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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online) Volume 1, Number 1, July - Aug (2010), © IAEME

95

SUBMERGED ARC WELDING PARAMETERS

ESTIMATION THROUGH GRAPHICAL TECHNIQUE

Aniruddha Ghosh

Dept. of Mechanical Engineering

Govt. College of Engg & Textile Technology, Berhampore

Somnath Chattopadhyaya

Dept of ME&MME

ISM, Dhanbad, India

ABSTRACT:

In submerged arc welding (SAW), selecting appropriate values for process

variables is essential in order to control bead size and quality. Also, condition must be

selected that will ensure a predictable weld bead, which is critical for obtaining high

quality. In this investigation, mathematical models (based on multi regression method)

have been developed and side by side Prediction through artificial neural networks has

been made. A comparative study also has been done. Based on multi regressions and a

neural network, the mathematical models have been derived from extensive experiments

with different welding parameters and complex geometrical features. Graphic displays

also represent the resulting solution on bead geometry that can be employed to further

probe the model. The developed system enables to input the desired weld dimensions and

select the optimal welding parameters. The experimental results have been proved the

capability of the developed system to select the welding parameters in SAW process

according to complex external and internal geometry features of the substrate.

Keywords: submerged arc welding, multi regression method, artificial neural networks,

Graphic displays.

Article outline:

• Introduction

• Experimental procedure

International Journal of Mechanical Engineering

and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online) Volume 1,

Number 1, July - Aug (2010), pp. 95-108

© IAEME, http://www.iaeme.com/ijmet.html

IJMET © I A E M E



96

• Model development, Results and Discussion –

1. multi regression method

2. artificial neural networks

3. Graphical representation

• Conclusion

INTRODUCTION:

In the early days, arc welding was carried out manually so that the weld quality

can be totally controlled by the welder ability. The welder when welding can directly

monitor flow pattern in puddle and make immediate adjustments in welding parameters

to obtain a good weldability. To consistently produce high quality of weldability, arc

welding requires welding personnel with significant experience. One reason for this is

need of properly select welding parameters for a given task in order to get good weld

quality which identified by its microstructure and the amount of spatter, and relied on the

correct bead geometry size. Therefore, the use of the control system in arc welding can

eliminate much of the guess work often employed by welders to specify welding

parameters for a given task (Ref.1).in addition of specific importance is the development

mathematical models that can be employed to predict welding parameters about arc

welding parameters about arc welding process with respect to the work piece and bead

geometry to develop a robotic welding system. The submerged arc welding is one of the

major fabrication processes in industry because of its inherent advantages, including deep

penetration and a smooth bead (Refs.2, 3).Critical set of input variables are involved in

this process which are needed to control. For this reason , in the application of SAW,

engineers often face the problem of selecting appropriate and optimum combinations of

input process-control variables for achieving the required weld bead quality or predicting

the weld bead quality for proposed process-control-variable values (Ref.4) .For automatic

SAW, the control parameters must be fed to the system according to the some

mathematical formula it may be come from multi regression method or artificial neural

networks or any other suitable and efficient method to achieve the desired results

(ref.5).These important problem can be solved with the development of mathematical

models through effective and strategic planning, design and execution of experiments.

Already many efforts have been carried out development of various algorithms in the



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modeling of arc welding process using various technologies (ref.6, 7, 8 and 9).Multiple

regression techniques were used to establish the empirical models for various arc welding

processes(Ref.6,7).However ,the regression techniques cannot describe adequately the

most of the arc welding process as a whole. One of the artificial intelligent (AI)

techniques, a neural network as a tool for incorporating knowledge in a manufacturing

system is massively interconnected networks of simple elements and their hierarchical

organizations. These processes are characterized by welding parameters due to lack of

adequate mathematical models to relate these parameters with bead geometry (Ref. 8,

9).While numerical techniques such as finite element method (FEM) also have several

limitations. The potentially viable processing routes are numerous and, therefore, various

intelligent systems are necessary to identify optimal processing parameters (Ref.10).Now,

it is possible to make this selection with the help of a computer, and complex simulations

become an effective memory for choosing the welding parameters. Also, arc welding

requires a steady hand to keep the electrode at a constant distance from the part being

welded. At the same time that the hand has to move at constant speed, it has to adjust for

distance, as the electrode shortens. This operation requires hundreds of hours of practice,

burning expensive electrodes. There are many systems that simulate a welding machine

and permit significant saving in consumables. The selection of welding parameters for a

given welding process is based on experimental methods and human qualifications

according to fabrication standards and empirical rules (ref.11).This papers represents the

development of intelligent system to obtain detailed information about the bead geometry

in relation to the welding conditions, and to provide the welding engineer with sufficient

information to design the most economic and reliable welded components for a given set

of fabrication conditions.

Experimental Procedure: Process in Action

Figure 1 MEMCO Semi Automatic Submerged Arc Welding machine at the workshop of the

Indian School of Mines, Dhanbad, India.



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Specifications: Input voltage supply- 380/440 volts Welding speed Trolley-30 to 1200 mm/min

3 Phase,50/60 Hz cycle, Air cooled Wire feed speed-100 to 8000mm/min

Output current 600 amps Wire diameter -2 to 5 mm

Duty cycle 100% Head movement vertical/horizontal -135

mm

Open circuit voltage 56volts, 35Kva Deposition rate- 4 to 6 kg/hr

Flux hopper capacity 12.5 kg Wire flux ratio-1:1

• Flux used: ADOR Auto melt Gr II AWS/SFA 5.17(Granular flux)

• Electrode: ADOR 3.15 diameter copper coated wire

• Test Piece: 300 x 300x20 mm square butt joint

• Weld position flat

• Electrode positive and perpendicular to the plate

Process flow chart:

SELECTING THE EXPERIMENTAL DESIGN

The experiments were conducted as per the design matrix at random to avoid

errors due to noise factors. The job 300x300x15 mm (3 pieces) was firmly fixed to a base

plate by means of tack welding and then the welding was carried. The welding

parameters were noted during actual welding to determine the fluctuations if any. The

slag was removed and the job as allowed to cool down. The job was cut at three sections

V-Groove

preparation on

Shaper Machine

Face Grinding on Automatic

Grinding Machine

Cutting of 20 mm Mild

Steel Plate by Gas Cutting

Welding by

Submerged Arc

Welding

Cutting of Samples by

Power Hack Saw

Grinding of the Face where

further study is to be carried out

Carbon coating on the

surface

Removal of wax and

cleaning of surface

Wax Coating of the Ground

Surface

Study of bead geometry,

estimation of dimensions of bead

geometry



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by hacksaw cutter and the average values of the penetration, reinforcement height and

width were taken using venire caliper of least count 0.02mm

Figure 2 Bead geometry, P-Penetration, H-Reinforcement height,W-Bead Width

Table-1: Observed Values for Bead Parameters

Heat input(kj/cm)

Wirefeed

rate(cm/min)

Penetration(mm

)

Reinforcement

height(mm) Width(mm)

6.15 60 3.4 1.5 13.5

12.31 60 3.5 1.7 10.2

6.15 120 4 1.9 15.6

12.31 120 4.7 2.3 11

3.20 60 3.2 1.2 10.2

6.40 60 3.24 1.4 8.3

3.20 120 3.3 1.8 9.9

6.40 120 3.52 1.5 9.2

MODEL DEVELOPMENT, RESULTS & DISCUSSION:

Multi regression model:

The response function representing any of the weld bead dimensions can be

expressed as y=f (Q, F).The relationship selected, being second degree response surface,

is expressed as follows (Ref.12): Y=b0+ b1Q+ b2F+ b3Q2+ b4F

2+ b5QF.Where Q=Heat

input, F-Wire feed rate.

Table-2: Regression coefficients of model

Sl.No. Coefficient For the case of

Penetration

For the case of

Reinforcement Height

For the case

of Width

1 b0 6.9714 1.2218 88.1254

2 b1 0.0601 0.0852 2.6686

3 b2 -0.1081 -0.0111 -2.1975

4 b3 0.0079 -0.0016 0.1816

5 b4 0.0008 0.0001 0.0161

6 b5 -0.0015 0.0000 -0.0708



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Checking the adequacy of the developed model:

(a) The adequacy of the models was then tested by the analysis of variance

techniques. The calculated value of the F ratio of model developed does not exceed the

tabulated value of F ratio for a desired level of confidence (selected as 95%).

(b) Adding a variable to the model will always increase the value of coefficient of

multiple determination R2, regardless of whether the additional variable is statistically

significant or not. Fulfillment of above condition means model is adequate. Here models

are adequate.

Table-3: Calculation of Variants for Testing the Adequate of the Models

Bead

Parameters

SSR SSE DF

R

DF

E

D

FT

MSR MSE F0 R2

Whether

model is

adequate

Penetration 84.22 6.48 6 1 7 14.03 6.48 2.1

6

0.9

2

adequate

Reinforcem

ent height

14.16 1.061

5

6 1 7 2.36 1.061

5

2.2

2

0.9

3

adequate

Width 826.83 17.9 6 1 7 137.8 17.9 7.6

9

0.9

7

adequate

Where SST is total sum square,SSR is sum square due to model (or to regression) and sum

square due to error or residual.DF-degree of freedom.

Development of final mathematical model:

The final mathematical models developed are given below. The process control variables

are in their coded form.

Penetration, mm= 6.9714+0.0601Q-0.1081F+0.0079Q2+0.0008F

2-0.0015QF -----------(1)

Reinforcement height, mm=1.2218+0.0852Q-0.0111F-0.0016Q2+0.0001F

2 ------------ (2)

Width of weld bead,

mm =88.1254+2.6686Q-2.1975F+0.1816Q2+0.0161F

2-0.0708QF ---------------- (3)

These mathematical models furnished above can be used to predict bead geometry by

substituting the values of the respective process parameters.

Conducting conformity tests:

To determine the accuracy of the mathematical models developed, conformity test

runs were conducted with same experimental setup. After collecting experimental results

a comparison was made between the actual and predicted values of bead parameters, and

the results are 97% accurate.



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Artificial neural networks: Two primary elements make up a neural network-processing

elements (called nodes or units) and interconnections. The network mimics the human

brain, which contains more than 10 billion (biological) neurons. Hence, the processing

elements in ANNs are also called artificial neurons. An ANN node model of a biological

neuron is shown in Figure 2.

W1 Transfer function

Inputs W2 Output

W3 TJ

Weights

Figure 2 Artificial neural network node

In this model, the j-th processing element computes a weighted sum of its inputs and

outputs yj according to whether this weighted input sum is above or below a certain

threshold Tj:

yj= f( )- )-------------------------------------------------------------------------------(4)

Where the function f is called transfer function. The most commonly used transfer

function is the sigmoid (S-shape) function. A typical sigmoid function is:

f(x)= ------------------------------------------------------------------------------------------(5)

Other types of functions such as hard limit, symmetrical hard limit, linear, and hyperbolic

tangent are commonly used.

Neural Network Structures: The structure of the neural network is defined by the

interconnection architecture between the processing elements. The basic types of

structures are feed forward and recurrent nets (shown inFig.3). Others are combinations

of the two types (Multilayer feed forward network and Multilayer recurrent network).

Figure 3 the structure of neural network employed for the prediction of bead geometry.

Sum of

(xiwi)



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Rules adopted for learning: The primary training method commonly used is Error-

Correction Learning. It is a form of supervised learning where the weights are adjusted in

proportion to the output-error vector, . The output error from the k-th node on the output

layer is defined as:

=dk – ck -------------------------------------------------------------------------------(6)

Where dk is the desired output and ck is the calculated output, for the k-th node on the

output layer only. The total squared error on the output layer, E, is:

E= = - )2

---------------------------------------------------------------(7)

Knowing E, we can calculate the change in the weight factor for the i-th connection to the

j-th node, Wij:

wijnew - wijnew = ijnew = jaiE ----------------------------------------------------------(8)

Where ŋj is a linear proportionality constant for node j, called the learning rate (typically,

0 < ŋj < 1), and a1 is the i-th input to node j.

Neural Network Design

With more than 40 functioning models to choose from, it is important to know which

models have had the most success and to understand their similarities and differences.

After choosing the model, you then have to decide the number of hidden layers and the

nodes for each layer. The sizes (number of nodes) of input and output layers are fixed by

the number of inputs and outputs used. The sizes of middle (hidden) layers are

determined by trial and error. It is better to choose the smallest number of neurons

possible for a given problem to allow for generalization. If there are too many neurons,

the net will tend to memorize patterns. The number of neurons may be dictated by the

number of input training examples, or facts. In other words, the number of training

examples should be greater than that of trainable weights. In an ideal world, having 10 or

more facts for each weight are required. For instance, in a 10101 architecture there are

110 (= 10 x 10 + 10 x 1) weights, so you should have about 1,100 facts (example data).

BACK PROPAGATION NETWORK:

The back propagation neural network (BPN) is the most widely used feed forward

neural network system. The term back propagation refers to the training method by which



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the weights of the network connection are adjusted. The calculations procedure is feed

forward, from input layer through hidden layers to output layer. During training, the

calculated outputs are compared with the desired values, and then the errors are back

propagated to correct all weight factors. The whole calculation procedure (for a three-

layer BPN) is summarized as follows eqn.no.15:

1. Randomly assign values between 0 and 1 to weights Wi,j (l) for each layer, l. All

input-layer thresholds are assigned to zero, i.e. Ti,1 = 0; all hidden- and output-layer

thresholds are assigned to one, i.e., Ti,3 = 1.

2. Introduce the input Ii into the neural network, and calculate the output from the first

layer according to the equations:

xi = Ii + Ti,1 -------------------------------------------------------------------------------------(9)

ai ,1 = f (xi) ------------------------------------------------------------------------------------(10)

Where f ( ) is the transfer function mentioned in the previous section.

3. knowing the output from the first layer, calculate outputs from the second layer, using

the equation:

ai2=f[ ----------------------------------------------------------------------(11)

4. Given the output from the second layer, calculate the output from the output-layer,

using the equation:

ai3= ---------------------------------------------------------------------- (12)

yi = ai,3 ---------------------------------------------------------------------------------------------(13)

Steps 1 to 4 represent the forward activation flow; that is, the given input values Ii

move forward in the network, activate the nodes, and produce the actual output values yi

based on the initially assumed values of interconnecting weights, Wi,j(l) and internal

threshold, Ti,l. Obviously, the initial calculation will not produce the desired output

values (di). The next few steps of the back propagation algorithm represent the backward

error flow in which the errors between the desired output di and the actual output yi flow

backward through the network and try to find a new set of network parameters (Wi,j(l)

and Ti).

5. Now back propagate the error through the network, starting from the output layer and

moving backward toward the input layer. Calculate the gradient-descent term ( 1,3 ) using

the equations:



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xi3= ---------------------------------------------------------------------- (14)

i3= ------------------------------------------------------------------------------ (15)

6. Knowing the output-layer, 1,3, 1,2 has been calculated the gradient-descent term for

the hidden layer (layer 2) using these equations:

xi2= ---------------------------------------------------------------- (16)

i2= --------------------------------------------------------------- (17)

7.Knowing the deltas for the hidden and output layers, calculate the weight changes,

Wi,j , using the equation: Wi,j(l),new = ŋ +α Wi,j(l),old

Where h is the learning rate, and a is the momentum coefficient. The momentum term is

added to speed up the training rate. The momentum coefficient, a, is restricted to 0< α< 1.

8.Knowing the weight changes, update the weights as: Wi,j(l),new = Wi,j(l),old+

Wi,j(l),new---------------------------------------------------------------------------------(18)

One iteration has now been completed. This feed forward calculation and error

back propagation procedure is repeated until the sum of errors is less than the specified

value. This is the whole learning process for the neural network. The new weight factors

are calculated from the old weight factors from the previous training iteration by the

following general expression:

[Wi]new = [Wi]old+[learning rate]×[input term]×[gradient descent corrected

term]×[momentum coefficient]×[previous weight change]--------------------------------(19)

Table-4: Predicted parametric values through neural network

Heat input(kj/cm)

Wire feed

rate(cm/min)

Penetration

(mm)

Reinforcement

height(mm) Width(mm)

6.15 60 3.3 1.4 13.0

12.31 60 3.5 1.6 10.3

6.15 120 3.8 1.8 15.1

12.31 120 4.5 2.2 11.2

3.20 60 3.2 1.2 10.1

6.40 60 3.3 1.4 8.8

3.20 120 3.2 1.8 10.3

6.40 120 3.6 1.5 10.0



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Table-5: % of error for prediction through neural network

For the case of Penetration For the case of

Reinforcement height

For the case of Width

3.66 5.94 3.70

-1.32 6.49 -0.98

4.09 7.84 3.21

4.26 4.35 -1.82

-0.10 0.00 0.98

-1.34 -1.31 -6.02

2.10 1.97 -4.03

-2.27 0.00 -8.70

Figure 5 Comparison between predicted and experimental values of output parameter

GRAPHICALLY PREDICTION:

Graphically prediction technique is a new and very simple technique; previously

it was not seen in any literature. It is more appropriate technique w.r.t regression and

neural networks model. In this technique by taking input variables with in there range at

first values of out put variables values have been found out then it has been graphically

plotted with the help of MATLAB-7. By clicking on these graphs, values of variables can

be gotten very quickly.



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Figure 6A:Change of Penetration w.r.t

change of input variables

Figure 6B: Change of Reinforcement height

w.r.t change of input variables

Fig.6C: Change of width w.r.t change of

input variables

Figure 6 Change of output variables w.r.t change of input variables.

Value of input & output variables have some limit. Limit of output variables can

be detected through this graphically prediction technique. For any welding machine,

input parameters have some range. Beyond this range, particular machine cannot work, it

is practically true but theoretically variables values can be calculated beyond these limits.

The output parameters values beyond the input variable values range can be calculated

through multi regression method, artificial neural networks, but their have no practical

visibility. Suppose output variable value is selected whose corresponding input variables



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values are beyond the range of input variables. So it is not applicable. In this method, the

range of possible output variables can be predicted and corresponding input variables

values can be easily predicted so there is no chance of above mentioned mistake for this

method. This graphically prediction technique gives maximum, minimum range of

possible output and input variables, side by side only just clicking on the graphs idea of

variables can be gotten quickly and accurately. It is the main advantage & difference

from other methods of this method.

CONCLUSION:

The performance of the developed system has been tasted experimental for certain

welding conditions for a particular bead dimensions. The experimental data were proved

a clear correlation between welding parameters and the weld bead dimensions, and

showed the geometrical features. The neural network model is capable of making bead

geometry prediction of the real-time quality control based on observation of bead

geometry and for on-line welding process control. In this paper a graphically prediction

technique has been described which can able to give maximum, minimum range of

possible output and input variables, side by side only just clicking on the graphs idea of

variables can be gotten quickly and accurately. It is the main advantage & difference

from other method of this method.

REFERENCES:

1) G.E. Cook, Feedback and adaptive control in automated arc welding system. Met.

Construct.139 (1990), pp. 551 – 556.

2) P.D. Houldcroft (1989), Submerged Arc Welding Abington Publishers, U.K.

3) Annon. (1978). Principal of Industrial Welding. The James F.Lincoin Arc

Welding Foundation, Cleveland, Ohio.

4) S.Balckman. (1981).Welded fabrication of subsea pipelines in the North Sea.

Welding and Metal Fabrication.

5) N.Murugan, R.S.Paramar, S.K.Sud.1993.Effect of submerged arc process

variables on dilution and bead geometry in single wire surfacing. Journal of

Materials Processing Technology 37:767-780.

6) J.Ravindra, R.S.Pramar, Mathematical models to predict weld bead geometry for

flux cored arc welding. Met. Construct.192 (1987), pp. 31R-35R.



108

7) L.JYang, R.S.Chandel and M.J.Bibby, The effects of process variables on the

weld deposit area of submerged arc welds.Weld.J.721 (1993), pp. 11s-18s.

8) P.Li,M.T.C. Fang and J.Lucas,Modelling of submerged arc welding bead using

self-adaptive offset neural network.J.Mater.Process Technol.71(1997),pp.228-

298.

9) L.T.Srikanthan,R.S.Chandal,Neural Network based modeling of GMA welding

process using small data sets,in:Proceedings of the fifth international conference

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10) I.S.Kim, C.E.Park, Y.G.Cha, J.Jeong and J.S.Son, A study on development of a

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Welding Institute,1987,pp.38-1-38-12.

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