the value of design available in the experimental design module. i) Smaller the Better Type The number of flaws in the welding is measured as y variable and analyzed by S/N ratio. The effect of signal factor is zero and it shows the flaws on the welding is zero. The S/N ratio is an expression of the assumed quadratic nature of the loss function and by increasing the ratio will increase the quality of the metal. In this paper the S/N ratio to find the small least error in welded metal is found out by the given equation below.

S N where, n is the number of trial test,

Y

i

is the

i

th

 2  10 log  Y i   n  smaller

(2)

number of quality characteristics.

ii) Bigger the Better Type The quality of the product should be satisfies and the S/N ratio of product quality should be greater as possible. Welding quality of product will be higher and there is no adjustment of factor for the 8

S/N ratio. If error occur it transformed in to smaller the better type for the quality characteristics. The S/N ratio for evaluating the higher quality is derived from the equation below. 1    2  S  10 log  Y i  (3)  n  N Bigger     th where, n is the number of trial test, i is the i number of quality characteristics.

Y

iii) Nominal the Best Type The S/N ratio is used when the quality of the product is equalize with particular nominal value. The quality of product is nearer to the specification as possible to ensure high quality. The nominal best type for the quality equalize in welding can be derived from the following equation.[23]  _ 2   S   10 log  Y (4)  s2  N No min al     _

where,

Y

is the mean value quality characteristics, s is the sum of variance on the test value.

iv) Grey Relational Analysis Multiple performance on the system can be analyzed using grey relational method. In this method the multiple performance parameters are converted in to single grey relational grade. Step 1: In Taguchi analysis the S/N ratio obtained have to normalized in range of 0 to 1 Larger the Better: In grey relational the S/N ratio is normalized to larger the better to find the larger optimum quality of the material. The grey relational is computed by taking the average of grey relational coefficient corresponding to every processes. The larger the better value in grey relational is found by the equation below.

Y Y X Y Y ij



ij

ij max

where,

Y

ij min

X

ij

is the normalized S/N ratio,

Y

ij

ij min

(5)

ij min

is the S/N ratio analyzed by Taguchi,

Y

ij max

and

are the maximum and minimum S/N ratio obtained.

Smaller the Better: The smaller the better value is the normalized S/N ratio in the grey relational for obtaining smaller error in the quality standards. The grey relational analyzed for the prediction of small errors in quality by the following equation.

Y Y (6) X  Y Y where, X is the normalized S/N ratio, Y ij max and Y ij min are the maximum and minimum S/N ratio obtained, Y is the S/N ratio analyzed by Taguchi. ij max

ij

ij

ij max

ij

ij

9

ij min

Step 2: The grey relational grade in the process analysis the values are obtained indicating relational degree between every sequence of operation. The grey relational grade of optimal quality is found by the given equation below.

GC

ij



 

min ij

where,

GC

the better,

ij



is the grey relational grade,

max

and



min



 





max

(7)

max

is the assumed value 0.5 for the larger and smaller

are the maximum and minimum absolute difference.

Step 3: By finding the average grey relational coefficients the grey relational grade is found out by the following equation below. (8) Gi  1 mGCij where,

GC

ij

is the grey relational grade, m is the number of response variables,

G

i

is the grey

relational grade. Step 4: Optimization Using Artificial Neural Networks The artificial neural networks is optimization tool for the optimization of input parameters with the hidden layers and desired output will be an optimized value. An artificial neural network is a mathematical model for optimization based on biological neural networks. A neural network consists of artificial neurons for the optimization of input parameters. The artificial neural networks are recently used for the optimization of welding process using back propagation algorithm approaches. The back propagation algorithm is a neural network algorithm for the training of neural network for the optimization process. The back propagation algorithm generally operates with multiple input unit with several input variables are used. In this paper back propagation algorithm based artificial neural network is used for the optimization of welding material properties.

Fig: 2: Artificial Neural Network for Optimization of TIG Weld The figure 2, describes the process of artificial neural network used in this paper to optimize the weld property of metal after the welding process. The input parameters taken for the 10

optimization are varying oxide fluxes, current for the welding operation, time taken for weld, electrode diameter. Each input node is connected with the hidden layer for the processing and output is executed. Step 5: Mathematical Modelling of Proposed ANN Input Function: The input function for the artificial neural network is explained in equations below

BXW 1

where,

B is 1

1

I 11



XW 2

I 21

 ...........

XW

X

the input function in neural network design, I

1

i

I

(9)

in

is the input value for the

I

initialization of neural network. W 11 , W 21 is the input weight acting on each node for the adjusting the input data in the hidden layer for the optimization process. M

B X W 

i

B

i 1

is the total sum of input value in neural network design,

i

acting on neural network, total sum of inputs and

i

W

is the I ij

i

th

i

M

I

(10)

ij

is the number of hidden layer

number of inputs for network design processing.

X

i

is the

is the sum of weights acts on hidden layers in the input nodes of

neural network for the optimization. Transig function: This function is basically used for train the neural network for the optimization purpose. The transig function for the optimization process is explained in equation below.

H

H W

j I ij

is the transig function in neural network,

j



1  1  exp    

X

i

M

X W i 1

i

  ij  

(11)

I

is the as the sum of inputs in neural network,

is the total sum of weights in hidden layers in artificial neural network.

Output function: The output layer the weight is also applied for the output node for the adjusting the value to be optimized. N

B H W k

B

k

is the output function in ANN,

H

j



i 1

j

o jk

is the transig function in artificial neural network,

(12)

W

o jk

is the output weights act with hidden layer for the output function. Activation function Purelin function: This function is generally a transfer function for the calculation of hidden layer output value from the total input value which can be formulated from the given equation.

11

Y

where,

Y

is the net output calculated by purelin function,

B

out

B

out

1

(13)

is the output value calculated from

its input value. The actual output is calculated by sum of input transig function and net weight act on the output layer which is explain in the equations below. N

Y H W k

where,

Y

k

is the actual output,

H



j 1

j

o

(14)

jk

o

j

is the input transig function in neural network. W jk is the

total weights act on the output layer of neural network. Step 6: Analysis of Variance (ANOVA) Analysis of variance otherwise called ANOVA is a decision making tool for analyzing the average difference in performance of test conducted. The anova test the variables by mean squaring and estimate the experimental errors at specific levels. Analysis of variance generally used tool named F-test to analyze the particular design has any significant change in quality standards. In analyzing the parameters the F-test tool ratio of mean square and residual error is used to find the significance of a factor. The analysis of variance conducting the average of test is evaluated by using the equation below.

SS  i  nm n

T

Where,

SS

T

is total sum of squared deviation,

experiments conducted in orthogonal array,



i

2



(15)

i 1

n

m

is the total mean S/N ratio, n is number of

is the mean S/N ratio for the experiment.

Step 7: Percentage in ANOVA The percent in analysis of variance is defined as the rate of quality based on the significant rate of process parameters. The percentage number in the welding depends on the flux, current, time, gas flow rate and electrode diameter. The percentage validation can be observed from the table 2 as the parameters listed above is machined in the tungsten inert gas arc welding of duplex stainless steel 2205 grade composites with filler material ER316L. The percentage of machined material can be found out by the given equation below. P

ss ss

d

(16)

T

Thus the paper briefly shows the analytical method for the optimization of weld quality is improved by the varying the flux materials, current, gas flow rate etc. The base metal made of DSS2205 and filler metal of ER316L is made to TIG welding then the weld metal is subjected to tensile testing. The metal forms irregularities on the weld quality, so to optimize property of weld metal Taguchi optimization method of orthogonal array with artificial neural network and analysis of variance is carried out to improve quality and tensile strength of metals. 4. EXPERIMENTAL DATA COLLECTION The experiment is carried out by duplex stainless steel 2205 of Avesta type with 200×50×3mm cross section plates is used. The electrode material is made of tungsten of diameter 2.4mm and nozzle of material ceramic with diameter of 9.5mm. The arc formation is constant through the 12

welding process maintaining an arc gap of 3mm and the electrode is placed perpendicular to the base plate. The welding process is carried out by different oxide fluxes like SiO2, TiO2, mixture of two and other input parameters like time, current and electrode diameter are used. By fluctuating the input parameters the weld quality of material can be improved. Table 2: Experimental Data for TIG welding of DSS2205 Experiment

Oxide Flux

Time (seconds)

Current (A)

1. 2. 3. 4. 5. 6. 7. 8. 9.

SiO2 SiO2 SiO2 TiO2 TiO2 TiO2 Mixture Mixture Mixture

100 130 160 100 130 160 100 130 160

150 200 250 200 250 250 250 150 200

Electrode Diameter (mm) 2 2.4 3 3 2 2.4 2.4 3 2

In table 2 the experimental data regarding the TIG welding of DSS2205 is provided, which includes the type of oxide flux used, time, current and also the diameter of the electrode used. The table also gives information about the variation in the experimental data for oxide fluxes such as SiO2, TiO2 and the mixture. For each flux the current is applied in the range of 150 to 250 A, time of application is between 100 to 160 seconds and the electrode used are having diameters 2, 2.4 and 3 mm respectively. Grey Relational Analysis Results Table 3: Experimented and ANN Predicted Values S. N o

Flux

1. SiO2 2. SiO2 3. SiO2 4. TiO 2 5. TiO 2 6. TiO 2

Tim e (s)

Curr ent( A)

Electro de diamete r (mm)

100 130 160 100

150 200 250 200

2.0 2.4 3.0 3.0

Experimented Dept UTS h (mm ) 4.78 715 4.97 909 5.50 920 4.81 680

Ha rdn ess 92 94 95 90

ANN Predicted Dept h (mm ) 4.9 5.3 6.01 5.01

UTS (KN /mm 2 ) 745 956 988 745

Error

Hard ness

%

Coefficie nt

Grade ( Gi )

93 96 97 92

0.016 0.074 0.097

0.266 0.453 0.516 0.712

0.369 0.481 0.519 0.402

0.453

0.428

0.426

0.491

130

250

2.0

6.00

788

91

6.52

821

93

160

150

2.4

4.12

653

90

5.25

701

95

0.067 0.031

0.078 13

Grey Relational

7. 8. 9.

Mix Mix Mix

100 130 160

250 150 200

2.4 3.0 2.0

4.64 4.10 5.92

706 654 774

93 91 94

5.66 5.13 6.26

782 731 802

96 92 95

0.133 0.069 0.055

0.429 0.409 0.428

0.543 0.390 0.301

In table 3, the experimental and ANN predicted values are tabulated. The table provides data about the depth of weld, ultimate tensile strength and hardness. From the table it can be seen that the maximum depth of weld is 6 mm for experimented case where as it is 6.52 mm for ANN predicted case. Maximum hardness is predicted for SiO2 at 250A condition and the experimented hardness at 250 A for SiO2 showed the maximum among the whole experimental data.

Fig 3: Experimented and Predicted Results The figure 3, represents the graphs of experimented and predicted results of grey relational analysis. The graph is plotted against the ultimate tensile strength (UTS), depth of weld and hardness of SiO2, TiO2 and the mixture. For SiO2 the UTS was maximum for 3 mm electrode diameter and the value was found to be 988 KN/mm2 in the predicted section. The depth of weld recorded for TiO2 in the predicted part is 6.52mm and the value obtained in the experimental section is 6 mm, which is the maximum depth of weld. Table 4: Grey Relational-Orthogonal L9 Array Experimentation for SiO2 Test No

Current (A)

Time (s)

1 2 3 4

100 200 250 100

100 130 160 100

Electrode Diameter (mm) 2.0 2.4 3.0 2.0 14

Depth (mm)

UTS (KN/mm2)

Hardness

4.78 4.80 4.82 4.86

715.874 720.445 734.684 781.546

81 84 87 90

5 6 7 8 9

200 250 100 200 250

130 160 100 130 160

2.4 3.0 2.0 2.4 3.0

4.88 4.97 5.15 5.28 5.50

794.654 825.445 910.741 914.118 920.984

93 96 94 96 97

Table 4, displays the results obtained after the grey relational-orthogonal L9 array experimentation for SiO2. The results were calculated for a current input ranging from 100 to 250 A. The depth of weld was maximum at 250 A and the minimum was obtained at 100 A respectively. From the table it can be summarized that as the current input increased the ultimate tensile strength and hardness increased proportionally. Table 5: Grey Relational-Orthogonal L9 Array Experimentation for TiO2 Test No

Current (A)

Time (s)

1 2 3 4 5 6 7 8 9

100 200 250 100 200 250 100 200 250

100 130 160 100 130 160 100 130 160

Electrode Diameter (mm) 2.0 2.4 3.0 2.0 2.4 3.0 2.0 2.4 3.0

Depth (mm) 4.81 4.86 4.88 4.90 4.92 5.20 5.45 5.75 5.90

UTS Hardness (KN/mm2) 680.745 674.148 618.684 673.126 678.891 682.746 689.951 675.963 653.984

90 92 94 89 94 97 92 95 90

The table 5, presents the results obtained after the grey relational-orthogonal L9 array experimentation for TiO2. The results were considered for a current input vacillating from 100 to 250 A. The data from the table shows that for TiO 2 in the final two sets of analysis as the current input is increased to 250 A the hardness is seen to decrease. In the case of ultimate tensile strength, the value is decreasing for the mixture. Table 6: Grey Relational-Orthogonal L9 Array Experimentation for Mixture Test No

Current (A)

Time (s)

1 2 3 4 5 6 7 8

100 200 250 100 200 250 100 200

100 130 160 100 130 160 100 130

Electrode Diameter (mm) 2.0 2.4 3.0 2.0 2.4 3.0 2.0 2.4 15

Depth (mm)

UTS (KN/mm2)

Hardness

4.68 4.72 4.92 5.04 5.10 5.25 5.36 5.74

706.115 715.425 725.994 736.874 740.985 751.961 762.985 768.415

93 92 94 89 94 97 92 95

9

250

160

3.0

5.92

774.369

94

The table 6, presents the results obtained after the grey relational-orthogonal L9 array experimentation for mixture. The results were measured for a current input varying from 100 to 250 A. The hardness of the mixture is lower when compared to the first two set of data. The depth of weld increased from 4.68 to 5.92 mm and the UTS raised from 706.115 to 774.369 KN/mm2

Fig 4: Comparison of Test and Predicted Results In figure 4, optimal mean comparison of oxide mixtures and its performance of mean depth, ultimate tensile strength and hardness is elaborated in this given figure respectively. 5. PERFORMANCE ANALYSIS RESULTS AND DISCUSSION The proposed system for the analysis is implemented in the working platform of MATLAB with the following system specification. Processor : Intel Core 2 Quad @ 2.5 GHz RAM : 3GB Operating system : windows 7 Mat lab version : R 2014a version 8.3 The optimization process is done by selecting the parameters in welding such as current, gas flow rate, flux coating, electrode diameter, time for welding etc. The duplex stainless steel 2205 is subjected to TIG welding by varying oxide fluxes for better results. The filler material and fluxes used for the analysis of weld are filler material made of ER316L and fluxes used some are SiO2, TiO2 and mixture of both fluxes SiO2 and TiO2. The titanium dioxide TiO2 and silicon dioxide SiO2 are widely used flux has better result on performance while comparing with other oxide fluxes. The test evaluation of the welding process with input parameters and analysis of variance is elaborated in the following sections. Experimentation of analysis of variance and orthogonal L9 array are elaborated in the appendix section respectively. Table 7: Test Evaluation of ANOVA S.NO

Flux

Time

Current 16

(s)

1 2 3

SiO2 100 TiO2 130 Mixture 160

(A)

Electrode Diameter (mm) 2 2.4 3

150 200 250

S/N Ratio -2.93 -2.87 -2.89

The table 7, shows the test evaluation of ANOVA and the test is conducted with three level of sets with input current 150A, 200A, 250A with metal diameter 2mm, 2.4mm, 3mm respectively. During test each level increase of parameters like penetration depth, weld pool geometry and some levels have no effect. By conducting the test can improve the mechanical properties like tensile strength, hardness, ductility and quality of weld etc. ANOVA Analysis of Welding Parameters Welding process parameters can be investigated by ANOVA to verify the parameters that significantly affected the quality characteristic. We conducted the ANOVA analysis for a standard DSS 2205 grade material and the properties were obtained as follows Table 8: ANOVA Result of Depth SOURCE

DEGREE OF

SUM OF SQUARES (SS)

MEAN SQUARE (MS)

F-STAT

P-VALUE

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

3

74532.4

74509.8

74544.4

24844.1

24836.6

24848.1

14.75

14.7

14.7

0.00216

0.0013

0.0013

8

13467.1

13467.1

13467.2

1683.3

1683.3

1683.4

11

87999.5

87976.9

88011.6

FREEDOM (DF) B/W Groups Within Groups Total

Table 9: ANOVA Result of UTS SOURCE

DEGREE OF

SUM OF SQUARES (SS)

MEAN SQUARE (MS)

F-STAT

P-VALUE

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

4

1069435

876785.3

10459.9

26735

219196

261478

195.70

138.6

191.5

0

0

0

10

1366

15805.8

13647

1366

1580.5

1364.7

14

1083096

892591.1

105956

FREEDOM (DF) B/W Groups Within Groups Total

Table 10: ANOVA Result of Hardness 17

SOURCE

DEGREE OF

SUM OF SQUARES (SS)

MEAN SQUARE (MS)

F-STAT

P-VALUE

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

SiO2

TiO2

Mixture

4

7456

74846.1

74941.1

18641

18711.5

18735.2

13.824

13.885

13.909

0.0004

0.0004

0.0004

10

13485

13475.1

13469.2

13458.5

1347.5

1346.9

14

88053.1

88321.2

88410.3

FREEDOM (DF) B/W Groups Within Groups Total

In table 8 to 10, the Anova result of depth, UTS and hardness is analyzed and its efficiency measures are tabulated in the given section respectively. In this result tabulation the P-value, sum of squares, means square, F-statistic values are also noted.

Table 11: ANOVA Analysis Results for The Hardness of TIG welded joints Grade

Tensile Yield Elongation Strength(MPa) Strength(MPa) (%) DSS 2205 621 448 25

HRC 31

Table 11, describes the ANOVA analysis result of a standard specimen and the results regarding the tensile strength, yield strength, elongation and hardness is tabulated. From the table it can be identified that ANOVA results are much similar with the grey relational analysis results.

Fig 5: Mean S/N Ratio vs Inputs 18

The quality of the weld product can be quantified in terms of noise factor and signal factor is defined in the figure 5 above. In this graph the quality of weld is quantified by taking the mean of S/N ratio to the welding parameters such as flux, current, time and electrode diameter. The S/N ratio is taken for oxide flux material used in welding process. The result shows the titanium dioxide flux has -2.87 signal to noise ratio with the time induced of 100 seconds for the processing. The amount of feed current has S/N ratio of -2.9 with 200 ampere output current and the electrode diameter of 2mm has higher S/N ratio for the optimum quality of weld.

Fig 6: Mean of Means vs Inputs The mean of means is the total mean for the response in the experiments are considered. The figure 6 shows the mean for the welding parameters such as fluxes, time, current, and electrode diameter is taken. The mean value has a range of 350 to 450. The mean for the titanium dioxide flux has lower in the range closer to 350 and higher for silicon dioxide of range 425. The time mean for the welding process has higher of 400 with time of 125 seconds. The mean for the weld current induced on the process has higher of 410 with maximum current of 250 ampere and the electrode diameter with higher mean of 387 with diameter of 2mm.

19

Fig 7: Mean Hardness vs Inputs The figure 7 shows that the hardness of weld material is carried in the Brinell hardness tester. The flux materials of SiO2, TiO2 and mixture of two is taken for the reference. The hardness of SiO2 flux material has higher hardness value of 93.7 and TiO2 has 90.1 and mixture of 92.8 in the hardness tester. The hardness of weld material can be improved by the varying the time, current and electrode diameter of the weld material. The parameters of current, time and electrode diameter range higher of 92.8, 93.1, 92.3 respectively. The Brinell hardness number is calculated using the equation below BHN 

2F  D D  

2

D d

2

(17)

 

Where, F is force applied on the weld metal, D is the indenter diameter and d is the indentation diameter.

20

. Fig 8: Mean of UTS vs Inputs The above figure 8 shows the ultimate tensile strength of weld metal using welding parameters such as flux material, time, current, electrode diameter is taken for the tensile testing. The testing is carried out in universal testing machine. The oxide fluxes forms a major role in tensile strength of weld material. The SiO2 flux has higher tensile strength of 820 N/mm2 with varying time 100 seconds, input current closer to 150 ampere and electrode diameter of 2mm.

Fig 9: Mean Depth vs Inputs The depth of penetration of weld pool in tungsten inert gas arc welding is described in the figure 9. Generally the weld pool in TIG welding ranges from 2.5mm to 3.5mm. In this paper the SiO2 flux usage in the welding forms the depth of penetration ranging from 5.5mm to 6mm with welding time, current and electrode diameter are 100 seconds, 150 ampere and 2mm respectively.

21

The depth of penetration for time, current, and electrode diameter are ranged from 4mm to 5.5mm respectively. During the learning of neural networks, a batch mode of training had been adopted. As the set of 32 input–output data (with the help of which the regression analysis had been carried out) might not be sufficient to provide proper training to the network, one thousand input–output data had been generated at random by using the above regression equations. Results of the neural networkbased approaches developed to model the input–output relationships in TIG welding process are stated and explained below. The performance of a back-propagation neural network (BPNN) depends on the quality and quantity of data used in training. It is also dependent on its architecture, connecting weights, learning rate, momentum constant, coefficients of transfer functions (TFs), bias value. To determine an optimal set of the above parameters, a study was carried out, by varying one parameter at a time and keeping the other parameters unaltered. In the first stage, the number of neurons to be lying in the hidden layer was varied in the range of 5–30, keeping the other parameters, viz. learning rate η, momentum constant α, coefficient of transfer function of the input neurons a1, coefficient of transfer function of the hidden neurons a2, coefficient of transfer function of the output neurons a3, and bias value fixed to 0.5, 0.5, 1.0, 1.0, 1.0 and 0.0005, respectively. It is interesting to note that the NN with 22 neurons lying in its hidden layer showed the best performance in terms of mean squared deviation in prediction. Thus, in the second stage and on-wards, the number of hidden neurons was kept fixed to 22. In the similar way, the optimal/near-optimal values of η, α, a1, a2, a3 and bias value were determined in stages. As the parameters were determined in stages (but not simultaneously), there is no guarantee that the obtained network will be globally optimal but it could be a near-optimal one. Table 12: The Parameters of Optimal Network No. of neurons in the hidden layer

22

Learning rate, η

0.35

Momentum constant, α

0.40

Coefficient of TF of the input neurons

0.15

Coefficient of TF of the hidden neurons

1.6

Coefficient of TF of the output neurons

0.90

Bias value

0.0005

The table 12, demonstrates the parameters of the optimal network, which includes number of neurons in the hidden layer, learning rate, momentum constant, coefficient of TF of the input 22

neurons, coefficient of TF of the hidden neurons, coefficient of TF of the output neurons and bias value. 6. CONCLUSION The optimization of ultimate tensile strength, hardness and depth of weld are the main objectives discussed in this paper. Defects are mainly occurred due to the improper fusion of weld form on the metal surface. Welding of DSS 2205 is carried out by tungsten inert gas arc welding by the filler material made of ER316L is discussed in this paper. After the weld the metal is subjected to tensile testing by universal testing machine where the metal brittle by the poor quality of weld. It is mainly occur due to current, gas flow, fusion time and oxide fluxes. To optimize the tensile strength of weld metal taguchi method of orthogonal array and analysis of variance is carried out to optimize the tensile strength and weld quality of the base metal. The final step is analysis which is undergone by Mat Lab where the tested parameter of tensile testing and welding is taken for the reference of analysis and result are plotted in simulated plot graphs. For the forthcoming of welding the base metal and filler can be replaced with good composition grade and welding equipment is upgraded for the better surface finish of the metal. The experiment on metal welding can be continued by optimizing the corrosion formation on the weld area by optimizing of better standards in coating over the flux material and base metal. Thus the recent technology in metal joining process discussed on this paper can improve the hardness and by increasing the tensile strength of metal. By comparing recent research on welding the neural network and taguchi method is an effective method in optimization of welding process. REFERENCE [1]M. Ericsson, R. Sandstorm, Influence of welding speed on the fatigue of friction stir welds, and comparison with MIG and TIG, International Journal of Fatigue. 25 (12) (2003)1379-1387. [2]Rizvi, S. Ali, S.P. Tewari, W. Ali, Weldability of Steels and its Alloys under Different Conditions-A Review, International Journal of Science, Engineering and Technology Research. 2 (3) (2013) 539. [3]D.D. Kumar, D.B. Jabaraj, Research on Gas Tungsten Arc Welding of Stainless Steel-An overview International Journal of Scientific & Engineering Research. 5 (1) (2014)1612. [4]L. Grad, J. Grum, I. Polajnar, J.M. Slabe, Feasibility Study of Acoustic Signals for on-line Monitoring in Short Circuit Gas Metal Arc Welding. International Journal of Machine Tools and Manufacture 44(5) (2004) 555-561. [5]H.G. Fan, H.L. Tsai, S.J. Na, Heat Transfer and Fluid Flow in a Partially or Fully Penetrated Weld Pool in Gas Tungsten Arc Welding. International Journal of Heat and Mass Transfer. 44 (2) (2001) 417-428. [6]J. Tusek, M. Suban, Experimental research of the effect of hydrogen in argon as a shielding gas in arc welding of high alloy stainless steel, International Journal of Hydrogen Energy. 25(2000) 4 369-376. [7]H. A. Khalid, A. Lateef, M. Javed, T. Pramesh, Influence of welding speed on tensile strength of welded joint in TIG welding process, International Journal of Applied Engineering Research. 1(3) (2010) 518. [8]Y.S Tarng, H.L. Tsai, S.S. Yeh, Modelling, Optimization and Classification of Weld Quality in Tungsten Inert Gas Welding, International Journal of Machine Tools and Manufacture. 39 (9) (1999) 1427-1438.

23

[9]H.E. Beardsley, Y.M. Zhang, R. Kovacevic, Infrared Sensing of Full Penetration State in Gas Tungsten Arc Welding, International Journal of Machine Tools and Manufacture. 34 (8) (1994) 1079-1090. [10]S. Mukhopadhyay, T.K. Pal, Effect of Shielding Gas Mixture on Gas Metal Arc Welding of HSLA Steel Using Solid and Flux-Cored Wires, International Journal of Advanced Manufacturing Technology. 29 (3-4) (2006) 262-268. [11]LS Kim, C.E. Park, Y.K. Jeong, J.S. Son, Development of an Intelligent System for Selection of the Process Variables in Gas Metal Arc Welding Process, International Journal of Advanced Manufacturing Technology 18(2) (2001) 98-102. [12]A.K. Lakshminarayanan, K. Shanmugam, V.Balasubramanian, Effect of Autogenous Arc Welding Processes on Tensile and Impact Properties of Ferritic Stainless Steel Joints, International Journal of Iron and Steel Research. 16 (1) (2009) 62-16. [13]F. Wang, S.Williams, M. Rush, Morphology Investigation on Direct Current Pulsed Gas Tungsten Arc Welded Additive Layer Manufactured Ti6Al4V Alloy, International Journal of Advanced Manufacturing Technology. 57 (5-8) (2011) 597-603. [14]A. Farzadi, S. Serajzadeh, A.H. Kokabi, Modelling of Heat Transfer and Fluid Flow during Gas Tungsten Arc Welding of Commercial Pure Aluminium, International Journal of Advanced Manufacturing Technology 38(3-4) (2008) 258-267. [15]P.J. Singh, D.R.G. Achar, B. Guha, H. Nordberg, Fatigue life Prediction of Gas Tungsten Arc Welded AISI 304L Cruciform Joints with Different LOP Sizes, International Journal of Fatigue. 25(1) (2003) 1-7. [16]Chakraborty, Nilanjan, S. Chakraborty, P. Dutta, Modelling of Turbulent Transport in Arc Welding Pools, International Journal of Numerical Methods for Heat & Fluid Flow. 13 (1) (2003) 7-30. [17]M.Balasubramanian, V.Jayabalan, V.Balasubramanian, Optimizing Pulsed Current Parameters To Minimize Corrosion Rate In Gas Tungsten Arc Welded Titanium Alloy, International Journal of Advanced Manufacturing Technology. 39(5-6) (2008) 474-481. [18]K. Kishore, P.V. Gopal Krishna, K. Veladri, G.K. Kumar, Analysis of defects in gas shielded arc welding of AA 6351 using Taguchi methods, International Journal of Applied Engineering Research. 5(3) (2010) 393-399. [19]C.A. Anoop, P. Kumar, Application of Taguchi Methods and ANOVA in GTAW Process Parameters Optimization for Aluminium Alloy 7039, International Journal of Engineering and Innovative Technology (IJEIT), 2 (2013) 11. [20]K.N. Naik, M.Vasudevan, K.R. Balasubramanian, Multi-objective optimization of activated tungsten inert gas welding of duplex stainless steel using response surface methodology, International Journal of Advanced Manufacturing Technology. 77 (1-4) (2015) 67-81. [21]Y. S. Tarng, S. S. Yeh, S. C. Juang, Fuzzy Pattern Recognition of Tungsten Inert Gas Weld Quality, International Journal of Advanced Manufacturing Technology. 13(6) (1997) 387-392. [22]K.Kalaiselvan, A.Elango, N.M.Nagarajan, Artificial Neural Network Application on Ti-Al Joint Using Laser Beam Welding–A Review, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering. 8(8) (2014) 1477-1481. [23]Sathiya, Paulraj, S. Aravindan, and A.N. Haq. "Optimization for friction welding parameters with multiple performance characteristics." International Journal of Mechanics and Materials in Design 3.4 (2006): 309-318.

24

[24]E. Gharibshahiyan, A.H. Raouf, N. Parvin, M. Rahimian “The effect of microstructure on hardness and toughness of low carbon welded steel using inert gas welding” Science direct Materials and Design 32 (2011) 2042–2048. [25]I. A. Ibrahim, S. A. Mohamat, A. Amir, A. Ghalib “The effect of Gas Metal Arc Welding (GMAW) processes on different welding parameters”. [26]E. Karadeniz, U. Ozsarac, C. Yildiz “The effect of process parameters on penetration in gas metal arc welding Processes” Science direct Materials and Design 28 (2007) 649–656.

Conflict of Interest Statement

We, Authors A. Balaram Naik and A. Chennakeshava Reddy stated that there is no conflict of interest between us in the preparation and the submission of manuscript. Highlights 

TIG welding is an improved technique used for manufacturing different components.



The conceptual model can increase the property and lowers error values in welding.



The optimization is undergone by neural networking which is an efficient process.



The taguchi method and ANOVA is utilized for finding the best optimized value.



Thus the above criteria forms most appropriate method for TIG welding of materials.

25