Optimization Of Pulsed Current Parameters To Minimize Pitting

International Journal of Lean Thinking Volume 4, Issue 1 (June 2013)

Lean Thinkingjournal homepage: www.thinkinglean.com/ijlt

Optimization Of Pulsed Current Parameters To Minimize Pitting Corrosion In Pulsed Current Micro Plasma Arc Welded AISI 304L Sheets Using Genetic

Algorithm K. Siva Prasad* Department of Mechanical Engineering, Anil

Neerukonda Institute of Technology &

Sciences , Visakhapatnam, INDIA

E-mail: [email protected]

C. Srinivasa Rao Department of Mechanical Engineering, AU

College of Engineering, Andhra University,

Visakhapatnam, INDIA

D. Nageswara Rao Centurion University of Technology &

Management, Odisha, INDIA

A B S T R A C T K E Y W O R D S

A R T I C L E I N F O

AISI 304L, Pulsed current,

Micro Plasma Arc Welding,

Pitting Corrosion,

Genetic Algorithm.

Received 04 January 2013

Accepted 04 June 2013

Available online 30 June 2013

Austenitic stainless steel sheets have gathered wide acceptance in

the fabrication of components, which require high temperature

resistance and corrosion resistance, such as metal bellows used in

expansion joints in aircraft, aerospace and petroleum industry. In

case of single pass welding of thinner sections of this alloy, Pulsed

Current Micro Plasma Arc Welding (PCMPAW) was found beneficial

due to its advantages over the conventional continuous current

process. This paper highlights the development of empirical

mathematical equations using multiple regression analysis,

correlating various process parameters to pitting corrosion rates in

PCMPAW of AISI 304L sheets in 1 Normal HCl. The experiments

were conducted based on a five factor, five level central composite

rotatable design matrix. A Genetic Algorithm (GA) was developed

to optimize the process parameters for minimizing the pitting

corrosion rates.

________________________________

* Corresponding Author

1. Introduction

AISI 304L is a austenitic stainless steel with excellent strength and good ductility at high

temperature. Typical applications include aero-engine hot section components, miscellaneous

hardware, tooling and liquid rocket components involving cryogenic temperature. AISI 304 L

can be joined using variety of welding methods, including Gas Tungsten Arc Welding (GTAW),

Plasma Arc Welding (PAW), Laser Beam Welding (LBW) and Electron Beam Welding (EBW). Of

these methods, low current PAW (Micro PAW) has attracted particular attention and has been

used extensively for the fabrication of metal bellows, diaphragms which require high strength

and toughness. PAW is conveniently carried out using one of two different current modes,

namely a Continuous Current (CC) mode or a Pulsed Current (PC) mode.

Pulsed current MPAW involves cycling the welding current at selected regular

frequency. The maximum current is selected to give adequate penetration and bead

contour, while the minimum is set at a level sufficient to maintain a stable arc [1,2].

This permits arc energy to be used effectively to fuse a spot of controlled

dimensions in a short time producing the weld as a series of overlapping nuggets.

Rahul Vylen, N. Rajesh Mathivanan , N. Shashidhar Chandrasekhar / International Journal of Lean Thinking Volume 4, Issue 1(June 2013)

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By contrast, in constant current welding, the heat required to melt the base material is supplied

only during the peak current pulses allowing the heat to dissipate into the base material leading to

narrower Heat Affected Zone (HAZ). Advantages include improved bead contours, greater tolerance to

heat sink variations, lower heat input requirements, reduced residual stresses and distortion, refinement

of fusion zone microstructure and reduced with of HAZ. Based on the worked published [3-8] , four

independent parameters that influence the process are peak current, back current, pulse rate and pulse

width.

In this investigation, experiments conducted using the design of experiments concept were used

for developing mathematical models to predict such variables. Many works were reported in the past for

predicting bead geometry, heat-affected zone, bead volume, etc., using mathematical models for various

welding processes [9-11]. Usually, the desired welding process parameters are determined based on the

experience of skilled workers or from the data available in the handbook. This does not ensure the

formation of optimal or near optimal weld pool geometry [12]. It has been proven by several researchers

that efficient use of statistical design of experiment techniques and other optimization tools can impart

scientific approach in welding procedure [13,14]. These techniques can be used to achieve optimal or

near optimal bead geometry from the selected process parameters.

Kim et.al reviewed that optimization using regression modeling, neural network, and Taguchi

methods could be effective only when the welding process was set near the optimal conditions or at a

stable operating range [15], but, near-optimal conditions cannot be easily determined through full-

factorial experiments when the number of experiments and levels of variables are increased. Also, the

method of steepest ascent based upon derivatives can lead to an incorrect direction of search due to the

non-linear characteristics of the welding process. Genetic algorithm, being a global algorithm, can

overcome the above problems associated with full-factorial experiments and the objective function to be

optimized using GA need not be differentiable [16].

In the present study, a sequential genetic algorithm has been used to optimize the process

parameters and achieve minimum pitting corrosion rate of PCMPAW AISI 304L sheets.

2 EXPERIMENTAL PROCEDURE Austenitic stainless steel (AISI 304L) sheets of 100 x 150 x 0.25mm are welded autogenously with

square butt joint without edge preparation. The chemical composition of SS304L stainless steel sheet is

given in Table 1. High purity argon gas (99.99%) is used as a shielding gas and a trailing gas right after

welding to prevent absorption of oxygen and nitrogen from the atmosphere. The welding has been

carried out under the welding conditions presented in Table 2. From the literature four important factors

of pulsed current MPAW as presented in Table 3 are chosen. A large number of trail experiments were

carried out using 0.25mm thick AISI 304L sheets to find out the feasible working limits of pulsed current

MPAW process parameters. Due to wide range of factors, it has been decided to use four factors, five

levels, rotatable central composite design matrix to perform the number of experiments for investigation.

Table 4 indicates the 31 set of coded conditions used to form the design matrix. The first sixteen

experimental conditions (rows) have been formed for main effects. The next eight experimental

conditions are called as corner points and the last seven experimental conditions are known as center

points. The method of designing such matrix is dealt elsewhere [17,18]. For the convenience of recording

and processing the experimental data, the upper and lower levels of the factors are coded as +2 and -2,

espectively and the coded values of any intermediate levels can be calculated by using the expression

[19].


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Xi = 2[2X-(Xmax + Xmin)] / (Xmax – Xmin) (1)

Where Xi is the required coded value of a parameter X. The X is any value of the parameter from

Xmin to Xmax, where Xmin is the lower limit of the parameter and Xmax is the upper limit of the parameter.

Table 1 Chemical composition of AISI 304L (weight %)

C Si Mn P S Cr Ni Mo Ti N

0.021 0.35 1.27 0.030 0.001 18.10 8.02 -- -- 0.053

Table 2 Welding conditions

Power source Secheron Micro Plasma Arc Machine (Model:

PLASMAFIX 50E)

Polarity DCEN

Mode of operation Pulse mode

Electrode 2% thoriated tungsten electrode

Electrode Diameter 1mm

Plasma gas Argon & Hydrogen

Plasma gas flow rate 6 Lpm

Shielding gas Argon

Shielding gas flow rate 0.4 Lpm

Purging gas Argon

Purging gas flow rate 0.4 Lpm

Copper Nozzle diameter 1mm

Nozzle to plate distance 1mm

Welding speed 260mm/min

Torch Position Vertical

Operation type Automatic

Table 3 Important factors and their levels

Levels

Serial

No

Input Factor Units -2 -1 0 +1 +2

1 Peak Current Amperes 6 6.5 7 7.5 8

2 Back Current Amperes 3 3.5 4 4.5 5

3 Pulse rate Pulses/Second 20 30 40 50 60

4 Pulse width % 30 40 50 60 70


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Table 4 Design matrix and experimental results

Serial

No

Peak

Current

(Amperes)

Back current

(Amperes)

Pulse rate

(Pulses/Second)

Pulse

width

(%)

Pitting

corrosion

rate

(mm/Year)

1 6.5 3.5 30 40 0.54120

2 7.5 3.5 30 40 0.99950

3 6.5 4.5 30 40 0.53390

4 7.5 4.5 30 40 0.82320

5 6.5 3.5 50 40 0.85370

6 7.5 3.5 50 40 0.70170

7 6.5 4.5 50 40 0.79770

8 7.5 4.5 50 40 0.60120

9 6.5 3.5 30 60 0.80370

10 7.5 3.5 30 60 0.99020

11 6.5 4.5 30 60 0.54110

12 7.5 4.5 30 60 0.82740

13 6.5 3.5 50 60 1.12450

14 7.5 3.5 50 60 0.82460

15 6.5 4.5 50 60 0.85000

16 7.5 4.5 50 60 0.67440

17 6.0 4.0 40 50 0.78280

18 8.0 4.0 40 50 0.86260

19 7.0 3.0 40 50 1.23460

20 7.0 5.0 40 50 0.78540

21 7.0 4.0 20 50 0.89920

22 7.0 4.0 60 50 0.81610

23 7.0 4.0 40 30 0.78220

24 7.0 4.0 40 70 0.82250

25 7.0 4.0 40 50 0.66290

26 7.0 4.0 40 50 0.64746

27 7.0 4.0 40 50 0.72800

28 7.0 4.0 40 50 0.71500

29 7.0 4.0 40 50 0.52060

30 7.0 4.0 40 50 0.62900

31 7.0 4.0 40 50 0.61690

3 RECORDING THE RESPONSES

The welded joints were sliced at the mid-section to prepare pitting corrosions test specimens. For

pitting corrosion test, specimens of 50×50 mm (width and length) were prepared to ensure the exposure

of 12 mm diameter circular area in the weld region to the electrolyte. The rest of the area was covered

with an acid resistant lacquer. The specimen size and dimensions are given in Figure 1. The specimen

surface was polished strictly following metallographic procedures. The polarisation studies of the welds

were carried out in 1 Normal HCl solution. Analar grade chemicals and double distilled water were used


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for the preparation of the electrolyte. The schematic circuit diagram of the potentiodynamic polarization

set up is shown in Figure 2. A potentiostat (Make: AUTOLAB / PGSTAT12) was used for this study in

conjunction with an ASTM standard cell and personal computer. Experiments are performed for 2 hours

duration each at a scan rate of 1 millivolts/mm. The pitting corrosion rate was calculated by polarizing the

specimen anodically and cathodically and by extrapolating the Tafel regions of anodic and cathodic curves

to the corrosion potential. The experimentally evaluated results are presented in Table 4.

12 mm

50mm

50 mm

Figure 1 Dimensions of corrosion test specimen

Figure 2 Block Diagram for Experimental Set up

A- Reference Electrode B- Auxiliary Electrode (Platinum) C-Working Electrode (AISI 304L)

D- Auto lab/PGTAT12 E-Electrolyte (1 N Hcl)

F- D.E. Amplifier

Figure 3a SEM image before corrosion Figure 3b SEM image after corrosion

Corrosion area


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4 DEVELOPING MATHEMATICAL MODELS

The output response of the weld joint (Y) is a function of peak current (A), back current (B),

pulse (C) and pulse width (D). It can be expressed as [20-22].

Y = f (A, B, C,D) (2)

The second order polynomial equation used to represent the response surface ‘Y’ is given by [17]:

Y = bo+bi xi +iixi2 + bijxixj+ (3)

Using MINITAB 14 statistical software package, the significant coefficients were determined and

final model is developed using significant coefficients to estimate pitting corrosion rate values of weld

joint.

The final mathematical model are given by

Pitting corrosion rate (CR)

CR= 0.645694+0.023167X1-0.087025X2+0.008392X3+0.036017X4+0.075631X22-0.127775X1X3

(4)

Where X1, X2, X3 and X4 are the coded values of peak current, back current, pulse rate and pulse

width. 5. CHECKING THE ADEQUACY OF THE DEVELOPED MODELS

The adequacy of the developed models was tested using the analysis of variance technique (ANOVA).

As per this technique, if the calculated value of the Fratio of the developed model is less than the standard

Fratio (from F-table) value at a desired level of confidence (say 99%), then the model is said to be adequate

within the confidence limit. ANOVA test results are presented in Table 5 for all the models. From the table

it is understood that the developed mathematical models are found to be adequate at 99% confidence

level. The value of co-efficient of determination ‘ R2 ’ for the above developed models is found to be

about 0.86 .

Table 5 ANOVA Table

Pitting corrosion rate

Source DF Seq SS Adj SS Adj MS F P

Regression 14 0.72098 0.72098 0.051499 7.11 0.000

Linear 4 0.22746 0.22746 0.056866 7.85 0.001

Square 4 0.20184 0.20184 0.050461 6.97 0.002

Interaction 6 0.29168 0.29168 0.048613 6.71 0.001

Residual Error 16 0.11590 0.11590 0.007244

Lack-of-Fit 10 0.08727 0.08727 0.008727 1.83 0.237

Pure Error 6 0.02863 0.02863 0.004772

Total 30 0.83689

Where DF= Degrees of Freedom, SS=Sum of Squares, MS=Mean Square, F=Fishers ratio


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6. OPTIMIZATION PROCEDURE

The purpose of optimization is to minimize pitting corrosion rate of AISI 304L welded sheets.

The tool used for optimization is GA. 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 they have been successfully used in a wide variety of problem domains [23].

6.1 Working Principle of GA

GA simulates the survival of the fittest among individuals over consecutive generation for solving

a problem. Each generation consists of a population of characters. Each individual represent a point in a

search space and a possible solution. The individuals in the population are then made to go through a

process of evolution. The basic concept of GA is to encode a potential solution to a problem as a series of

parameters. A single set of parameter value is termed as the genome of an individual solution. An initial

population of individuals is generated randomly. In every generation the individuals in the current

population are decoded according to a fitness function. The chromosomes with the highest population

fitness are selected for mating. The genes of the parameters are allowed to exchange to produce new

ones. These new ones then replace the earlier ones in the next generation. Thus the old population is

discarded and the new population becomes the current population. 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 [23,24].

6.2 Implementation of GA

The GA operates on three main operators namely 1) Reproduction 2) Crossover and 3) Mutation.

Reproduction: It selects the copies of chromosomes proportionate to their fitness value. Here Roulette

wheel was used as reproduction operator to select the chromosomes.

Crossover: After reproduction, the population is enriched with good strings from the previous

generation but does not have any new string. A crossover operator is applied to the population to create

better strings.

Mutation: It is the random modification of chromosomes i.e., changing from 0 to 1 or vice versa on a bit

by bit basis. The need for mutation is to keep diversity in the population.

The GA algorithm for the optimization problem is given below

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.


16

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. The number of generations is

continued till the termination criterion is achieved. The parameters used in GA are presented in Table 6.

Table 6 Parameters used in GA

Pitting corrosion rate

1 Sample size 40

2 Crossover probability 0.5

3 Mutation probability 0.9

4 Number of generations 100

5 Type of crossover Single

6.3 Objective Function

The optimization of pitting corrosion rate is carried out with the help of its mathematical

equation. The mathematical equation is considered as objective function. The source code was developed

using Turbo C. It is desirable to minimize pitting corrosion rate. The objective function for minimizing

pitting corrosion rate (Equation-4) is taken as the fitness function.

6.4 Results of GA

Figure 5 shows the results obtained by running the C program. The initial variation in the curve is

due to the search for optimum solution. It is evident that the minimum pitting corrosion rate of 0.4931

mm/Year is observed at the 2nd iteration. It then converges to the same value up to 100 generations.

Figure 5 Variation of pitting corrosion rate with number of generations

Co

rro

sio

n

Rat

e

(mm

/Ye

ar)

Number of Generations


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7 RESULTS & DISCUSSIONS

Based on the results obtained in GA, the optimum values of PCMPAW input parameters and

output response pitting corrosion rate is computed and presented in Table 7. The optimum pitting

corrosion rate of 0.4931 mm/Year is obtained for the welding input parameter combination of Peak

Current 6.5221 Amperes, Back Current 4.2067 Amperes, Pulse rate 30.0292 pulses/second and pulse

width 41.3226 %.

Table 7 Comparison of GA and Experimental values

Welding Parameters Experimental Values GA values

Peak current(Amperes) 7 6.5221

Back current(Amperes) 4 4.2067

Pulse rate (pulses/second) 40 30.0292

Pulse width (%) 50 41.3226

Pitting corrosion rate (mm/Year) 0.5206 0.4391

8 CONCLUSIONS

Empirical models are developed for pitting corrosion rate for PCMPAW AISI 304L austenitic steel

sheets using RSM CCD design matrix. The pitting corrosion rate is optimized using GA. The optimum

value of pitting corrosion rate obtained using GA is 0.4391 mm/Year, where the value obtained

experimentally is 0.5206 mm/Year. The optimum values obtained using GA is in good agreement with

experimental values

9 ACKNOWLEDGEMENTS

The authors would like to thank Shri. R.Gopla Krishnan, Director, M/s Metallic Bellows (I) Pvt

Ltd, Chennai, India and Mr. P V Vinay, Associate Professor, GVP College for PG courses (Technical

Campus), Visakhapatnam for their support to prepare this manuscript.


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Optimization Of Pulsed Current Parameters To Minimize Pitting