Optimization of Process Parameters to Minimize Angular ...€¦ · the welding process control...

Sudhakaran .R et al. / International Journal of Engineering Science and Technology Vol. 2(5), 2010, 731-748

Optimization of Process Parameters to Minimize Angular Distortion in Gas

Tungsten Arc Welded Stainless Steel 202 Grade Plates Using Genetic Algorithms

SUDHAKARAN .R *, VEL MURUGAN .V**, SIVA SAKTHIVEL. P. S ***

*Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore – 641006,

Tamilnadu, India, Ph: 91 – 422 – 2669401 (O), Fax: 91 – 422 – 2669406 Mobile: 91 – 9894030121, [email protected]

**Department of Aeronautical Engineering, Kumaraguru College of Technology, Coimbatore – 641006, Tamilnadu, India, [email protected]

***Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore – 641006, Tamilnadu, India

Abstract

This paper presents a study on optimization of process parameters using genetic algorithm to minimize angular distortion in 202 grade stainless steel gas tungsten arc welded plates. Angular distortion is a major problem and most pronounced among different types of distortion in butt welded plates. The extent of distortion depends on the welding process control parameters. The important process control parameters chosen for study are gun angle (θ), welding speed (V), plate length (L), welding current (I) and gas flow rate (Q). The experiments are conducted based on five factor five level central composite rotatable designs with full replication technique. A mathematical model was developed correlating the process parameters and the angular distortion. The developed model is checked for the adequacy based on ANOVA analysis and accuracy of prediction by confirmatory test. The optimization of process parameters was done using genetic algorithms (GA). A source code was developed using C language to do the optimization. The optimal process parameters gave a value of 0.000379° for angular distortion which demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of producing weld with minimum distortion. Key words: Angular distortion, genetic algorithm, design of experiments, gas tungsten arc welding. 1. Introduction Gas Tungsten Arc Welding is an arc welding process that produces coalescence of metals by heating them with an arc between a non consumable electrode and base metal. GTAW process is suitable for joining thin and medium thickness materials like stainless steel sheets and for applications where metallurgical control of the weld metal is critical. SS 202 grade has wide applications in making seamless stainless tubes for boilers, heat exchanger tubes, super heater tubes, cookware etc. In arc welding processes, due to rapid heating and cooling the work piece undergoes an uneven expansion and contraction in all the directions. This leads to distortion in all the directions of the work piece .Angular distortion or out of plane distortion is one such defect that makes the work piece distort in angular directions around the weld interface. Post weld treatment is required to eliminate the distortion so that the work piece is defect free and accepted. The extent of angular distortion is directly influenced by the welding input parameters during the welding process [1]. One of the methods to remove the angular distortion during the fabrication process is to provide an initial angular distortion in the negative direction. If an exact magnitude of angular distortion is predicted, then a weld with no angular distortion would be the result. It is difficult to obtain analytical solution to predict angular distortion. Costly and time consuming experiments are required in order to determine the optimum welding process parameters due to complex nature of the welding process. Several works have been done for optimizing welding process parameters using conventional optimization techniques. These conventional techniques require more computational effort and time [2]. GA is widely used as the optimization method in evolutionary computation. It is an optimization and search technique based on the principles of genetics and natural selection. The GA searches for an optimum solution, from a population composed of many individuals according to an objective function which is used to establish the fitness of each candidate as a solution. Watanabe and Satosh [4] used a

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combination of empirical and analytical methods to study the effects of welding conditions on the distortion in the welded structures. Mandal and Parmar [5] used a statistical method of two level full factorial techniques to develop mathematical model and reported that welding speed has a positive effect on angular distortion. Gunaraj and VeL Murugan [6] studied the effect of process parameters on angular distortion in gas metal arc welding of structural steel plates. They developed a mathematical model for angular distortion based on five level five factorial central composite designs. They studied the effect of process parameters on angular distortion. Huajun Zhang et.al [7] made fundamental studies in process controlling of angular distortion in asymmetrical double sided double arc welding (ADSAW). They conducted experiments to investigate the thermal character and the effects of arc distance and welding parameters on the angular distortion in ADSAW. They developed a 3D finite element model to simulate transient temperature and welding deformation with different arc distance and heat input. They concluded that simulated results are in good agreement with the experimental measurements. Giridharan and Murugan [8] carried out the optimization of pulsed gas tungsten arc welding process parameters to obtain optimum weld bead geometry with full penetration in welding of stainless steel (304L) sheets of 3mm thickness. They developed mathematical model correlating the important controllable pulsed GTAW process parameters with weld bead parameters such as penetration, bead width, aspect ratio and weld bead area of the weld. Using the mathematical model they studied the main and interaction effects of the pulsed GTAW process parameters on weld bead parameters. They employed Quasi – Newton numerical optimization technique to solve the optimization problem. Correia et.al [9] explored the possibility of using GA as a method to decide near optimal settings of a GMAW process. Their problem was to choose best values of three control variables on four quality responses i.e, deposition efficiency, bead width, depth of penetration and reinforcement inside a previous delimited experimental region. From their study they concluded that GA was able to locate near optimum conditions, with relatively small number of experiments. Kumar and Sundarajan [10] studied the effect of welding parameters on mechanical properties and optimization of pulsed TIG welding of Al – mg – si alloy. They employed Taguchi method to optimize the pulsed TIG welding parameters of heat treatable (Al – mg – si) aluminium alloy weldments for maximizing the mechanical properties. III – Soo Kim et.al [11] optimized the bead width for multipass welding in robotic arc welding processes. They developed new algorithms to establish mathematical models for optimizing bead width for multi pass welding by both neural network and multi – pass welding by both neural network and multiple regression methods. They concluded that the proposed model was able to predict bead width with reasonable accuracy but the neural network model performed better than empirical models. Palani and Murugan [12] optimized the weld bead geometry of stainless steel cladding by flux cored arc welding. They developed mathematical model to predict weld bead dimensions. They optimized the process parameters using excel solver to achieve maximum dilution, maximum reinforcement, minimum penetration, maximum bead width. Kannan and Murugan [2] optimized flux cored arc welding process parameters using particle swarm optimization (PSO). They developed mathematical models using multiple regression method and optimized percentage dilution using PSO. Control of distortion in robotic CO2 – shielded flux cored arc welding was investigated by Arya and Parmar [13]. A three level fractional factorial technique was used to predict the angular distortion in 10 mm thick low carbon steel. The effect of arc voltage, wire feed rate, welding speed and groove angle on the angular distortion in single vee but welds was investigated with and without sealing run. It was concluded that the models developed were fairly accurate and can be usefully employed for controlling the angular distortion in automated welding process. Application of design of experiments for the study of effect of process parameters on weld bead geometry was widely reported but a mathematical model correlating GTAW process parameters with angular distortion for 202 grade stainless steel plates was reported. There is very little published information available with regard to optimization of process parameters using GA for minimizing angular distortion in 202 grade stainless steel plates. Hence an attempt was made to correlate important GTAW process parameters such as welding gun angle, welding speed, plate length, welding current and gas flow rate with angular distortion. A statistically designed experiment based on central composite rotatable design was employed for the development of models [14]. The model developed was very useful to predict and also to optimize the GTAW process parameters for minimum angular distortion. In the optimization procedure, the angular distortion was taken as objective function with the limits of the process parameters as constraints. The optimization of process parameters was done using genetic algorithms (GA). GA was chosen due to its simplicity, ease of operation, minimal requirements and global perspective. The optimal process parameters gave a value of 0.000379° for angular distortion. The conformity test was also conducted to verify the optimized results and the percentage error was calculated. The error percentage was within the permissible limit of 5%. This demonstrates the accuracy and effectiveness of the model presented and program developed.

2. Experimental Procedure

The experiments were designed based on five factor five level central composite rotatable designs with full replication technique [15]. These experiments were conducted as per the design matrix using Lincoln V 350 Pro electric digital welding machine. A servo motor driven manipulator was used to maintain uniform welding

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speed. The main experimental set up used consists of a traveling carriage with a table for supporting the specimens. A power source is present. A welding gun is held stationary in a frame above the table and it is provided with an attachment for setting the required nozzle to plate distance and welding gun angle respectively. The welding machine used for conducting the experiment is shown in Fig.1.

Fig. 1 Welding machine

Test plates of following sizes (100mmX35mmX3mm), (125mmX35mmX3mm), (150mmX35mmX3mm), (175mmX35mmX3mm) and (200mmX35mmX3mm) are cut from grade 202 stainless steel plates and one surface was cleaned to remove oxide scale and dirt before welding. The chemical composition of AISI stainless steel plate is given in Table 1.

Table.1. Chemical composition of stainless steel 202 grade

SAE Designation

UNS Designation

%C %Mn %Si %Cr %Ni %P %S %N %Fe

202 S20200 0.15 9.25 0.49 17.1 4.1 0.06 0.03 0.25 70.01

3. Plan of Investigation The research was carried out in the following steps.

1. Identification of process parameters 2. Finding the limits of the process parameters 3. Developing the design matrix 4. Conducting the experiments 5. Recording the response i.e. angular distortion 6. Developing the mathematical model 7. Checking thee validity and adequacy of the model 8. Developing GA algorithm and source code 9. Optimization of the process parameters and responses

3.1. Identification of Process Parameters The independently controllable process parameters affecting the angular distortion were identified to enable the carrying out of experimental work and developing the mathematical model. These are welding gun angle (θ), welding speed (V), plate length (L), welding current (I), gas flow rate (Q).

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3.2 Finding the limits of the process parameters

Table 2 Welding Parameters and their levels

Parameter Unit Notation Levels -2 -1 0 1 2

Gun Angle Deg θ 50 60 70 80 90 Welding gun velocity

mm/min V 80 90 100 110 120

Plate Length mm L 100 125 150 175 200 Current Amperage I 70 80 90 100 110 Gas flow rate liter/min Q 5 10 15 20 25

The working ranges of all selected factors are fixed by conducting trail runs. This was carried out by varying one of the factors while keeping the rest of them as constant values. The working range of each process parameters was decided upon by inspecting the bead for a smooth appearance without any visible defects such as surface porosity, undercut etc. The upper limit of a given factor was coded as +2 and the lower limit was coded as -2. The coded values for intermediate values were calculated using the equation (1).

minXmaxX

))minXmax(X2(2X

iX

(1)

Where Xi is the required coded value of a variable X and is any value of the variable from Xmin to Xmax. The selected process parameters with their limits and notations are given in Table.2.

3.3 Development of Design Matrix

The design matrix chosen to conduct the experiments was a five level, five factor central composite rotatable designs consisting of 32 sets of coded conditions and comprising a half replication 24 =16 factorial design plus 6 centre points and 10 star points. All welding variables at the intermediate (0) level constitute the centre points while the combination of each welding variables at either its lower value (-2) or its higher value (+2) with the other three parameters at the intermediate level constitute the star points. Thus the 32 experimental runs allow the estimation of the linear, quadratic and two way interactive effects of the process parameters on the angular distortion. Experiments were conducted at random to avoid schematic errors creeping into the experimental procedure.

3.4 Recording the Responses

The angular distortion was measured using Microscribe G2 coordinate measuring machine. The work piece under observation is clamped to the desk with the help of C- clamp. With the help of trisquare, straight lines are drawn to scribe at three or four places in the work piece. The Microscribe G2 is interfaced with the Rhino 4 software. The limits for the work piece in the X and Y directions are fixed i.e. boundary for indicating the domain of the work piece. The angle β between the two lines is measured as shown in the Fig. 1.The distorted angle α is obtained from the relation 2)180( . Four readings are taken randomly on each welded

plate and the average value is recorded. The measured value of α is given in Table 3.

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Table 3 Design Matrix and Response

Gun angle (θ) Degrees

Welding speed (V) mm/min

Plate length (L) mm

Welding current (I) amps

Gas flow rate (Q)

Angular distortion (α) Degrees

-1 -1 -1 -1 1 7.35 1 -1 -1 -1 -1 0.96

-1 1 -1 -1 -1 9.75 1 1 -1 -1 1 6.48

-1 -1 1 -1 -1 3.68 1 -1 1 -1 1 4.73

-1 1 1 -1 1 0.73 1 1 1 -1 -1 2.88

-1 -1 -1 1 -1 2.72 1 -1 -1 1 1 0.84

-1 1 -1 1 1 5.9 1 1 -1 1 -1 1.09

-1 -1 1 1 1 0.54 1 -1 1 1 -1 5.72

-1 1 1 1 -1 1.41 1 1 1 1 1 1.68

-2 0 0 0 0 6.182 0 0 0 0 4.36 0 -2 0 0 0 2.74 0 2 0 0 0 3.72 0 0 -2 0 0 4.51 0 0 2 0 0 1.12 0 0 0 -2 0 4.58 0 0 0 2 0 0.42 0 0 0 0 -2 3.22 0 0 0 0 2 3.25 0 0 0 0 0 2.91 0 0 0 0 0 2.78 0 0 0 0 0 2.92 0 0 0 0 0 2.910 0 0 0 0 2.84 0 0 0 0 0 2.73

In this table, for experimental runs from 27 to 32 all welding conditions remain the same, the response vary slightly. This is due to the effect of unknown and unpredictable variables called noise factors which creep into the experiments. To account for the impact of these unknown factors on the response repeated runs were included in the design matrix.

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Fig. 2 Angular Distortion Measured from Microscribe G2 Coordinate Measuring Machine

3.5 Development of Mathematical model

A procedure based on regression was used for the development of mathematical model and to predict the angular distortion [16]. The response surface function representing angular distortion can be expressed as α = f

(θ, V, L, I, Q) and the relationship selected is a second order response surface for k factors is given by

2iX

K

1i iibjXiXk

ji

1ji,ijb

k

ki iXibobY

(2)

Where, bo is the free term of the regression equation. The coefficients b1, b2, b3, b4 and b5 are linear terms. The coefficients b11, b22, b33, b44 and b55 are the quadratic terms and the coefficients b12, b13, b14, b15, b23, b24, b25, b34, b35 and b45 are the interaction terms [16]. The values of the coefficients of the polynomial are calculated by regression with the help of the following equations.

YiiX0.034091Y0.159091ob (3)

Y)i(X0.04167ib (4)

Y0.03409Y)ii(X0.002841Y)ii(X0.03125iib (5)

Y)ij(X0.0625ijb (6)

Statistical software package [17] was used to calculate the values of these coefficients. The initial mathematical model was developed using the coefficients obtained from the above equations. The mathematical model is as follows.

IQ 0.250LQ 0.754LI 0.708VQ 0.045VI 0.179

VL 1.207θQ 0.383θI 0.326θL 1.562θV 0.2262Q 0.0932I 0.0912L 0.012

2V 0.0912θ 0.601Q 0.004I 1.041L 0.854V 0.223θ 0.4722.852α

(7)

3.6 Testing the Coefficients for Significance

The value of the regression coefficients gives an idea as to what extent the control parameters affect the response quantitatively. The less significant coefficients are eliminated along with the responses with which they are associated without sacrificing much of the accuracy. This is done by using student’s t – test [18]. According to this test when the calculated value of t corresponding to the coefficient exceeds the standard tabulated value for the probability criterion kept at 0.75, the coefficient becomes significant. The final mathematical model was developed using only the significant coefficients. The final mathematical model as determined by the above analysis is as follows.

IQ 0.250LQ 0.754LI 0.708VQ 0.045VI 0.179

VL 1.207θQ 0.383θI 0.326θL 1.562θV 0.2262Q 0.0942I 0.090

2V 0.0922θ 0.602I 1.041L 0.854V 0.223θ 0.4722.852α

(8)

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The square multiple values of R of the full model and the reduced model are presented in Table 4. It is evident from the table that both the models have the same value of adjusted square multiple R but the reduced model is better than the full model as reduced model have lesser values of standard error of estimate than that of respective full model.

Table 4 Comparison of square multiple ‘R’ values and standard error of estimate for full and reduced models

Response Adjusted square multiple R Standard error estimate

Full model Reduced model Full model Reduced model

Angular distortion 0.999 0.999 0.063 0.061 3.7 Checking the adequacy of the model

The adequacy of the model was tested using the analysis of variance technique (ANOVA). As per this technique [19] the calculated value of the F – ratio of the model developed should not exceed the standard value of F – ratio for a desired level of confidence i.e. 95% and the calculated value of R – ratio of the model developed should exceed the standard tabulated value of the R – ratio for the same confidence level., then the model is considered to be adequate. The results of ANOVA are presented in Table 5. It is evident from the table that the model is adequate.

Table 5 Results of ANOVA Analysis

Parameter First order

term Second order term

Lack of fit Error term F ratio

R ratio Remarks

SS DOF SS DOF SS DOF SS DOF Angular Distortion

50.06 5 97.35 13 0.017 7 0.032 6 0.453 1538.7 Model is adequate

SS - Sum of Squares, DOF- Degree of Freedom Mean Sum of Squares = Sum of Square Terms/DOF F ratio = Ms of Lack of Fit/ Ms of Error Terms R ratio = Ms of First Order Term & Second Order Term/ MS of Error Term F ratio (7, 6, 0.05) = 4.21 R ratio (18, 6, 0.05) = 3.90

3.8 Validation of the model

Conformity tests were conducted with the same experimental set up to validate the accuracy of the model. The results of the conformity test are presented in Table 6.

Table 6 Results of conformity test

Test No

Process Parameter Angular Distortion

θ Degrees V mm/min L mm I amps Q litre/min Observed values

Predicted values

Error %

1 80 110 200 70 5 1.49 1.421 4.63 2 90 120 175 80 10 3.98 4.051 -1.78 3 70 115 112.5 100 20 5.95 5.824 2.11 Mean Error 1.65 From the conformity test, it was found that the developed models are able to predict the angular distortion with a reasonable accuracy. The validity of the model was tested again by drawing scatter diagram which show the closeness between observed and predicted values. The scatter diagram is shown in Fig.2.The results show that for the developed model the accuracy is 95%.

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Fig. 3 Scatter diagram for angular distortion

4. Optimization of angular distortion

The purpose of optimization is to minimize angular distortion and to find the optimum process parameters for minimum angular distortion. The tool used for optimization is GA. The GA is an adaptive search and optimization algorithm that mimics principles of natural genetics. Due to their simplicity, ease of operation, minimum requirements and global perspective Gas have been successfully used in a wide variety of problem domains [20].

4.1 Working principle of GA

The GA is a population based search optimization technique. It has been used as a powerful tool for optimization. The data processed by GA includes a set of strings or chromosomes with an infinite length in which each bit is called an allele (or a gene). A selected number of strings are called population and the population at a given time is known as generation. Generation of the initial population of strings are randomly since the binary alphabet offers the maximum number of schemata per bit of information of any coding, a binary encoding scheme is traditionally used to represent the chromosomes using either zeros or ones. Thereafter the fitness value (objective function value) of each member is computed. The population is then operated by the three main operators namely, reproduction, crossover and mutation to create a new population. The new population is further evaluated and tested for determination. One iteration of these operators is known as generation in the parlance of GA. The current population is checked for acceptability or solution. The iteration is stopped after the completion of maximum number of generations or on the attainment of the best results [21].

4.2 Implementation of GA

Coding: In order to use Gas to solve the problem, variables Xi’s are first coded in some string structures. Binary coded strings having ones and zeros are primarily used. The length of the string is usually determined according to the desired solution accuracy. In this case 20 bits are chosen for gun angle, welding speed, plate length, welding current and gas flow rate. The strings (00000000000000000000) and (11111111111111111111) would represent the point’s lower and upper limits of the process variables. The total string length is 100.

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Table 6 Solution accuracy for the process parameters

Process parameter

Limits Code Decode Range Accuracy

Gun angle (θ) Degrees

50 to 90

00000000000000000000 11111111111111111111

0 1048575

40 53.81X101048575

40

Welding speed (V) mm/min

80 to 120

00000000000000000000 11111111111111111111

0 1048575

40 53.81X101048575

40

Plate length (L) mm

100 to 200

00000000000000000000 11111111111111111111

0 1048575

100 5X1053.91048575

100

Welding current (I) amps

70 to 110

00000000000000000000 11111111111111111111

0 1048575

40 53.81X101048575

40

Gas flow rate (Q) litre/min

5 to 25 00000000000000000000 11111111111111111111

0 1048575

120 5X109.11048575

120

The solution accuracy obtained in the given interval for the process parameters along with the coding are shown in the above Table 6. Fitness function: GA imitates the survival of the fittest principle. So, naturally they are suitable to solve maximization problems. Maximization problems are usually transformed to minimization problems by suitable transformation. A fitness function f(x) is derived from the objective function and is used in successive genetic operations. The minimization problem is an equivalent maximization problem such that the optimum point

remains unchanged. The fitness function often used is )(1

1)(

xfxF

(9)

Reproduction: It is the first operator applied on population. Chromosomes are selected from the population to be parents to cross over and produce off spring. In this process individual strings are copied into a separate string called the mating pool according to their fitness value, i.e. the strings with a higher probability of contributing one or more off spring in the next generation. Roulette wheel selection is used for selecting chromosomes for parents to cross over in proportion to its fitness. In this way more highly fit strings have higher numbers of off spring in the succeeding generation. Once the string has been selected for reproduction an extra replica of the string is made. The string is then entered into the mating pool, a tentative new population for further genetic operator action. Crossover: After reproduction, the population is enriched with better individuals from the previous generation but does not have any new ones. A crossover operator is applied to the population to hopefully create better strings. The total number of participative strings in crossover is controlled by the crossover probability, which is the ratio of total strings selected for mating and the population size. The crossover operator is mainly responsible for the search aspects of GA. In order to perform crossover, a random number is generated between 1 and 7. If the random number is 2 the bits after the 2nd position are exchanged. Mutation: After crossover, the strings are subjected to mutation. Mutation of a bit involves flipping it, changing 0 to 1 and vice versa on a bit by bit basis with a small mutation probability of 0.1 to 0.9. The need for mutation is to keep diversity in the population. The GA algorithm for the optimization problem is given below GA algorithm Step 1: Choose a coding to represent problem parameters, a selection operator, a crossover operator and a mutation operator. Step 2: Choose population size (n), crossover probability (Pc) and mutation probability (Pm). Step 3: Initialize a random population of strings of size L. Choose a maximum allowable generation number Gmax. Set G = 0. Evaluate each string in population. Step 4: If G>Gmax or other termination criteria is satisfied, terminate or perform reproduction on the population. Step 5: Perform crossover on random pair of strings Step 6: Perform mutation on every string Step 7: Evaluate strings in the new population, set G = G+1 and go to step 3. Each time after applying the GA operators, a new set of population is created. Then they are decoded and objective function values are calculated. This completes one generation of GA. Such iterations are continued till termination criterion is achieved. The above process is simulated by a computer program with a population size of 100, iterated for 100 generations and cross over and mutation probability are selected to be 0.7 and 0.1 respectively.

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4.3 Results of genetic algorithm

The Fig. 4 shows the results obtained by running the C program. From the figure, it is evident that the minimum distortion is observed at the 95th iteration. The initial variation in the curve is due to the search for optimum solution. The angular distortion is gradually decreasing up to the end of the iteration.

Fig. 4 GA graph

The optimum values of the process variables obtained from GA for 100 iterations are given below Welding gun angle = 53.2° Welding speed = 84 mm/min Plate length = 187.75 mm Welding current = 96 amps Gas flow Rate = 21 litre/min Angular distortion = 0.000379°

5. Results and discussion

The mathematical model developed can be used to predict angular distortion by substituting the values of the respective process parameters. The influence of process parameters on the angular distortion was studied using the developed model. The direct effects of the welding process parameters are studied by keeping all the process parameters at the middle level except the parameter whose direct effect is studied. The interaction of the parameters is studied by keeping all the parameters at the middle level except the parameters whose interaction effects are studied. The direct of all the parameters and the interaction effects of welding process parameters which have strong interaction on angular distortion are discussed below.

5.1 Direct effect of gun angle on angular distortion

The Fig. 5 represents the direct effect of gun angle on angular distortion. From the figure it is clear that the angular distortion decreases for lower gun angles. At lower gun angles the preheating of work piece is less. This results in less depth of penetration and width of the bead. Higher gun angles results in more penetration and width of the bead. An increase in width of the bead will contract more at the top surface of the weld pool. Also at higher gun angles weld metal gets more exposure to the arc which increases the thermal stresses in the heat affected zone. Hence there is an increase in angular distortion.

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Fig. 5 Direct effect of welding gun angle on angular distortion

5.2 Direct effect of welding speed on angular distortion

Artem Pilipenko [22] reported a relationship for angular distortion

2Sh

IV0.13α (10)

Where I is current in Amps, V is voltage, S is welding speed in m/s and h is plate thickness in m. Watanabe and Satoh [4] reported another relationship for angular distortion

Shh

12C

e1m

Shh

11cα (11)

From these two equations, it is clear that the increase in welding speed results in decrease in angular distortion. Welding speed is one of the main factor controlling heat input and the bead width. The bead width and dimensions of heat affected zone decrease with the increase in welding speed. This is because heat input is inversely proportional to welding speed. As width of the bead and heat affected zone decreases with the increase in welding speed, the angular distortion also decreases with the increase in welding speed. This is clearly shown in Fig. 6

.

Fig. 6 Direct effect of welding speed on angular distortion

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5.3 Direct effect of plate length on angular distortion

From the Fig. 7, it is clear that the angular distortion decreases with the increase in plate length. When the plate length increases the plate gets stiffened. This resists distortion. Also when the plate length increases there is more length available for the thermal stresses in the heat affected zone to get distributed. Hence the cumulative effects of the above two factors results in decrease in distortion for the corresponding increase in plate length.

Fig. 7 Direct effect of plate length on angular distortion

5.4 Direct effect of welding current on angular distortion

From Artem Pilipenko [22] the heat input is directly proportional to angular distortion. When the welding current increases the heat input increases. The increase in heat input results in preheating of the work piece during the forward welding. This also results in more penetration and width of the bead. The increase in welding current also increases the thermal stresses in the heat affected zone. The cumulative affect of the above two factors results in increase in angular distortion for the corresponding increase in welding current. This is shown in Fig. 8

Fig. 8 Direct effect of welding current on angular distortion

5.4 Direct effect of gas flow rate on angular distortion

The Fig.9 shows the direct effect of gas flow rate on angular distortion. When the gas flow is varied from the lower level to higher level there is a decreasing trend in angular distortion up to the middle level and then it increases back to its initial value. This is because initially as the gas flow rate increase more heat is carried by the gas. This results in less depth of penetration and decrease in dimensions of the heat affected zone. Hence

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there is a decrease in angular distortion but as the gas flow increases the heat input increases with high arc energy. This results in formation of wide weld pool and high shrinkage which increases distortion.

Fig. 9 Direct effect of gas flow rate on angular distortion

5.5 Interactive effect of gun angle and plate length on angular distortion

The Fig. 10 represents the interactive effect of gun angle and welding speed on angular distortion. From the figure it is clear that α decreases for varying gun angle from 50° to 70° with the increase in plate length. The rate of decrease is very high i.e. about 15° whereas the rate of decrease is 9° for θ = 60° and 3.5° for θ = 70°. This is due to the fact that thermal stresses induced in the heat affected zone are less at lower gun angles. The value of α increases for θ = 80° and θ = 90° with the increase in plate length. This is because increase in gun angle results in more exposure of parent metal to the arc which increases the thermal stresses in the heat affected zone. Increase in plate length always results in decrease in α. These effects are further explained with the help of a response surface, as shown in Fig. 11. From the contour surface, it is noted that α is maximum when θ and L are at the -2 level. The value of α reaches a minimum when θ is maintained at -2 level and plate length is at (+2) level.

Fig.10 Interactive effect of plate length and welding gun angle on angular distortion.

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Fig. 11 Response surface for interaction effect of gun angle and plate length on angular distortion

5.6 Interactive effect of welding speed and plate length on angular distortion

The Fig. 12 shows the interactive effect of welding speed and plate length on angular distortion. From the figure, it is clear that α increases by 6.24° when the welding speed is at the lower level (-2) for all levels of plate length. The increase in α decrease to 1.41°, when the welding speed is at (-1) level and the plate length is varied from (-2) to (+2) level. This is because welding speed at lower levels has slight positive effect on α than plate length. The trend changes for other three levels of welding speed. When the welding speed is at (0), (1) and (2) levels α decrease for all levels of plate length. It decreases by 3.5° when the welding speed is at the middle level (0) and by 8° when the welding speed is at (+1) level. It further drops to 13° when the welding speed is at (+2) level. In all the above cases the plate length is varied from (-2) to (+2) level. This is due to the fact that welding speed at higher level and plate length has negative effect on α. These effects are further explained with the help of a response surface plot shown in Fig. 13. The value of α reaches maximum when the welding speed is maintained at (+1) level and the plate length is maintained at (-2) level. When both the welding speed and plate length are maintained in the range of (+1) to (+2) level it reaches minimum.

.

Fig. 12 Interactive effect of plate length and welding speed on angular distortion

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Fig. 13 Response surface for interactive effect of plate length and welding speed on angular distortion

5.7 Interactive effect of plate length and welding current on angular distortion

The Fig. 14 shows the interactive effect of plate length and welding current on angular distortion. When the plate length is maintained at (-2), (-1), (0), and (+1) levels, there is a decrease in angular distortion for all levels of welding current. It decreases by 10° when the plate length is at the lower level (-2) and by 7° when plate length is at (-1) level. The decrease in α drops by 4° and 1.3° as the plate length is at (0) and (+1) level respectively for all levels of welding current. There is a marginal increase in α from 0.02° to 1.5° as the plate length is at (+2) level and the welding current is varied from (-2) to (+2) level. The above effects are due to the combined effects of plate length and welding current on angular distortion. These effects are further explained with the help of a response surface plot as shown in Fig. 15. α reaches maximum as the plate length and welding current are at lower level (-2). It reaches minimum as the plate length is maintained at (+2) level and welding current is at (-2) level.

Fig. 14 Interactive effect of plate length and welding current on angular distortion

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Fig. 15 Response surface for interactive effect of plate length and welding current on angular distortion.

5.8 Interactive effect of plate length and gas flow rate on angular distortion

The Fig. 16 shows the interactive effect of plate length and gas flow rate on angular distortion. When the plate length is maintained at (-2) level α increases from 1.9° to 7.9° for all levels of gas flow rate. When the plate length is maintained at (-1) level α increases from 2.5° to 5.5° for all levels of gas flow rate. The trend changes as the plate length is maintained at (0) level. The value of α decreases from 3.2° to 2.8° for the corresponding increase in gas flow rate from (-2) to (0) level. It then increases again to 3.2° for the increase in gas flow rate from (0) to (+2) level. The angular distortion decreases from 3.8° to 0.8° for the plate length at (+1) level and the gas flow rate is varied from (-2) to (+2) level. It further decreases by 6° for the plate length at (+2) level for all levels of gas flow rate. These effects are because as the plate length is increased the plate gets stiffened. Also when the plate length increases there is more length available for the thermal stresses in the heat affected zone to get distributed. This resists distortion. The gas flow has a positive effect on α because gas flow increases the heat input with high arc energy. This results in formation of wide weld pool and high shrinkage which increases distortion. At lower plate lengths α tends to increase because the effect of gas flow is more significant than plate length whereas the effect of plate length is more significant than gas flow at higher plate lengths. These effects are further explained with the help of a response surface plot shown in Fig. 17. α reaches maximum as the plate length is maintained at (-2) level and gas flow rate is in the range of (+1) to (+2) level. It reaches minimum as the plate length is at (+2) level and gas flow is in the range of (0) to (+1) level.

Fig. 16 Interactive effect of plate length and gas flow rate on angular distortion

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Fig. 17 Response surface for interactive effect of plate length and gas flow rate on angular distortion

6. Conclusions

The following conclusions were arrived at from the investigation. 1. The second order quadratic model can be effectively used to predict angular distortion in gas tungsten

arc welding of stainless steel 202 grade plates. 2. Central composite designs can be conveniently used to predict the direct and interactive effects of

different combinations of process parameters within the range of investigation. 3. The predicted angular distortion is compared with the experimental value and the deviation falls within

the limit of 95% confidence level. 4. The maximum angular distortion obtained from experimental studies is 10° when the process

parameters such as gun angle, gas flow rate, welding current and plate length are maintained at 60°, 10 litre/min, 80 amps and 125 mm respectively and the welding speed is maintained at 110 mm/min.

5. The minimum angular distortion obtained from experimental studies is 0.4° when the process parameters such as gun angle, welding speed, plate length and gas flow rate are maintained at 70° , 100 mm/min, 150 mm and 15 litre/min respectively and the welding current is maintained at 90 amps.

6. Out of the five process parameters selected for investigation, welding current has strong effect on angular distortion; plate length, welding speed has a negative effect on angular distortion.

7. Out of the different combination of process parameters, gun angle and plate length, welding speed and plate length, welding current and plate length and gas flow rate and plate length has strong interaction on angular distortion.

8. The optimization of process parameters was done using GA and an algorithm was successfully developed using C language to do the optimization

9. The optimal process parameters gave a value of 0.000379° for angular distortion which demonstrates the accuracy and effectiveness of the model developed.

10. With GA based optimization used in this work, it would be possible to minimize the angular distortion by using optimal process parameters.

Acknowledgement

The author wishes to thank All India Council for Technical Education, New Delhi, India for sponsoring this research project.

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