APPLICATION OF AN MCDM TECHNIQUE FOR OPTIMIZING WELD CLADDING PROCESSAPPLICATION OF AN MCDM TECHNIQUE FOR OPTIMIZING WELD

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Bidhan Chandra Biswas, Anup Kumar Verma, Ayan Chaudhury, Protap Roy,  Samiran De, and Santanu Das

Department of Mechanical Engineering, Kalyani Government Engineering College,
Kalyani-741235, West Bengal, India

Email: bidhan0202@gmail.com, anupkgec@rediffmail.com, chaudhury.ayan17@gmail.com, protap.7002@yahoo.in, samirdandulia@gmail.com, skntsrn@gmail.com, sdas.me@gmail.com

Abstract: Mechanical components as well as different structures exposed to hostile environment get corroded over a period of operation. To prevent failure of them, appropriate servicing and maintenance need be undertaken involving related cost to incur. Among different steps usually adopted to protect components or structures from the adverse effect of their surroundings, cladding is used widely maintaining economy. In the present work, an MCDM (multi-criteria decision making) technique, namely the Analytic Hierarchy Process (AHP), is employed for optimizing cladding process using duplex stainless steel electrode to clad low alloy steel flats utilizing Gas Metal Arc Welding (GMAW) under different heat input values. To achieve both high corrosion resistance and productivity, the AHP is applied, and optimal process parameters are evaluated that may be recommended for practice.

Keywords: welding, cladding, GMAW, duplex stainless steel, corrosion resistance, MCDM, AHP, Analytic Hierarchy Process.

1. Introduction
Cladding is a well established surface treatment process for modifying surface of a component or structure exposed to harmful environment by applying a layer of a wear-resistive alloy on it. These alloys are rich in some elements that promote growth of certain phases to resist the effect of hostile environment, thereby increasing their service life and reducing cost of maintenance or replacement. Among various processes which are used for cladding, arc welding is used frequently by welding industries [1,2]. And, Gas Metal Arc Welding is widely employed for cladding. There is a considerable impact of various process parameters, specially heat input, on weld bead attributes which decide the standard of the clad layer obtained. Hence, proper selection of process parameters is the key for obtaining a defect free good quality clad layer. Optimal selection of process parameters in GMAW was tried by many researchers [1-8] to obtain good quality weld. Use of austenitic stainless steel [9-12] as well as duplex stainless steel [13-16] as clad material was explored regarding their effect on anti corrosive property. Duplex stainless steel was reported to impart superior corrosion resistance compared to different grades of austenitic stainless steels. MCDM (multi-criteria decision making) techniques are well used to find out optimal solutions to various types of problems having two or more criteria that often put some difficulty to optimize. Among different MCDM techniques, the Analytic Hierarchy Process (AHP) is a simple, but powerful tool to solve such problems as introduced by Saaty [17,18]. After its introduction, the AHP was applied [5,8,19-25] in varying areas including different welding processes successfully. In this work, the objective is to optimize duplex stainless steel cladding process employing gas metal arc welding with 100% CO₂ gas shield. The Analytic Hierarchy Process is used to find out the optimum cladding considering corrosion resistance and productivity.

2. Experimental Work
Experiment is done in using an ESAB, India made GMAW machine under 100% CO₂ gas shield. The welding gun is mounted on a motor driven vehicle. Flow rate of CO₂ is kept at 18 litre/min. Low alloy steel flats of the size 100mm× 50mm× 6mm are taken as the base metal. The overlap of two successive beads is taken as 50%. The base metal used had 0.076%C, 0.138%Si and 0.343%Mn, while duplex stainless steel filler wire (E2209T0-1) had 0.020%C, 22.52%Cr, 9.09%Ni, 2.91%Mo, 1.01%Mn, 0.76%Si and 0.125%N. Duplex stainless steel has almost equal proportion of ferrite and austenite phases and it is known to provide good corrosion resistance under hostile atmosphere. Thus under severe corrosive atmosphere, electrode wire material is expected to offer much resistance against general and pitting corrosion compared to that of the base plate. Accelerated corrosion test is done on the cladding using a solution of anhydrous ferric chloride, hydrochloric acid, and distilled water. Only the clad portions of the test specimens are exposed to the solution and the rest is masked with teflon. Each sample is immersed in 33ml of the solution for 24 hours.

3. Results and Discussion
In Table 1, process parameters with the respective heat input for the experiments conducted are shown. Table 2 indicates that experiment run 4 gives lower corrosion rate than the other runs. This corresponds to a moderate heat input of 0.38 kJ/mm. At the heat input of lower and higher values than this, corrosion rate is found to be higher. At a high heat input of 0.45 kJ/mm, quite a high rate of corrosion (0.499 kJ/mm) is observed. Overheating due to high input may have resulted in this low corrosion resistance than that at somewhat low heat input. Again, at a quite low heat input, there may be insufficient heating leading not to have favourable duplex phases of austenite and ferrite in the clad portion. 

Table 1 Heat input used for weld cladding with 6 passes

Sl. No.

Voltage   (V)

Current  (A)

Welding speed (mm/min)

Heat input, Q (kJ/mm)

1

26

145

402

0.45

2

28

145

540

0.36

3

28

145

468

0.42

4

26

145

516

0.38

Table 2 Corrosion rate of clad portion

Sl.

No.

Heat input, Q

(kJ/mm)

Pitting corrosion rate

(gm/(m²hr))

1

0.45

0.499

2

0.36

0.458

3

0.42

0.353

4

0.38

0.282

  1. 4. Application of the AHP for Appropriate Selection of Process Parameters to Obtain Optimum Cladding

4.1 The Analytic Hierarchy Process

The analytical hierarchy process (AHP) is an MCDM (multi-criteria decision making) technique introduced by T.L. Saaty. It is a simple but powerful optimizing tool. In the analytical hierarchy process (AHP), the hierarchy structure is first constructed. At the top level of the hierarchy, the goal or the objective of the decision is kept. Next the criteria and decision alternatives are put in descending levels [17,18]. In an example of hierarchy structure, as shown in Fig. 1, the goal is selected first. The chosen alternatives are placed at the bottom. There are different criteria based on which the suitable alternatives are to be selected.

 Fig. 1: An example of hierarchy structure

The pair wise comparison matrices are constructed by comparing an element with the elements of the next higher level. This helps to find out the local priority weights. A typical pair wise comparison matrix is shown in Equation (1).

C

E1

E2

E3

En

E1

a11

a12

a13

a1n

E2

a21

a22

a23

a2n

E3

a31

a32

a33

a3n

En

an1

an2

an3

ann

Here, aij (for i,j = 1,2,3……..n) is the strength of preferences for the alternative Ei over Ej corresponding to the criterion (C), aji = 1/aij and aii = 1 for all values of i and j.

Table 3: Ratio Scale of Comparison Matrix

Preferential Judgment

Rating

Extremely Preferred

9

Very Strongly to extremely preferred

8

Very strongly preferred

7

Strongly to very strongly preferred

6

Strongly preferred

5

Moderately to strongly preferred

4

Moderately preferred

3

Equally to moderately preferred

2

Equally preferred

1


The numerical values of aij are obtained from the ratio scale (Table 3). When all the elements of the matrix are selected, it is checked if the matrix is consistent or not. A comparison matrix is known as consistent if

aij*ajk= aik, for all values of i, j and k. (2)

For all consistent matrix, aij= wi/wj (3)

for all the values of i and j, where w is the priority weight.

If  alternative E1 is equally or moderately preferred over alternative E2, and E2 has a strong preference over E3, then the strength of preference of E1 alternative over E3 alternative, a13 is given by,

a13 = w1/w3 = (w1/w2)*(w2/w3) = a12*a23. (4) 

 

Hence, a13 = 2*5 = 10, where a12 = 2 and a23 = 5. But the value of a13 cannot be 10 as the highest value in this scale is 9 (Table 3). So, these elements in the comparison matrix and also the matrix as well are inconsistent.

 

In reality, matrix A is hardly found to be consistent. In that case the priority weight is calculated by solving the equation as given: 

 

Aw = λm*w (5)

 

where w = (w1, w2, w3,………)T, λm ≤ n and λm is the largest eigen value of the matrix A.

On the other hand, if the matrix become inconsistent, Eq.(5) would be simplified to

 

Aw = n*w. (6)

 

For an inconsistent matrix, the inconsistency is measured by consistency index (CI). 

 

CI = (λm-n)/(n-1). (7)

 

A random index (RI) is also evaluated which is consistency index of a matrix, the elements of which are selected randomly from (1/9, 1/8, 1/7, ……..1.……..7, 8, 9) scale. The ratio (CI/RI) is known as consistency ratio (CR). A consistency ratio of 10% or less is acceptable.

 

Local weights, wi can be evaluated by solving the equation: 

 

wi= , i = 1,2,3,…..n. (8)

 

If Pj (j = 1,2,3,…….m) are the priority weights of n alternatives with respect to the jth criterion, then global weights (ri) of the alternatives are determined as

 

ri = , i = 1, 2, 3, ……n. (9)

 

The largest value of the global weight is usually considered the optimum value, and the corresponding alternative is the decision [5,8,17,18].

 

4.2. Finding out the Optimal Condition for Weld Cladding

 

The data from the experiments on cladding performance of flux cored duplex stainless steel wire electrodes using GMAW on base plate of low alloy carbon steel are utilized to find out the optimum parametric condition. The AHP is applied for this purpose.