Analysis and Optimization of Shielded Metal Arc Welding...

ISSN No: 2309-4893 International Journal of Advanced Engineering and Global Technology I Vol-05, Issue-01, January 2017

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Analysis and Optimization of Shielded Metal Arc Welding Parameters

on Mechanical Properties of Carbon Steel Joints by Taguchi Method

S. H. Zoalfakar a, A. A. Hassan *b

a, b Department of Mechanical Engineering, The Higher Technological Institute (HTI),

10th

of Ramadan City, Egypt. *

Corresponding author’s Email: [email protected] Tel.: 01005855475

Abstract

The effect of welding parameters on mechanical and microstructural properties of St.37/2,

St.44/2 and St.52/3 joints produced by shielded metal arc welding (SMAW) was analyzed in

the present study. Different heat inputs (H) were applied to butt- welding joints by controlling

current. To study the effect of groove angles on mechanical properties, the specimens were

machined with different groove angles 40˚, 60˚, 80˚ and 100˚. In order to determine the effect

of welding process on the local heat affected zone (HAZ) thermal cycle during welding, three

different conditions were chosen, where temperatures were recorded by using K-type

thermocouples and a data acquisition system card of USB 6008, National Instrument type.

The mechanical properties were evaluated by means of microhardness and tensile tests at

room temperature. Taguchi approach was applied to determine the most influential control

factors which will yield better mechanical properties of the joints, where Taguchiʼs tools such

as signal-to- noise ratio (S/N) have been used to observe the significant parameters and the

optimal combination level of SMAW parameters.

KEYWORDS: Optimization, orthogonal array, S/N ratio, ANOVA., SMAW.

I NTRODUCTION

Shielded Metal Arc Welding (SMAW), commonly called stick, or covered electrode,

welding, is a manual welding process whereby an arc is generated between a flux-covered

consumable electrode and the workpiece. The process uses the decomposition of the coat flux

to generate a shielding gas and to provide fluxing elements to protect the molten weld-metal

droplets and the weld pool [1]. The problem that has faced the manufacturer is the control of

the process input parameters to obtain a good welded joint with the required weld quality.

Traditionally, it has been necessary to study the weld input parameters for welded product to

obtain a welded joint with the required quality. To do so, requires a time-consuming trial and

error development method.

Design of experiment (DOE) was first introduced in 1920's by R. A. Fisher who developed

the basic principles of factorial design and the associated data analysis known as ANOVA;

Analysis of Variance; [2]. S. R. Patil and C.A. Waghmare [3] studied the optimization of

welding parameters on ultimate tensile strength (UTS) of AISI 1030 mild steel using Taguchi

technique. From this study, they observed that welding current and welding speed are major

parameters which affect the tensile strength of the welded joint. The optimization of process

parameters in gas tungsten arc welding (GTAW) using Taguchi was investigated by Ajit

Khater et al. [4]. They came to the conclusion that control factors have varying effects on the

tensile strength and welding voltage having the highest effect. R. Sathish et al. [5] studied the

weldability and process parameter optimization of dissimilar pipe joints using GTAW. They

found that, lower heat input resulted in lower tensile strength and too high heat input also

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resulted in reduced tensile strength. An intermediate value of heat input gave the highest

tensile strength. The optimization of weld bead geometry and welding parameters in SMAW

using Taguchi was studied by S. M. Ravikumar and P. Vijian [6]. They used ANOVA to

identify the most significant factor. They concluded that the welding current is the most

significant welding process parameter followed by welding speed then weld velocity.

Optimization of welding parameters using Taguchi's and DOE for different welding

technique was studied to understand the most significant parameters affecting on the welding

quality [7-14].

The present study is intended to determine the optimal process condition in SMAW

process to yield desired weld quality and also to evaluate the effect of various parameters on

mechanical and microstructural properties of St.37/2, St.44/2 and St.52/3 joints, rank theses

cores, and determine the optimal welding parameter settings. Also confirmation tests are

performed by using experiments. ANOVA is performed to investigate the more influencing

parameters on the multiple performance characteristics.

2. EXPERIMENTAL PROCEDURE

SMAW process is done on 160 mm x 90 mm x 5 mm ST 37/2, ST 44/2 and ST 52/3 steel

plates. The experimental setup of the workpiece for SMAW process is shown in Fig.1. The

chemical compositions of the steel plates and the consumable electrode used for SMAW,

E6013 (φ3mm) are shown in Table 1. The levels of parameters are listed in Table 2. The

experimental design and observed values from the specimens are given in Table 4.

Table 1 Chemical compositions of base materials and consumable electrode (weight in %).

Materials C Si Mn P S N

ST 37/2 0.19 - 1.5 0.055 0.055 0.011

ST44/2 0.21 - 1.6 0.045 0.045 0.011

ST52/3 0.24 0.6 1.7 0.045 0.045 NON

E6013 0.10 0.35 0.3 0.04 0.035 NON

Table 2 Experimental parameters and levels.

Input parameters Symbol Levels

1 2 3 4

Groove angle (θ ) θ 40 60 80 100

C.E % CE 0.36 0.39 0.43 0.36

Heat input, kJ mm H 24.4 27.1 29.8 32.5

Preheating (˚C) Pr 25 600 950 25

To calculate carbon equivalent (C.E.) we have to use the equation [15]:

C.E. = C + Mn/6 + (Cr + Mo + V)/5 + (Ni + Cu)/15 (1)

Four values of the current were used.

I = 90, 100, 110, 120. [Ampere]


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To calculate the heat input (H)

(2)

Where:

H: Heat input (kJ/mm).

E: Arc voltage (Volts).

I: Current (Ampere).

S: Weld speed (mm/min).

Arc voltage and weld speed were kept contact at 280 V and 84 mm/min respectively.

Preheating of tested plates is performed in a muffle furnace at 600 ˚C and 950 ˚C for a time 2

hr.

1. Experimental Design

The design of experiments (DOEs) is a powerful scientific systematic statistical technique for

determining the optimal factor settings of a process and thereby achieving improved process

performance, reduced process variability and improved manufacturability of products and

processes. Taguchi’s such approach is a powerful tool for the successful design application of

high quality experimental procedure for quality products [16]. Taguchi’s technique focuses

on off-line experiments of single quality characteristic optimization for a product or a process

that needs improvement leading to controlling factors determination and subsequent

regulation, managing to adjust their influence even under a very noisy environment. The first

step in Taguchi’s statistical design is the selection of levels and their factors. In the present

study, groove angle (Ө), C.E, heat input and preheating were considered as parameters. Based

on the available literature and laboratory trials, various levels of these mix parameters were

chosen. Table 2 shows the chosen factors and their levels. A standard L16(44) orthogonal

array (OA) was selected for the design of experimental trial runs with four factors and four

levels, giving rise to a total of sixteen combination of trial mixes as shown in Table 4.

Taguchi used the S/N ratio as the quality characteristic of choice. The S/N ratio is used as a

measurable value instead of the standard deviation because, as the mean decreases, the

standard deviation also decreases, and vice versa [17 and 18]. By comparing the S/N ratio of

the observed values, the optimal combination levels of post-fire parameters were determined.

Each experiment is repeated three times to reduce the influence of the uncontrolled factors

(noise factors). The quality values (yi) of three-repeated numbers are transformed into the

S/N ratio (η) via the relation:

⁄ ( ) (

∑

)

(3)

Where the quality value with larger-the-better style has been assumed for all of the measured

quality parameters. A control parameter with the largest effect means that it has the most

significant influence on the weld quality. The analysis of variance (ANOVA) is used to

discuss the relative importance of all control parameters on the weld quality and to determine

which control parameter has the most significant effect. The predicted value of the S/N ratio

at the optimum parameter levels (Yopt) is calculated by using the following formula:


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∑ ( ) (4)

Where k is the number of control parameters, is the overall mean value of the 16

experiments, and Ymi is the average S/N ratio for i the control parameter corresponding to the

optimum parameter level.

3. Characterizations

3.1 Thermal measurements

In order to determine the effect of welding process on the local SMAW thermal cycle during

welding, three different conditions were chosen and used to produce welds instrumented with

thermocouples. During welding process, the temperature was measured by rapid response of

1 mm diameter K- type thermocouples. The thermocouples were imbedded in a series of

small hole (1.5 mm diameter) at different distances from welding edge, and as closed as

possible to the consumable electrode at different distance of 3.5 mm, 7 mm, 10.6 mm from

the edge of piece as shown in Fig.1. The holes were precision drilled into the tested plates of

the workpiece at 2.5 mm depth. The thermally conducting past was packed in the holes to

seat the thermocouples. The temperature was recorded digitally every one second by using

data acquisition card, shown in Fig.2.

Fig.1 The series of small holes. Fig. 2 data acquisition card.

3.2 Tensile test From each plate, tensile specimens were machined by dimensions shown in table 3 and Fig.3.

At the transverse direction intersected with the welding line as shown in Fig. (4). Tensile test

was carried at room temperature at across head speed of 5 mm/min and the ultimate tensile

stress (UTS) and elongation % as a measure of ductility of each sample were recorded.

Table (3) Standard dimensions of tensile test (ASTEM A 370).

Gage length, mm

Width, mm

Radius mm

Overall length, mm

Length of reduced section, mm

Length of grip section, mm

Width of grip Section, mm

Thickness, mm

G = 50 W =16 R= 13 L = 160 A = 65 B = 42.5 C = 25 T = 5


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Fig. 3 Dimension of tensile specimen. Fig. 4 Tensile test specimens.

3.3 Hardness test:

The welded joints were sectioned in the transverse direction and in the plane of the plate. The

welded cross section was polished and prepared and care was taken to ensure that they

remained flat and cool during preparation. The hardness profile was measured through the

base metal, the HAZ and the weld zone at 10 points at each sample. Hardness test was

conducted using a Rockwell a micro-hardness testing machine. The load was 50g and the

holding time was 17 second for each point. The data were recorded on a digital screen.

3.4 Scanning electron microscopy (SEM):

SEM Model Quanta 250FEG (Field Emission Gun) attached with EDX Unit (Energy

Dispersive X-ray Analyses), with accelerating voltage 30 kV, magnification 14x up to

1000000 and resolution for Gun.1n) was used.

4. RESULTS AND DISCUSSION

Effect of SMAW parameters on the ultimate tensile stress (MPa)

From Fig. 5 and Table 5 it is clear that the C.E % is the most significant parameter affecting

the ultimate tensile stress (56.66% contribution), followed by the groove angle (23.56%

contribution), heat input (16.13% contribution). However, the other parameter preheating has

lowest significant effect (14.5% contribution). Such data reflect the hardening effect of

carbon as well as Mn-disperoids. The groove angle and heat input are interrelated. The larger

the groove angle, the higher the heat input. Under these situations, preheating seems to has

little effect on strength properties simply due to the large that content of weld pool.

Effect of SMAW parameters on the elongation (%)

Fig. 6 and Table 6 clearly show that the preheating is the most significant parameter affecting

on the elongation (%) (37.7% contribution), followed by C.E (31.6% contribution) and groove

angle (16.3% contribution). However, the heat input has the lowest significant effect on the

elongation % (14.4% contribution). So, one can say that preheating is important to reduce the

residual stresses in both weld zone and HAZ via decreasing cooling rate as will be declared in

the last section of the present paper. In addition, comparing data in table 5 and table 6 clearly

shows that ductility is highly sensitive to preheating more than the response of UTS. Also, it

is interesting to note that one can attain high ductility while keeping UTS at high level as

shown for the data of exp.no. 11 (table 4). Such results were assisted by the fractographic

analysis which shows the dimple mode with a considerable grain refinement effect.


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Table 4 L16 matrix for the experiments and experimental results.

Exp.

no. Input parameters Experimental results

Groove

angle C.E %

heat

input

kJ

Preheating

˚C UTS

(MPa)

Elongation%

Average

hardness

1 40 0.36 24.4 25 292.5 9 42.46

2 40 0.39 27.1 600 250 10.6 39.27

3 40 0.43 29.8 950 380 5 52.38

4 40 0.36 32.5 25 290 10.2 43.7

5 60 0.36 27.1 950 250 6.2 41.48

6 60 0.39 24.4 25 282.5 8.4 43.61

7 60 0.43 32.5 25 330 7 51.11

8 60 0.36 29.8 600 342.5 15 43.28

9 80 0.36 29.8 25 327.5 11.6 43.01

10 80 0.39 32.5 950 262.5 4.6 38.86

11 80 0.43 24.4 600 452.5 10.2 49.72

12 80 0.36 27.1 25 337.5 19 40.95

13 100 0.36 32.5 600 340 12.2 42.72

14 100 0.39 29.8 25 375 12.2 42.42

15 100 0.43 27.1 25 442.5 8.4 50.26

16 100 0.36 24.4 950 282.5 11 38.12

Table 5 Analysis of the ultimate tensile stress (MPa) results.

Level Groove angle C.E heat input preheating

1 49.53 49.72 50.12 50.40

2 49.51 49.21 49.85 50.60

3 50.59 52.00 51.02 49.24

4 51.01 49.72 49.66 50.40

Delta 1.50 2.79 1.36 1.36

Rank 2 1 3 4

Contribution % 23.56% 56.96 16.13 14.5


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Fig. 5 Main effects plot (data means) for the ultimate tensile stress.

Table 6 Analysis of the elongation (%) results.

Level groove angle C.E heat input Preheating

1 18.44 21.01 19.64 20.22

2 18.69 18.49 20.10 21.48

3 20.07 17.38 20.13 15.98

4 20.69 21.01 18.01 20.22

Delta 2.26 3.62 2.12 5.50

Rank 3 2 4 1

Contribution % 16.3 31.6 14.4 37.7

Fig. 6 Main effects plot (data means) for the elongation.


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Effect of SMAW parameters on average hardness

The data given in Fig.7 and Table 7 demonstrated that the C.E is the most significant

parameter affecting on the average hardness (87.5% contribution), followed by heat input (5%

contribution), and followed by preheating (4.3% contribution). However, the groove angle

(3.2% contribution) has no significant effect on the average hardness simply because the first

two factors overwhelm the effect of groove angle.

Table 7 Analysis of average hardness results.

Level groove angle C.E heat input Preheating

1 32.91 32.45 32.73 32.98

2 33.01 32.25 32.63 32.79

3 32.66 34.13 33.08 32.54

4 32.70 32.45 32.85 32.98

Delta 0.35 1.87 0.46 0.44

Rank 4 1 2 3

Contribution % 3.2 87.5 5 4.3

Fig. 7 Main effects plot (data means) for the hardness.

Mathematical modeling

The regression models are very promising for practical applications, such as estimating the

observed values and correlated parameters, although the parameters may not be as precise as

those produced by the Taguchi method. The models for the quality of SMAW were

developed to evaluate the relationship of welding process parameters to ultimate tensile

stress, elongation %, and average hardness. Through these models, any experimental results

of the measured response data with any combination of SMAW process parameters can be

estimated. The model has been employed on the basis of experimental results. Among several

models tested, the exponential model is found to be the best-fit model. If the quadratic model

is chosen, the second-order polynomial function that is the most accurate is given. The

quadratic model of [19] can be written as:

(5)

jii j

ij

k

i j

kXXX

iiX

i iy 2

110


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The coefficient ßo is the free term, the coefficients ßi are the linear terms, the coefficients ßij

are the interaction terms, and the coefficients ßii are the quadratic terms.

Where: Groove angle C.E, Heat input, preheating

Process multi-response optimization

A single response optimization algorithm provides a single optimal solution.

However, most of the multi- response problems, in principle, give rise to a set of optimal

solutions instead of a single optimal solution. In the present work, three responses have

been considered, i.e. ultimate tensile strength, elongation (%) and average hardness. For the

production purpose, the best combination of parameter level should produce the maximum

values of ultimate tensile strength, elongation (%) and average hardness. A single optimal

solution will not serve our purpose, as these objectives are conflicting in nature.

Derringer and Suich [20] described a multiple response method called desirability. It is an

attractive method for industry to optimize multiple quality characteristic problems. The

method makes the use of an objective function, D (X), called the desirability function

(utility transfer function) and transforms an estimated response into a scale free value (di)

called desirability. The desirable range was ranging from zero to one (least to most

desirable respectively).

In the present study, four parameters have been optimized simultaneously using developed

models, i.e. Eq. (5) based on composite desirability optimization technique. In response,

optimization, a measure of how the solution has satisfied the combined goals for all

responses, must be assured. Figure 8 represents the optimized graphs of the three responses

(Ultimate tensile stress, elongation %, and average hardness) and also the optimization

results. The vertical lines inside the cells represent current optimal parametric setting, and

the horizontal dotted lines represent the current response values. The value of composite

desirability 0.97042, the current optimal process parameters setting are groove angle of

100, C.E of 0.39, heat input of 30, and preheating of 25 ˚C.

Model validation

Once the optimal level of the process parameters is selected, the final step is to predict

and verify the improvement of the performance characteristic using the optimal level of the

input parameters. Experiments performed to carbon steel joint at the above optimal input

parametric setting for the response values. Table 8 shows the % error of experimental

Validation of the developed models for the responses with optimal parametric setting.

From the analysis of this table, it can be observed that the calculated error is small. The

smallest error % between experimental and predicted values is for ultimate tensile stress,


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elongation %, and average hardness lie within 2.1, 3.1, and 4 respectively. Obviously, this

confirms excellent reproducibility of the experimental conclusions.

Table (8) Experimental validations of the developed models with optimal parameters.

Responses Predicted Experimental Error%

UTS (MPa) 372.22 380 2.1

Elongation. % 12.62 13.02 3.1

Average hardness (Hv) 42.197 44 4

Fig. 8 Multi response optimization results.

Thermal Cycles

Fig.9 show the temperature distribution of sp. No. 2 (C.E = 0.39), an increase in temp. was

observed and a temp. of 900 °C was reached. Fig.10 show the temperature distribution of sp.

No. 6 (C.E = 0.39). A peak temperature of 780 ºC was recorded as compared with 900 ºC for

sp. No. 2. Such difference can be related to the rate of preheating.


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Fig. 9 Temperature distribution of specimen no. 2, (groove angle = 40º, heat input = 27.1

kJ/mm and preheating = 600ºC).

Fig. 10 Temperature distribution of specimen no. 6 medium carbon steel (C.E. =0.39, groove

angle = 60º, heat input = 24 kJ/mm and preheating = 25˚C).

Fractographic Analysis:

Ductile fracture:

Fig. 11 shown the fracture surface of sp. No. 15 where small micro voids form in the

interior of the material. Next, deformation continuous and the micro voids enlarge to form a

crack. The crack continues to grow and it spreads laterally towards the edges of the

specimen. Finally, crack propagation is rapid along a surface that makes about a 45degree

angle with the tensile stress axis. The new fracture surface has a very irregular appearance.

The final shearing of the specimen produces a cup type shape on the fracture surface and a

cone shape on the adjacent connecting fracture surface, hence the name, cup and cone

fracture. Fig. 12 shown the fracture appearance of sp. No. 10 where non-metallic inclusions

coexistion well as ridges beginning at the origin of the crack and spreading out across the

crack surface. Fig. 13 shows the fracture surface of sp. No. 11 where the fracture is a

combination between the ductile and brittle fracture. The dimples occur on the material,

then they coalescence to each other to form a crack. The crack makes fracture.


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Fig.11 Ductile fracture surface of sp.no. 15 with different magnifications, (groove angle

=100°, C.E. =0.43, heat input 27.1 KJ/MM and preheated 25°C).

Fig.12 The brittle fracture surface of sp.no. 10, (groove angle =80°, C.E. =0.39, heat input

32.5 KJ/MM and preheated 950 °C).


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Fig.13 The mixed mode fracture surface of sp.no. 11 (groove angle =80°, C.E. =0.43, heat

input 24.4 KJ/MM and preheated 600 °C).

Conclusions

In this study, the effect of experimental parameters namely groove angle, C.E, heat input, and

preheating on UTS, elongation and average hardness are investigated experimentally and

statistically Taguchi technique and ANOVA. Specific findings of this research include the

followings:

- The combined effect of both carbon equivalent and groove angle seems play an

important role in improving tensile strength while keeping ductility at relatively high

level.

- The right choice of groove angle and chemical composition of steel is a suitable tool

to obtain a weld with tough structure. In this regard annealing is effect to reduce the

hardening effect during and after welding via reducing crack susceptibility.

- The error between the experimental results at the optimum settings and the predicted

values for UTS, elongation%, and average hardness (Hv) lie within 2.1, 3.1, and 4%

respectively. Obviously, this confirms excellent reproducibility of the experimental

conclusions

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