OPTIMIZATION OF PROCESS PARAMETERS IN AUSTENITIC …€¦ · techniques in MIG welding and Tungsten Inert Gas (TIG) welding are presented as follows. In order to relate the weld parameters

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 10, October 2018, pp. 1569–1584, Article ID: IJMET_09_10_160

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=10

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

OPTIMIZATION OF PROCESS PARAMETERS IN

AUSTENITIC STAINLESS STEEL 316L DURING

PULSED MIG JOINING PROCESS

Dhivyasri G

School of Electrical Engineering, Vellore Institute of Technology, Vellore, India,

Sudha Ramasamy

School of Electrical Engineering, Vellore Institute of Technology, Vellore, India

ABSTRACT

Welding is a technique used to join materials by applying heat. The choosing of

appropriate weld process parameters; weld current, weld voltage and weld speed is

important, to achieve the desired weld bead geometry in a fusion joining process. In the

present study, the bead geometry such as Bead Width (BW), Reinforcement Height (RH)

and Depth of Penetration (DOP) of Pulsed Metal Inert Gas (MIG) welding is carried out

on AISI 316L. Genetic algorithm (GA) based optimization technique has been opted to

obtain the desired combination of process variables and weld bead geometry. Initially,

regression models are developed using the training dataset. The developed GA optimizes

the weld process parameters and weld bead geometry by minimizing the least square error

based objective function. The microstructure examination is performed on the optimal

weld geometry obtained from the optimization technique using optical and scanning

electron microscopy techniques. Energy Dispersive X-ray (EDS) analysis is carried out

to examine the compositional variations in the weld bead. The study attested that the width

of HAZ and depth of the fusion zone increases with increase in welding current (80A to

100A).

Keywords: Pulsed Metal Inert Gas welding; Bead width, Reinforcement Height and Depth of

Penetration and Genetic Algorithm

Cite this Article Dhivyasri Ga and Sudha Ramasamy, Optimization of Process Parameters in

Austenitic Stainless Steel 316l During Pulsed Mig Joining Process, International Journal of

Mechanical Engineering and Technology, 9(10), 2018, pp. 1569–1584.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=10

1. INTRODUCTION

In the last two decades, the demand for stainless steels in versatile engineering applications are

raised at the rate of 5% [1]. The austenitic stainless steel 316L grade is low carbon, Mo-added

alloy which exhibits excellent combination of toughness and strength. This alloy is highly

resistance to pitting and crevice corrosion and possess excellent welding capability. Such

excellent and rare combination of mechanical properties and high corrosion resistance finds this

Dhivyasri Ga and Sudha Ramasamy


alloy in extensive applications including chemical, construction, oil and marine environment.

AISI 316L can be joined by almost all the welding techniques. Although,this alloy exhibits good

weldability, there are few challenges experienced during welding and is mostly influenced by the

welding parameters [2].

The conventional MIG welding does not control the short-circuiting spikes occurring at

reduced arc length which leads to excessive spattering and irregular droplet formation. The

current pulsing in welding is a process in which high peak electric current and low base electric

current are applied at regular time intervals to break away the droplet formation at the electrode

wire tip using electromagnetic pinch force generated by the current pulse. The schematic diagram

of Pulsed MIG Welding is shown in Fig. 1.

Figure 1 Pulsed MIG Welding

The selection of a suitable parameters in pulsed Metal Inert Gas (MIG) process to achieve the

desired weld bead geometry needs lot of experimentation. This necessitates to develop a

methodology for optimization technique of welding parameters since experimental trial runs

involve time and cost. Out of the various optimization techniques, the efficient way of

establishing the weld process parameters is by adopting Genetic algorithm (GA). GA is an

optimization technique based on the principles of biological evolution which is inspired by

Darwin's theory about the survival of fitness in the search space. The fittest individual in any

population has a greater chance to reproduce and survive. GA is widely used to solve non- linear

problems and their application in optimizing the weld parameters are highly emerging [3]. Some

of the research reports have reported the optimization of process parameters using soft computing

techniques in MIG welding and Tungsten Inert Gas (TIG) welding are presented as follows. In

order to relate the weld parameters to the quality of weld bead profile, Ridings et al. presented a

neural network model to envisage the weld bead geometry [4]. Vasudevan et al. developed a GA

based numerical model to obtain the optimal weld parameters in TIG welding, using 304LN and

316LN austenitic stainless steels to attain the target weld bead geometry [5]. It is inferred from

the study that the regression model resulted in good correlation between the calculated and

measured process parameters. Further, these authors presented the soft computing techniques in

modelling and predicting the microstructures of stainless steel welding. A neural network model

is developed to predict the solidification modes in stainless steels and fuzzy logic systems are used

to monitor and control the weld processes. Thus author compared the different soft computing

techniques to analyse the microstructure of the welded stainless steels [6]. Nagesh and Datta

presented a genetic algorithm approach to optimize the process parameter and predict the weld

bead geometry of 1100 aluminium using TIG welding process. This proposed method is effective

in determining the weld bead geometry for TIG welding [7].

Optimization of Process Parameters in Austenitic Stainless Steel 316l During Pulsed Mig Joining

Process


Kamal Pal et al. addressed on the modelling and optimization of deposition efficiency in

pulsed MIG welding. GA and differential evolution techniques are deployed in maximising the

deposition efficiency and the latter is found to yield optimal solution [8]. Madhumitha et al.

have applied decision trees to identify weld central line in austenitic stainless-steel joints to find

flaws during ultrasonic testing. Their model was found to be very swift and quantitative [9]. In

a study, the effect of welding parameters on AISI 316 weld joints using ANOVA is examined

by Bharath et al. and the results attest that tensile strength and bend strength of the material are

greatly influenced by the current and weld speed [10]. Shyam Narayan Divakar have employed

GA based Artificial Neural Network (ANN) to measure the weld bead geometries such as front

width, front height, back width and back height and prediction results on aluminium sheet during

TIG welding process [11]. Luis Pe ´rez Pozo et al. optimized the welding parameters using GA

during MIG welding of curved specimens of SAE 1020 steel, for the described objective

functions the weld characteristics obtained less than 6% error [12]. Jerold Jose and Dev Anand

reported on the optimization of weld bead profile during TIG welding of Inconel 718 alloy

using regression analysis and RSM with 95% accuracy level [13].

Zhongmei Gao et al. have proposed a hybrid kriging and genetic model to optimise the weld

process parameter of 316L stainless steel using laser-MIG butt welding. The proposed model is

proven to be more efficient and feasible [14]. Nabendu Ghosh et al. performed parametric

optimization using Grey-Based Taguchi method during MIG welding of SS316L. The quality of

welds are evaluated based on Ultimate Tensile Strength (UTS), Yield Tensile Strength (YTS) and

percentage of elongation [15]. Nabendu Ghosh et al. have performed parametric optimization

using Principal Component analysis (PCA) - Based Taguchi method during MIG welding of

SS316L. The weld quality was evaluated based on UTS, YTS and percentage of elongation.

Optimal parameters were predicted and validated [16].

In this present work, genetic algorithm (GA) based computational model is developed to

determine the optimum process parameters to achieve the target weld bead geometry in 316L

stainless steel material. The Pulsed MIG welding process is done using FD-B6 welding robot

machine. The results indicates closer agreement between experimental and GA predicted

values. The weld bead geometry of the experimentally performed data’s are examined using

microscopic analysis.

2. EXPERIMENTAL WORK

2.1. Base Metal and Welding

AISI 316L stainless steel sheet of 2 mm thick with the dimensions 150mm x 150mm is used as

the candidate metal for the study, whose chemical composition is presented in Table 1. MIG

welding process is adopted using DAIHEN’s FD-B6 MIG welding robot and PLC based FD11

manipulator controller. The filler wire is SS308 of 1.2mm diameter. The shielding gas used is the

mixture of 98% Argon and 2% O2 whose flow rate is 18 litres/minute. The experimental setup is

shown in the Fig. 2.



Figure 2 Experimental setup with FD-B6 MIG welding robot

The FD11 manipulator controller consists of a PLC programmer in which the user can

program the robotic movements to perform welding. The panel screen is shown in Fig. 3 in which

the user can set the input parameters.

Figure 3 setting the input parameters in PLC panel

2.2. Design of Experiments

In this experimentation, the process parameters namely the welding current (I), voltage (V) and

weld speed (WS) are varied as they significantly influence on the weld bead geometry such Bead

Width (BW), Reinforcement Height (RH) and Depth of Penetration (DOP). The geometry of the

weld bead is shown in Fig. 4. Then 15 trained data samples and 5 test data samples are adopted

to perform GA optimization for FD-B6 MIG welded samples. The experimental data are listed in

Table 1. The experimental data is optimized using GA algorithm therefore the target weld bead

geometry which can be achieved for specific process parameters is obtained.


Process


Figure 4 Weld bead geometry

3. OPTIMIZATION METHODOLOGY

The optimizing methodology for pulsed MIG welding process parameters with GA is done by

developing the regression models correlating the process parameters with the weld bead

geometry. Then a GA is developed by means of MATLAB toolbox, where the regression model

is used to evaluate the objective function.

Table 1 Experimental Data of 316L welded in FD-B6 MIG welding robot

Trial No.

Experimental Data Experimental Results

Weld Speed

(cm/min)

Welding

Current (A)

Welding

Voltage (V) DOP (mm) BW (mm) RH (mm)

Training Dataset

1 55 70 15 1.0108 3.2233 1.547

2 55 73 14.9 1.0106 3.2456 1.658

3 55 75 14.1 1.0106 3.2600 1.7248

4 55 77 14.3 0.9761 3.2664 1.7786

5 55 80 14.4 1.0090 3.2674 1.7568

6 55 83 14.5 1.1321 3.267 1.7456

7 55 85 14.6 1.1677 3.672 1.6149

8 55 87 14.6 1.1752 3.7145 1.71

9 55 90 14.7 1.1872 3.7365 1.7228

10 55 93 14.8 1.1645 3.7298 1.9856

11 55 95 14.9 1.1511 3.6776 2.0664

12 55 97 14.9 1.1401 3.7546 2.0114

13 55 100 14.9 1.3367 3.8895 1.8565

14 55 105 15.1 1.2369 3.9871 1.8457

15 55 110 15.2 1.2245 3.9962 1.8341

Test Dataset

16 55 80 14.2 1.0103 3.2606 1.7058

17 55 85 14.5 1.0587 3.2358 1.7589

18 55 90 14.7 1.1872 3.7445 1.7181

19 55 95 14.9 1.1511 3.6798 2.0574

20 55 100 14.9 1.3258 3.7695 1.8665



3.1. Regression Model for Pulsed MIG Welded SS316L

Regression models are developed using multiple regression method. The standard regression

model notation is given by,

3.2. Development of Genetic Algorithm

Genetic Algorithm (GA) is developed using MATLAB tool to optimize the pulsed MIG welded

process parameters during the welding of SS316L. The flow chart in Fig. 5 depicts the procedure

involved during the execution of GA. The search space defines the input parameter ranges for

weld current (I), weld voltage (V) and weld speed (WS) as shown in Table 2 within which optimal

solution is identified by the GA.

�� = �0 + �1 ∗ � + �2 ∗ + �3 ∗ � + �4 ∗ �2 + �5 ∗ 2 + �6 ∗ �2

+ �7 ∗ ∗ � + �8 ∗ � ∗ � (1)

Where �0, �1, �2, �3, �4, �5, �6, �7, �8 are the estimated co-efficient. I is welding current, V is

Welding voltage and WS is Weld speed.

The relationship between three weld-bead geometry parameters and the process variables are

estimated as follows.

�� = 0.311 + 0.0512 ∗ � + 0.1312 ∗ + 0.2017 ∗ � + 0.4932 ∗ �2

− 0.5110 ∗ 2 + 0.0021 ∗ �2 + 0.0017 ∗ ∗ � + 0.0112 ∗ �

∗ � + 0.0098 ∗ ∗ (2)

� = 0.3617 + 0.0745 ∗ � + 0.2698 ∗ + 0.1693 ∗ � − 0.0002 ∗ �2

− 0.0063 ∗ 2 + 0.0013 ∗ �2 − 0.0012 ∗ ∗ � + 0.0123 ∗ �

∗ � + 0.0076 ∗ ∗ � (3)

�� = −0.3063 + 0.0042 ∗ � + 0.1012 ∗ + 0.0102 ∗ � + 0.0003 ∗ �2

− 0.013 ∗ 2 + 0.0012 ∗ �2 − 0.0102 ∗ ∗ � + 0.0006 ∗ �

∗ � + 0.0213 ∗ ∗ � (4)


Process


Figure 5 Procedure of GA optimization

There are more than one process variable that influences the target parameters; DOP, BW and

RH. The objective function is one that directs the solution to convergence. According to

Vasudevan et al. the least-square error minimization is commonly preferred objective function

[5]. In this present work, the sum of the least-square errors of the weld-bead geometries is

preferred as the objective function which is known by the equation,

Where,

�� - Objective function

��( ),�( ), ��( ) – are the values of DOP, BW and RH of the ith individual respectively

The GA attempts to maximize the solution though the objective function gets minimized.

Thus, a fitness index is given to the solutions were objective functions higher value corresponds to

lower fitness index of the solution. Since the convergence rate of GA is influenced by population

size, crossover type, number of generation, mutation rate, crossover rate and trial-and-error



approach is used to arrive at the best fit of GA parameters [5]. With this view, the variation

performed in GA parameters in illustrated in the Table 2 and Table 3.

Table 2 Range of GA parameters

GA Parameters Variation range

Size of population 50-500

No. of generation 100-1000

Crossover rate 0.55-0.90

Mutation rate 0.001-0.009

Table 3 GA parameters for optimizing the Pulsed MIG joining process

GA Parameters Value

Size of population 100

No. of generation 200

Crossover rate 0.75

Mutation rate 0.006

Crossover type Single point

The minimum and maximum values of the variables I, V, WS are specified independently.

Initially 100 population is selected to perform the first iteration, with every individual

representing a set of process variables. In this present work, the maximum value

among all the independent variables is considered as 110 (which is the highest value of

current), then the length of the chromosome in every gene is considered as 8 (as 28=256). The

number of variables is 3, therefore the length of the chromosome is 24 bits (3 × 8). Thus 200

number of generations is specified. Then, using the objective function all the chromosomes were

evaluated for their fitness. Roulette Wheel Selection (RWS) method is used to choose the

chromosome with highest fitness among the available chromosomes until total population of next

generation is generated [7]. Single Point Crossover (XSOP) is performed on the particular

chromosomes to produce offspring. Mutation is then performed after crossover, with the mutation

rate of 0.006 to avoid perturbations. Then using objective function the offspring’s are checked for

the fitness. Next 100 chromosomes are selected by mixing the chromosomes of offspring and

parents and selecting the best 100 chromosomes based on fitness index. The recently chosen 100

chromosomes are used for subsequent iterations. The iterations are sustained until no further

change in the optimized variables is observed.

3.3. Experimental Validation

In the experimentation, limited target weld bead geometries are selected from the experimental

data, and GA is applied to optimize Pulsed MIG welding process parameters. Whenever the GA

is executed, it resulted in various sets of process variables that all produced the same set of

targeted weld bead geometry. Nabendu Ghosh et al. identified that weld bead geometry target

can be attained by different combinations of weld process variables; i.e., different combination

of weld current, weld voltage and weld speed, with every combination capable of resulting the

same targeted weld bead geometry [15].


Process


Figure 6 GA predicted and experimental result for DOP

Figure. 7 GA predicted and experimental result for BW



Figure 8 GA predicted and experimental result for RH

From the Fig. 6, Fig. 7 and Fig. 8 it is clearly noted that a closed agreement is observed

between the predicted value and experimental values of DOP, BW and RH obtained from the

regression equations. Thus, all the regression models is observed to have a good correlation

between weld bead geometry and the process parameters (I, V and WS) with the error percentage

less than 0.04.

Table 4 Evaluation of actual and predicted pulsed MIG process parameters for 316L stainless steel

welds.

Parameters

Case 1 Case 2 Case 3

Actual

Value

Predicted

Value

Error

%

Actual

Value

Predicted

Value

Error

%

Actual

Value

Predicted

Value Error %

Welding

current (A) 80 80 0 85 85 0 90 90 0

Welding

Voltage (V) 14.2 14.2 0 14.5 14.5 0 14.7 14.7 0

Weld Speed

(cm/min) 55 55 0 55 55 0 55 55 0

DOP (mm) 1.0103 0.9837 0.0266 1.0587 1.0587 0 1.1872 1.1607 0.0265

BW (mm) 3.2606 3.2605 0.0001 3.2358 3.1716 0.0642 3.7445 3.7443 0.0002

RH (mm) 1.7058 1.7049 0.0009 1.7589 1.7587 0.0002 1.7181 1.7016 0.0165

Parameters

Case 4 Case 5

Actual Value Predicted

Value Error % Actual Value Predicted Value Error %

Welding current (A) 95 95 0 100 100 0

Welding Voltage (V) 14.9 14.9 0 14.9 14.9 0

Weld Speed (cm/min) 55 55 0 55 55 0

DOP (mm) 1.1511 1.1510 0.0001 1.3258 1.3209 0.0049

BW (mm) 3.6798 3.6789 0.0009 3.7695 3.7585 0.0385

RH (mm) 2.0574 2.0572 0.0002 1.8665 1.8288 0.0377


Process


For experimental validation, bead on trials were performed on 316L sheets with process

variables mentioned in case 1, case 2, case 3, case 4 and case 5. The samples were then cross-

sectioned and etched to carry out microscopic examination to measure the experimental values

of DOP, BW and RH. GA is applied to predict their corresponding combinations of welding

current, voltage and weld speed and their resultant DOP, BW and RH. The obtained results are

presented in Table 4. It may be noted that a good agreement is observed between predicted and

experimental values with very less error of 0.04%. This ability of GA makes it more superior to

Artificial Neural Networks (ANN) and regression models resulting in high accuracy.

5. RESULTS AND DISCUSSION

The weld bead geometry is estimated using Pulsed MIG welding process for various process

parameters. Optimal process parameter is determined using GA achieves the desired weld

geometry. For the optimal results listed on Table 4 experiment is done and microscopic analysis

are performed. The manually obtained hardware results have a good correlation with the GA

performed results. Thus GA is potential tool in experimental Pulsed MIG welding optimization.

Table 1 Welded samples of different current ratings

Welding

Current (A) Weld Image

80

85

90

95

100

Table 5 illustrates the welded samples of AISI 316L under various process parameters such

as, welding current from 80A increasing five intervals periodically till 100A. Thus welding

voltage are automatically tuned to set around 14.2V to 14.9V in DM 350 - Invertor DC power

source. The welding speed is set constant as 55 cm/min to reduce spatter and obtain regular bead

geometry.

The microscopic images of the experimentally validated samples in case 1, case 2, case 3, case

4 and case 5 are presented in Table 2.



Table 2 Experimental results

Sl.

No Welding Current (A)

Microscopic Images

DOP

(mm)

BW

(mm)

RH

(mm)

1

80

1.010

3.260

1.705

2

85

1.059

3.236

1.759

3

90

1.187

3.745

1.718

4

95

1.151

3.679

2.057


Process


5

100

1.336

3.769

1.866

BP = Bead Penetration; BH = Bead Height/ RH = Reinforcement Height; BW = Bead Width

The macrophotographs of austenitic stainless steel 316L is utilized for the experimental

analysis is presented in Table 6. The weld samples are rubbed with stainless steel brush applying

gentle pressure to elude the undesirable heat generation on materials and cleaned with acetone to

removal the oil residues, dirt, oxides and moistures. Narsimhachary et al. reports that change in

DOP depends on the melting of the material [17]. It is observed that with the increase in welding

current, the DOP and bead width also increases. For the current of 100A, the value of DOP and

bead width are 1.336 mm and 3.769mm respectively. While reinforcement height is 2.057 mm

for a welding current of 95A. Thus the targeted weld bead geometry of DOP and BW is obtained

at welding current of 100A and reinforcement height at 95A of welding current is obtained to

have the best targeted fitness values with respect to that of the predicted values.

5.1. SEM analysis

In Fig. 9 shows different focus of weldment like; base material, heat affected zone and fusion

zone for sample 1 (80A), sample 2 (85A), sample 3 (90A), sample 4 (95A) and sample 5 (100A).

The influence of welding current (80A, 85A, 90A, 95A and 100A) variations are clearly

presented, increase in the current value resulted in fast melting of the filler metals. This variations

are clearly observed in fusion zone, is presented in Fig. 9. From the Fig. 9 the coarse serrated

morphology appeared in the fusion boundary. The equiaxed dendrites and columnar dendrites

can be observed in the welding zone. In addition, columnar dendrites adjacent to the fusion line

developed in normal-to-isotherm line direction. Based on the proportion temperature, grade to

cooling rate of the microstructures solidification mode is contingent. The high heat input and low

cooling of the base material causes increased grain size.



Figure 9 SEM Results

5.2. Energy Dispersive X-Ray Analysis

For the case 1, case 2, case 3, case 4 and case 5 samples EDX analysis is carried out which are

presented in Fig. 10 (a, b, c, d and e) shows peaks of carbon, iron, chromium and nickel. The

Table 7 presents the elemental composition of carbon, chromium, nickel and iron.

Table 7 Elemental composition of samples

Sample No. Elemental wt. %

C Cr Fe Ni

1 13.92 23.84 56.01 6.03

2 13.86 23.99 55.26 6.97

3 13.73 24.02 54.84 7.14

4 13.62 25.25 53.30 7.83

5 13.5 31.02 47.05 8.04

Elemental chemical analysis by EDX is executed on five different current values, presenting

the innumerable distribution of elements in Fig. 10. It is observed finely that, when welding

current ratings are increased, the elemental composition nickel and chromium increases with

decreasing the iron and carbon weight composition. In general, Nickel increases the ductility and


Process


toughness of the steel materials. The content of carbon needs to be preferably low, such that

weldability of steel is improved [18]. Where else in Table 7, carbon content is observed to be

reducing which in turn produces no major change of chemical compositions is resulted in EDX

spectrum. Thus based on the welding current chemical compositions are varied within limits.

(a) (b)

(c) (d)

(e)

Figure 10 EDX pattern of 316L samples (a) 80A (b) 85A (c) 90A (d) 95A (e) 100A

6. CONCLUSIONS

It is essential to identify the suitable weld process parameters; weld current, weld voltage and

weld speed in demand to attain the desired weld bead geometry in a Pulsed MIG joining process.

The following conclusions are obtained as:

• From the experimental data, regression models are constructed and resultant plots

indicates a good agreement between predicted value and experimental values of DOP,

BW and RH obtained from the Genetic Algorithm optimization.

• Then the developed Genetic Algorithm based optimisation gave best solution for size

of population – 100; No. of generation – 200; crossover rate – 0.75; mutation rate

–

• 0.006 and mutation type – single point cross over.

• Experimental validation through microscopic analysis indicates close agreement with

the predicted values obtained from the results of GA. It was observed that increase in

welding current resulted in increased DOP and BW.

• From the EDX results it may be noted that with increase in welding current the

elemental composition of carbon and iron reduces while the elemental composition of

chromium and nickel increases. At the same time, there is no major amount of chemical

changes observed in the material, the original state of the material has been retained

austenitic property after welding.

• For advanced welding processes like robotic Pulsed-MIG welding, instead of wasting

resources by trial and error approach optimization techniques like GA can be opted to

estimate good combinations of process variables to achieve good target weldments.

ACKNOWLEDGEMENT

The authors express special thanks to Dr. N. Siva Shanmugam, Department of Mechanical

Engineering, NIT Trichy for providing the research lab facilities to perform the experimentation

work.



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