Process parameter optimization of gas metal arc welding on IS:2062 mild steel using response surface methodology

Journal of Manufacturing Processes 25 (2017) 296–305

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Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro

Process parameter optimization of gas metal arc welding on IS:2062 mild steel using response surface methodology Shekhar Srivastava ∗ , R.K. Garg Department of Industrial & Production Engineering, NIT Jalandhar, Punjab 144008, India

a r t i c l e

i n f o

Article history: Received 24 July 2016 Received in revised form 18 December 2016 Accepted 22 December 2016 Keywords: Bead height Bead width Box Behnken Design Depth of penetration Gas metal arc welding Mild steel

a b s t r a c t Quality of weld mainly depends on mechanical properties of the weld metal and heat affected zone (HAZ), which isin direct relation to the type of welding and its process parameters.Bead geometry variables, heat affected zone, bead width, bead height, penetration and area of penetration are greatly influenced by welding process parameter i.e. welding speed, welding current, shielding gas flow rate, voltage, arc travel speed, contact tip – work distance, type of shielding gas etc. and also it plays an important role in determining the mechanical properties of the weld such as tensile strength, hardness etc.In this paper, effect of the various process parameters has been studied on welding of IS:2062 mild steel plate using gas metal arc welding process with a copper coated mild steel wire of 0.8 mm diameter. A set of experimentshas been performed to collect the data using Box Behnken Design technique of Response Surface Methodology. Based on the recorded data, the mathematical models have been developed. Further an attempt has been made to minimize the bead width and bead height and maximize the depth of penetration using response surface methodology. © 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

1. Introduction Gas Metal Arc Welding (GMAW) is a versatile welding process that melts and joins metal by heating them with an established arc between a continuously fed filler wire electrode and the metals.The process is used with a shielding provided externally as a flow of inert gas (Argon). In early 1900, the process GMAW was introduced, however for the purpose of industrial usage it was available in 1950 [1]. Primarily it was used for the welding of aluminum because fundamentally at its introduction, it was a high current density, small diameterand bare metal electrode welding process using an inert gas for shielding. As a result of that it is also called as Metal Inert Gas (MIG) Welding. Further process advancement consisted operation at low current densities and pulsed direct current, application to a broader range of materials and the use of reactive gases like CO2 and other gas mixtures. GMAWs are used as automatic and semiautomatic operation modes. Nowadays all commercial metals and alloys such as carbon steels, stainless steels, high strength low alloy steels, alloys of magnesium, copper, aluminum, titanium and nickel can be welded in all positions with this versatile process by choosing appropriate process parameters for

∗ Corresponding author. E-mail address: [email protected] (S. Srivastava).

the particular joint design and process variables [1]. Generally, on the basis of some traditional methods like welder’s experience, process parameter charts and hand books, input welding parameters are defined. But later on, with the help of some welding experts, advanced computer techniques were developed to help welding engineers to find out the appropriate welding conditions [2]. Since the experimental investigation of the optimized process parameters is very costly and time consuming due to number of constraints and the above expert system is not reliable for selecting the welding parameters which gives the satisfactory result. To consider this problem, different methods have been developed to obtain the desired output variable through models which establishes a relation between input and output variables. A widely used method to determine the welding process model is to use experimental design using regression analysis, graphical methods and response surface methodology [3,6,7,21–24]. Tarng and Yang found out the welding process parameters which obtained optimal weld bead geometry in gas tungsten arc welding. They employed Taguchi method to formulate the experimental layout andanalyzed the effect of each welding process parameters on the weld bead geometry as well as predicted an optimal setting for each process parameter [4]. Kim et al. developed an intelligent algorithm to understand the relation between the process parameters and bead height and used that relation to predict process parameters for an optimal bead height through a neural network and multi pass welding process

http://dx.doi.org/10.1016/j.jmapro.2016.12.016 1526-6125/© 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

S. Srivastava, R.K. Garg / Journal of Manufacturing Processes 25 (2017) 296–305

Nomenclature AISI ANOVA BH BW CTWD df DP GF GMAW HAZ IS MIG RSM TS V WF

American Iron & Steel Institute Analysis of Variance bead height bead width contact tip – work distance degree of freedom depth of penetration gas flow rate gas metal arc welding heat affected zone Indian standard metal inert gas response surface methodology travel speed voltage wire feed rate

[5]. Above mentioned statistical tools are more or less accurate in terms of predicting the results from the process parameters. But the results are restricted to those points on which regression analysis is carried out i.e. anchor points. To find out a solution to this problem, Kumar and Debroy presented that multiple sets of welding variables, capable of producing the target weld bead geometry, can be determined in realistic time frame by coupling a real coded genetic algorithm with a neural network model [8]. A genetic algorithm does not require an objective mathematical function should be differential such that even if there is any bad data in the search space, the model does not get affected. But however, this algorithm could not produce a mathematical model between any process parameters and responses as output variables. To deal with this problem, response surface methodology uses the near optimal values as a reference point to obtain a model of the welding process and determine the optimal values of the process variables. Dey et al. performed bead on plate weld on austenitic stainless steel using an electron beam welding machine and formed a constrained optimization problem to minimize weldment area after ensuring the condition of maximum depth of penetration [9]. Sathiya et al. presented bead on plate welding of a super austenitic stainless steel AISI 304L using a gas metal arc welding and optimized it with the help of genetic algorithm. They concluded the result after a confirmation test and comparison [13]. T Udayakumar et al. examined microstructure and mechanical properties of friction welded super duplex stainless steel and concluded that weld transverse tensile failures repeatedly occurred away from the weld zone and shows more hardness, yield and ultimate tensile strengths with respect to the base metals [19]. Ehsan Azarsa and Amir Mostafapour investigated the effect of friction stir welding parameters on flexural strength of high density polyethylene sheets by using response surface methodology and found on the basis of experimental and analytical results that the welding performed at high rotational speed and lower travel speed increases flexural strength by reducing defects [20]. M Sen et al. made an attempt to optimize the DP-GMAW process parameters i.e. mean current, pulse frequency, thermal pulse frequency and standard arc voltage by developing three second order regression model and using response surface methodology [21]. R P Singh et al. found out that weld bead geometry parameters are significantly affected by different polarity as it effects the extent of heat produced at wire – work interface and they concluded that the polarity have significant effect on weld bead geometry in submerged arc welding process [23]. On the basis of above literature review, gas metal arc welding and IS 2062 mild steel sheet of 6 mm thickness has been consid-

297

ered in this study. From this literature review, it is realized that a lot of research has been done on optimal weld bead geometry using different statistical tools i.e. factorial techniques, multiple regression analysis, RSM, ANN etc. Further reported research on gas metal arc welding using particular set of process parameters used in this study i.e., voltage, wire feed rate, travel speed and gas flow rate, on IS 2062 mild steel sheets is scant. The relation between weld bead geometry and input variables used in this study is nonlinear, complex and interdependent. Therefore, the choice of selection of an optimal welding parameter is captious and important from the point of view that it should produce the sound weld bead. Hence, broad research is required to develop a better scientific knowledge of interdependent nature of MIG process parameters. 1.1. Response surface methodology As an important subject in the statistical design of experiments, the Response Surface Methodology (RSM) is a collection of mathematical and statistical technique useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response [17,18]. For example, the growth of a plant is affected by a certain amount of water X1 and sunshine X2 . The plant can grow under any combination of treatment X1 and X2 . Therefore, water and sunshine can vary continuously. When treatments are from a continuous range of values, then a Response Surface Methodology is useful for developing, improving, and optimizing the response variable. In this case, the plant growth ‘y’ is the response variable as given in Eq. (1), and it is a function of water and sunshine. It can be expressed as: y = f(X1 , X2 ) + D

(1)

The variables X1 and X2 are independent variables where the response y depends on them. The dependent variable y is a function of X1 , X2 , and the experimental error term, denoted as D . The error terms are presents any measurement error on the response, as well as other type of variations not counted in f. It is a statistical error that is assumed to distribute normally with zero mean and variance, S2 . In most RSM problems, the true response function f is unknown. In order to develop a proper approximation for f, the experimenter usually starts with a low-order polynomial in some small region. If the response can be defined by a linear function of independent variables, then the approximating function is a first-order model. A first-order model with 2 independent variables can be expressed as: y = b0 + b1 X1 + b2 X2 + D

(2)

If there is a curvature in the response surface, then a higher degree polynomial shall be used. The approximating function with 2 variables is called a second-order model: y = b0 + b1 X1 + b2 X2 + b11 X112 + b22 X222 + b12 X1 X2 + D

(3)

In general, all RSM problems use either one or the mixture of the both of these models. In each model, the levels of each factor are independent of the levels of other factors. In order to get the most efficient result in the approximation of polynomials the proper experimental design must be used to collect data. Once the data are collected, the method of least square is used to estimate the parameters in the polynomials. The response surface analysis is performed by using the fitted surface. The response surface designs are types of designs for fitting response surface. Therefore, the objective of studying RSM can be accomplish by • Understanding the topography of the response surface (local maximum, local Minimum, ridge lines).

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• Finding the region where the optimal response occurs. The goal is to move rapidly and efficiently along a path to get to a maximum or a minimum Response so that the response is optimized. 2. Design of experiment In order to achieve the desired aim, the present study has been made in the following sequence: (i) To find the important metal inert gas welding parameters that will have a maximum influence on weld bead geometry. (ii) To find out the upper and lower limits of the considered parameters. (iii) Developing an experimental design matrix (iv) Conducting the set of experiments according to the designed matrix. (v) Recording of responses (vi) Developing the mathematical models (vii) Adequacy of the developed model 2.1. Identification of parameters From an extensive literature review [10–16,21–24], factors which shows a big influence on bead geometry of gas metal arc welding process have been identified, these factors have been considered in this study i.e., voltage, gas flow rate, wire feed rate and travel speed. 2.2. Finding the working limits of the parameters A number of trial runs were performed on IS 2062 mild steel plate (6 mm thick) to find out the effective and feasible working limits of the gas metal arc welding parameters included in this study as per the conditions given in Table 1. Different sets of MIG process parameters have been used to carry out the trial runs. Appearance of bead geometry and visual inspection have been done to find the working limits of the selected parameters. Observations made by the trial runs are given below:

Table 1 Welding Conditions. Polarity Welding Current Electrode Electrode Diameter Shielding Gas Torch Position Operation

DCRP (Electrode Positive) 150–250 A Copper Coated Mild Steel Wire 1.2 mm CO2 Inclined Automatic

Table 2 Process Parameters and its Level. Sr No

Factor

Notation

Codes

Unit

1 2 3 4

Wire Feed Rate Voltage Gas Flow Rate Travel Speed

WF V GF TS

A B C D

m/min V lpm Mm/min

Levels Coded (−1)

(0)

(+1)

4.5 24 10 160

7 28 14 190

9.5 32 18 220

• With voltage less than 24 V, porosity and overlap at the weld edges were observed. On the other hand, if voltage is greater than 32 V porosity, spatter and undercut is observed. • If the shielding gas flow rate is less than 10 lpm, blow holes and porosity were major defect observed. But, if the gas flow rate is higher than 18 lpm then gas entrapment was observed. • For travel speed less than 160 mm/min, shallower penetration, wider weld bead and overlap were observed. On the other hand, for travel speed, greater than 220 mm/min leads to incomplete fusion and low material deposition rate. • With wire feed rate higher than 9.5 m/min, badly shaped weld bead and spatters were observed whereas wire feed rate less than 4.5 m/min causes less penetration and incomplete fusion defects were observed. Since interactions of all process parameter used in this study is considered therefore in trial runs while varying one parameter to find the suitable limits other parameters were kept constant as per

Table 3 DOE Table with Responses. RUN

WF (m/min)

V (V)

GF (lpm)

TS (mm/min)

BH (mm)

BW (mm)

DP (mm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

4.5 7 7 4.5 9.5 7 7 4.5 7 9.5 7 7 7 9.5 7 7 7 9.5 7 7 4.5 7 9.5 4.5 7 4.5 9.5

28 32 32 24 28 28 28 28 32 28 24 28 28 32 32 24 28 28 24 24 32 28 24 28 28 28 28

14 10 14 14 10 10 10 14 18 14 18 18 14 14 14 14 18 18 10 14 14 14 14 18 14 10 14

160 190 220 190 190 220 160 220 190 160 190 160 190 190 160 220 220 190 190 160 190 190 190 190 190 190 220

1.25 1.45 1.15 1 1.3 1.25 1.2 1.05 1.2 2.35 1.1 1.6 1.35 1.55 1.15 0.9 1.25 1.3 1.4 1.2 0.75 1.3 1.55 1.05 1 0.55 1.5

9.41 8.4 9.03 7.62 10.8 9.12 12.55 7.825 11.45 11.55 7.475 12.6 9.675 14.1 13.15 6.93 9.66 11.35 7.795 8.83 7.74 10.55 9.005 7.24 10.15 7.85 10.4

3.25 2.8 3.15 3.4 3.7 3.15 3.8 3.05 3.4 3.55 3.95 3.3 3.5 3.45 3.35 4.2 3 3.7 3.55 4 3.05 2.85 4.1 2.9 3.85 3.45 3.25

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299

Fig. 1. Reciprocating Platform Table.

Table 4 Chemical Composition of IS 2062 Mild Steel. Material

Mild Steel

Chemical Composition (%) C

Si

Mn

S

P

‘C’ Equivalent

0.23

0.4

1.5

0.045

0.045

0.42

Fig. 2. Weld Bead Geometry.

travel speed to specimen. The reciprocating platform working table is shown in Fig. 1. the combination of wire diameter and mild steel sheet of thickness 6 mm as given in [1]. 2.4. Conducting the experiments and recording of responses 2.3. Development of experimental design matrix Based on the above observations, range for the working parameters has been considered: For voltage (24–32 V), travel speed (160–220 mm/min), shielding gas flow rate (10–18 lpm) and wire feed rate (4.5 m/min to 9.5 m/min) i.e., the welding has been performed without any desfects. Due to wide range of process parameters, four parameters with three levels and Box Behnkendesign matrix has been considered to design the experimental condition. Table 2 shows the considered process parameters along with its levels and codes. Table 3 shows the DOE table trail runs of welding. For the welding of specimens, TIG welding machine is used from the welding lab of central workshop. For the desired travel speed, a table has been fabricated for providing automatic

The base metal used in this study is a structural steel, IS 2062 mild steel sheets, Table 4 shows the chemical composition of base metal. Butt joint specimen of 100 × 50 × 6 mm were prepared for the welding. After welding runs has been performed, weld specimens has been sectioned for polishing and etching. The specimens were then prepared for measurement of bead geometry according to standard procedure. Average values of bead geometry for three specimens are presented in Table 3.

3. Mathematical models: regression analysis Consider bead height of the weld bead as ‘BH’, the response function [15,17,18] can be expressed as (Fig. 2):

Table 5 ANOVA table for bead height. Source

Sum of Squares

Df

Mean Square

F Value

p-value Prob> F

Model A-WF B-V D-TS AB AD BD A2 B2 C2 D2 A2 D AC2 B2 D BC2 Residual Lack of Fit Pure Error Cor Total

5.12599692 1.54792013 0.4427405 1.151329 0.04995225 0.084681 0.07425625 0.07526112 0.074839343 0.052624593 0.02887737 0.141512 0.023877042 0.028920125 0.194760167 0.034744708 0.031544708 0.0032 5.16074163

14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 10 2 26

0.36614264 1.54792013 0.4427405 1.151329 0.04995225 0.084681 0.07425625 0.07526112 0.074839343 0.052624593 0.02887737 0.141512 0.023877042 0.028920125 0.194760167 0.002895392 0.003154471 0.0016

126.457002 534.614979 152.912091 397.64179 17.252325 29.2468134 25.64635142 25.99340987 25.8477378 18.17528888 9.973560322 48.87489582 8.246565124 9.98832676 67.26555243

<0.0001 <0.0001 <0.0001 <0.0001 0.0013 0.0002 0.0003 0.0003 0.0003 0.0011 0.0082 <0.0001 0.0140 0.0082 <0.0001

Significant

1.971544271

0.3831

not significant

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Table 6 Model summary statistics of BH.

Table 7 Model summary statistics of BW.

Std. Dev.

0.05380885

R-Squared

0.993267497

Std. Dev.

0.216946987

R-Squared

0.994650498

Mean C.V.% PRESS

1.37329629 3.91822583 0.28117783

Adj R-Squared Pred R-Squared Adeq Precision

0.985412910 0.945516003 40.15533649

Mean C.V.% PRESS

9.517037037 2.279564392 14.13766839

Adj R-Squared Pred R-Squared Adeq Precision

0.986091295 0.839311833 36.6114266

BH = f (Wire Feed Rate, Voltage, Gas Flow Rate, Travel Speed) Or, BH = f(WF, V, GF, TS)

(4)

The third order polynomial (regression) equation [18] used to represents the response surface ‘BH’ is given by: BH = b0 +



bi Xi +



bii Xi 2 +



bij Xi Xj +



biij Xi 2 Xj (5)

3.1. Bead height

an adequate signal. Therefore, this model can be used to navigate the design space. The graph of predicted bead height and actual bead height is shown in Fig. 3. 3.2. Bead width

Bead Width, BW = 10.49 + 1.18A + 1.60 B − 1.55D − 0.46AB + 0.30AC + 0.85BC − 0.55BD − 1.14A2 − 1.55B2 + 0.49D2

Bead Height, BH = 1.45 + 0.44A-0.24B-0.54D-0.11AB-0.15AD 2

2

2

2

+ 0.14BD + 0.12A -0.12B -0.099C -0.074D + 0.27A D-0.095AC2 + 0.12B2 D + 0.27BC2

2

-2.00A2 B + 0.86A2 D − 0.94AB2 + 0.58AC2 + 0.69B2 C − 0.46BC

(7)

(6)

According to the equation (vi)developed by regression analysis for bead height, analysis of variance results for the response surface cubic model for bead height is given in Table 5. The Model F-value of 126.46 indicated thatmodel is significant. There is only a 0.01% chance that large F-Value could occur due to noise. Values of “Prob > F” less than 0.05 indicates that model terms are significant [13]. In this case A, B, D, AB, AD, BD, A2 , B2 , C2 , D2 , A2 D, AC2 , B2 D, BC2 are significant model terms. Values greater than 0.1 indicate the model terms are not significant. If there are many insignificant model terms, model reduction may improve the model. The “Lack of Fit F-value” of 1.97 shows that lack of fit is not significant relative to the pure error. There is a 38.31% chance that large F − value oflack of fit could occur due to noise. Non-significant lack of fit is good [15]. The model summary statistical results for bead height are shown in Table 6. From Table 6, the “Pred. R-squared” of 0.9455 is in sensible agreement with the ‘Adj. R-squared’ of 0.9854. “Adeq precision” quantifies the signal to noise ratio. A ratio greater than 4 is desirable [15], but here the value of “Adeq precision” is 40.15 that indicates

According to the equation (vii) provided by multiple regression analysis for bead width, the analysis of variance results for bead width using response surface cubic model is given in Table 8. From Table 6, the Model F-value of 116.21 indicates the model is significant. There is only a 0.01% chance that this large Model F-Value could occur due to noise. Values of “Prob > F” less than 0.0500 indicate model terms are significant. In this case A, B, D, AB, AC, BC, BD, A2 , B2 , D2 , A2 B, A2 D, AB2 , AC2 , B2 C, BC2 are significant model terms. Values greater than 0.1000 indicates the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. The “Lack of Fit F-value” of 17.27 implies there is a 10.54% chance that large“Lack of Fit F-value” could occur due to noise. Significant lack of fit is bad. The model summary statistics of bead width is given in Table 7.The “Pred R-Squared” of 0.8393 is in reasonable agreement with the “Adj R-Squared” of 0.9861. “Adeq Precision” measures the signal to noise ratio. A ratio greater than 4 is desirable [13]. Your ratio of 36.611 indicates an adequate signal. This model can be used to navigate the design space. The graph of predicted bead width and actual bead width is shown in Fig. 4.

Table 8 ANOVA table for bead width. Source

Sum of squares

Df

Mean square

F Value

p-value Prob > F

Model A-WF B-V D-TS AB AC BC BD A2 B2 D2 A2 B A2 D AB2 AC2 B2 C BC2 Residual Lack of Fit Pure Error Cor Total

87.511353 5.5225 10.288056 19.15805 0.8326563 0.354025 2.8561 1.2265563 7.755856 14.416292 1.4549271 8 1.98375 1.7625031 0.6728 1.8769 0.4163281 0.47066 0.4639433 0.0067167 87.982013

16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 10 8 2 26

5.4694596 5.5225 10.288056 19.15805 0.8326563 0.354025 2.8561 1.2265563 7.755856 14.416292 1.4549271 8 1.98375 1.7625031 0.6728 1.8769 0.4163281 0.047066 0.0579929 0.0033583

116.20831 117.33524 218.58788 407.04653 17.691249 7.5218849 60.682877 26.060349 164.78683 306.29952 30.91249 169.9741 42.148264 37.447484 14.294821 39.878048 8.8456246

<0.0001 <0.0001 <0.0001 <0.0001 0.0018 0.0207 <0.0001 0.0005 <0.0001 <0.0001 0.0002 <0.0001 <0.0001 0.0001 0.0036 <0.0001 0.0139

Significant

17.268361

0.0559

not significant

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301

Fig. 3. Predicted vs Actual – Bead Height.

Fig. 4. Predicted vs Actual – Bead Width.

3.3. Depth of penetration

Depth of Penetration, DP = 3.34 + 0.22A + 0.095 B − 0.14D + 0.18A2 + 0.26B2 + 0.17A2 B − 0.17A2 D − 0.088BC2

(8)

According to the equation (viii), given by multiple regression analysis for depth of penetration, the ANOVA results for depth of penetration using response surface cubic model are given in Table 10. From Table 8, the Model F-value of 243.40 indicates the model is significant. There is only a 0.01% chance that this large F-Valuecould occur due to noise. Values of “Prob> F” less than 0.05 indicate model terms are significant. In this case A, B, D, A2 , B2 , A2 B, A2 D, BC2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are

302

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Fig. 5. Predicted vs Actual – Depth of Penetration.

many insignificantmodel terms model reduction may improve your model. The “Lack of Fit F-value” of 4.82 implies the Lack of Fit is not significant relative to the pure error. There is a 18.53% chance that a large value of “Lack of Fit F-value” could occur due to noise. Nonsignificant lack of fit is good. The statistical model summary for depth of penetration is presented in Table 9. As given in Table 9, The “Pred R-squared” of 0.9772 is in logical agreement with the “Adj R-squared” of 0.9868. “Adeq precision” quantifies the signal to noise ratio. A ratio greater than 4 is desirable [13]. The ratio of 69.158 indicates an adequate signal. The model can be used to navigate the design space. The graph of predicted depth of penetration and actual depth of penetration is shown in Fig. 5.

3.4. Checking adequacy of developed model The adequacy of developed models has been tested with the help of analysis of variance technique (ANOVA) [20–23]. According to the ANOVA method, if the calculated F-ratio of the developed model is less than the value of F-ratio from F-table for a given confidence level (say 95%), then the model is said to be adequate. Tables 5, 8 and 10 showing the ANOVA results for bead height (BH), bead width (BW) and depth of penetration (DP) respectively. From the table, it can be seen that the developed model for every response is found to adequate with respect to the 95% confidence level. P-

Table 9 Model summary statistics of DP. Std. Dev.

0.0320209

R-Squared

0.9908405

Mean C.V.% PRESS

3.5351481 0.9057859 0.0458432

Adj R-Squared Pred R-Squared Adeq Precision

0.9867697 0.9772487 69.157983

value less than 0.05 signifies that the model terms are considerable for model building. 4. Optimization of results In this study design expert 9.0 software has been used to perform the optimization through response surface methodology and constraints for the optimization is given in Table 11. Optimized results are given in Table 12 with their desirability. Among all sets of process parameters and responsesgiven in Table 12, the set has been chosen have maximum desirability i.e.,row 1.It can be noted that, using response surfaces not only one can find the optimal values of bead geometry but also optimum range of process parameters [15]. The optimum values of bead height, bead width and depth of penetration is shown in the response curves in Figs. 6, 7 and 8 respectively. RSM is a technique for the optimization of process parameters. By using RSM plots, it is easy to optimize welding pro-

Table 10 ANOVA table for depth of penetration. Source

Sum of Squares

df

Mean Square

F Value

p-value Prob > F

Model A-WF B-V D-TS A2 B2 A2 B A2 D BC2 Residual Lack of Fit Pure Error Cor Total

1.996513352 0.594075 0.035721 0.1653125 0.204585344 0.420386678 0.055611125 0.079120167 0.015576125 0.018456056 0.017989389 0.000466667 2.014969407

8 1 1 1 1 1 1 1 1 18 16 2 26

0.249564169 0.594075 0.035721 0.1653125 0.204585344 0.420386678 0.055611125 0.079120167 0.015576125 0.001025336 0.001124337 0.000233333

243.3973515 579.3952 34.83832166 161.2275706 199.5299694 409.9987767 54.23695475 77.16507981 15.19123353

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0011

Significant

4.81858631

0.1853

not significant

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303

Table 11 Process Parameter & Response Constraint Name

Goal

Lower Limit

Upper Limit

Lower Weight

Upper Weight

Importance

WF V GF TS BW BH DP

is in range is in range is in range is in range minimize minimize maximize

4.5 24 10 160 6.93 0.71 2.984

9.5 32 18 220 13.15 2.367 4.254

1 1 1 1 1 1 1

1 1 1 1 1 1 1

3 3 3 3 3 3 3

Table 12 Desirable Solutions. Number

WF

V

GF

TS

BW

BH

DP

Desirability

1 2 3 4 5 6 7 8 9 10

4.5 4.5 4.5 4.5 4.5 4.5 9.5 9.1 9.2 9.5

32 32 32 32 32 32 32 32 32 32

10.3 10.2 10.2 12.9 13.1 11.8 13.6 13.5 13.9 13

160.1 160 167.4 165.3 161.9 185.1 191.5 219.6 218.3 188.5

7.5563968 7.4646744 6.9300098 8.6547308 8.9570338 6.9301598 6.930006 6.9299781 6.9299301 6.9299279

0.9088606 0.9303273 0.9856148 0.7519303 0.7136565 0.9431736 1.5358751 1.1764704 1.2248241 1.5464063

4.0539243 4.0445153 3.974467 4.0652182 4.1067018 3.8404809 4.2431353 3.861727 3.8968633 4.2539795

0.8735666 0.8714187 0.8663153 0.8432116 0.8407905 0.8337051 0.7922654 0.7918771 0.7912933 0.7911634

Selected

Fig. 6. Combined effects of (A) V and WF on BH (B) TS and WF on BH (C) TS and V on BH.

cess variables to achieve most favorable bead geometries i.e., bead height, bead width and depth of penetration. In the surface plots shown in Figs. 6, 7 and 8, two parameters are shown on X and Y axes, and the responses are shown on Z axis.

5. Confirmation test The optimized parameters have been verified by conducting three confirmatory tests. For confirmatory test, new set of process parameters were considered for welding. The welding run were performed and the bead profile geometry is measured and comparison with one experimented result is presented in Fig. 9. Weldment

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Fig. 7. Combined effects of (A) TS and V on BW (B) GF and WF on BW (C) GF and V on BW.

Fig. 8. Combined effects of WF and V on DP.

S. Srivastava, R.K. Garg / Journal of Manufacturing Processes 25 (2017) 296–305

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Table 13 Comparative results of conformity test. Sr No.

Parameters

Optimized parameters with predicted values

Experimentally observed values

% of error

1 2 3 4 5 6 7 8

Wire feed rate Voltage Gas flow rate Travel speed Bead Width Bead height Depth of penetration Weldment area

4.5 m/min 32 V 10.3 lpm 160.1 mm/min 7.5563968 mm 0.9088606 mm 4.0539243 mm 25.000520 mm2

4.5 m/min 32 V 10 lpm 160 mm/min 7.53 mm 0.891 mm 4.02 mm 24.65322 mm2

– – – – 0.349 1.960 0.836 1.389

Fig. 9. (A) Experimented vs (B) Predicted Results.

area and the test results are compared to the predicted values as given in Table 12. The percentage errors have beencalculated and given in Table 13. It is observed that the percentage error is less than 2% i.e. within the acceptable range of percentage errors [13]. Therefore, the optimized parameters canbe considered to give good results i.e. the maximum depth of penetration and minimum bead width and height. 6. Conclusion – In this paper, butt joint weld runs has been conducted using gas metal arc welding process. Experiments were done on the basis of Box Behnken design technique and recorded data were used to find out an optimal bead geometry i.e. bead height, bead width and depth of penetration. – The search for the optimum is based on the criteria to maximize the depth of penetration and to minimize the bead width and bead height. It can be concluded that Response Surface Methodology is a powerful tool for optimization of welding process. – As ANOVA predicts significance of process parameters, wire feed rate followed by voltage and travel speed has been found as the sequence of effective parameter among all used in this study. Gas flow rate is least effective parameter. – To validate the predicted results, confirmation test has been carried out and errors in results have been found less than 2%. References [1] Nadkarni SV. Modern Welding Technology. New Delhi: Oxford and IBH Publishing Co. Pvt. Ltd.; 1988. [2] Fukuda S, Morita H, Yamauchi Y, Nagasawa I, Tsuji S. Expert system for determining welding condition of the pressure vessels. Iron Steel Inst Jpn Int 1990;30:150–4. [3] Yang LS, Chandel RS. An analysis of curvilinear regression equations for modeling the submerged arc welding process. J Mater Process Technol 1993;37:601–11.

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