Optimizing Plasma Arc Cutting Parameters for Structural Steel using Grey Relational Analysis


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Optimizing Plasma Arc Cutting Parameters for Structural Steel using Grey Relational Analysis

Dr. Pallavi H. Agarwal

Professor, Department of Mechanical Engineering, Babaria Institute of Technology, Varnama, Vadodara, India -391240

Mr. Ketulkumar R. Patel

Assistant Professor, Department of Mechanical Engineering, Babaria Institute of Technology, Varnama, Vadodara, India -391240

Abstract

This paper represents the experimental investigation on the plasma arc cutting of structural steel (IS 2062 E250 BR). The response parameters considered are material removal rate (MRR), top and bottom kerf widths and bevel angle: while machining variables are current, standoff distance (SOD), pressure and speed. Experiments are performed using response surface methodology (RSM). Further grey relational analysis is used to optimize the parameters. For material removal rate, higher the better output performance characteristic is considered whereas lower the better characteristic is considered for top kerf width, bottom kerf width and bevel angle. Optimization can be used for selecting the values of process variables to get the desired values of response parameters.

Keywords Plasma Arc Cutting, Structural Steel, Process Parameters, MRR, DOE, RSM, Optimization, Grey Relational Analysis (GRA).

  1. INTRODUCTION

    Structural steel: IS 2062 E250 BR is suitable for welded, bolted and reveted structures and for general engineering purpose. Plasma cutting was developed at the end of the 1950s for cutting high-alloy steels and aluminium. It was designed to be used on all metals which, due to their chemical composition, could not be subjected to oxy-fuel cutting owing to its extremely high cutting speeds especially with thin materials and narrow heat-affected zone. The technique is also used today for cutting non-alloy steels and low-alloy steels. Plasma arc cutting is used for cutting normal structural steel upto about 40 mm in thickness and results in very little distortion, particularly in case of thin work pieces. The high cutting speeds are especially important in the preliminary fabricating process. In comparision with oxyfuel cutting, cutting speeds of 5 to 6 times greater can be achieved by plasma arc cutting [1].

    Many researchers have done work on plasma arc cutting of different materials like EN 31 steel, AISI 31 stainless steel, St 37 mild steel, hardox-400, S235 mild steel, EN 10025 low alloy steel and AISI 304 stainless steel [2-11]. But the optimization of parameters using GRA is yet to be done. This paper attempts to perform GRA of plasma arc cutting process for the cutting of structural steel to get the optimum combination of process parameters for desired results.

  2. MATERIAL SELECTION

    Experiments are conducted on Structural Steel: IS 2062 E250 BR material (density 7.9 g/cm3) which is suitable for welded, bolted and riveted structures and for general

    engineering purposes. The work piece size is 100 mm x 50 mm x 5 mm.

    Table 1: Chemical composition of IS 2062 E250 BR

    Element

    C

    Mn

    S

    P

    Si

    % Contribution

    0.22

    1.50

    0.045

    0.045

    0.40

    Table 2: Mechanical Properties of IS 2062 E250 BR

    Tensile Strength (MPa)

    Yield Stress (MPa)

    %

    Elongation

    410

    < 20 mm

    20-40 mm

    >40 mm

    23

    250

    240

    230

  3. DESIGN OF EXPERIMENT: RESPONSE SURFACE METHODOLOGY

    Response surface methodology (RSM) Box-Behnken design is selected. The Box-Behnken Design is quadratic and does not contain embedded factorial or fractional factorial design. As a result, Box-Behnken Design has a limited capability of orthogonal blocking, compared to Central Composite Design. The main difference of Box- Behnken Design from Central Composite Design is that Box-Behnken is a three level quadratic design, in which the explored space of factors is represented by [-1,0,+ 1]. The true physical lower and upper limits are corresponding to [-1, 0, +1]. In this design, however, the sample combinations are treated such that they are located at midpoints of edges formed by any two factors [17].

    Figure 1: Box Behnken Design [17]

  4. EXPERIMENTATION

    The experiments are conducted using a Quality CUT 40 Air Plasma Cutting Machine. In this cutting machine manual plasma arc cutting torch as well as trolley mounted automatic plasma arc cutting torch are provided. For

    experimentation, trolley based plasma arc cutting torch is used for maintaining stand-off distance and cutting speed during actual cutting.

    The levels of factors selected for the final experiment runs by response surface methodology are as shown in Table 3 and 4 and final experiments are conducted and the results are shown in table 5.

    Table 3: Levels of Current, SOD and Pressure

    Level

    Current

    SOD

    Pressure

    A

    mm

    Bar

    -1

    30

    1.5

    4

    0

    35

    2

    4.5

    1

    40

    2.5

    5

    Speed (m/min)

    Level

    Current 30A

    Current 35A

    Current 40A

    -1

    0.24

    0.15

    0.38

    0

    0.3

    0.3

    0.43

    1

    0.38

    0.43

    0.5

    Speed (m/min)

    Level

    Current 30A

    Current 35A

    Current 40A

    -1

    0.24

    0.15

    0.38

    0

    0.3

    0.3

    0.43

    1

    0.38

    0.43

    0.5

    Table 4: Levels of Speed for different currents

    The three terms that are typical symbols and features for Grey System are:

    1. The Grey number in Grey system is a number with incomplete information.

    2. The Grey element represents an element with incomplete information.

    3. The Grey relation is the relation with incomplete information.

    B. Grey relational analysis

    The generation of Grey relation for experimental runs is shown in Figure 2. The process is elaborated here.

    Let the number of the experimental runs be m, and the number of the response parameters be n. Then a m x n value matrix (called eigen value matrix) is set up.

    x1 (1), x1 (2),…..x1 (n)

    x2 (1), x2 (2),….x2 (n)

    X = ….

    ….

    (1),

    Runs

    Input Parameters

    Response Parameters

    Current

    SOD

    /td>

    Pressure

    Speed

    MRR

    TKW

    BKW

    BA

    mm3/min

    mm

    mm

    Degree

    1

    -1

    -1

    0

    0

    1783.90

    2.24

    1.66

    9.13

    2

    1

    -1

    0

    0

    1852.22

    2.06

    1.65

    5.37

    3

    -1

    1

    0

    0

    1257.01

    2.32

    1.76

    6.56

    4

    1

    1

    0

    0

    1854.39

    2.20

    1.94

    4.21

    5

    0

    0

    -1

    -1

    897.10

    2.27

    2.21

    9.22

    6

    0

    0

    1

    -1

    778.91

    2.30

    2.24

    7.75

    7

    0

    0

    -1

    1

    1982.60

    2.10

    1.42

    7.93

    8

    0

    0

    1

    1

    2685.80

    2.03

    1.47

    8.77

    9

    -1

    0

    -1

    0

    1804.81

    2.20

    1.48

    10.92

    10

    1

    0

    -1

    0

    1944.89

    2.06

    1.82

    4.21

    11

    -1

    0

    1

    0

    1241.80

    2.17

    1.42

    11.25

    12

    1

    0

    1

    0

    2364.58

    2.03

    1.69

    5.65

    13

    0

    -1

    0

    -1

    862.87

    2.25

    2.06

    7.57

    14

    0

    1

    0

    -1

    1011.73

    2.40

    2.36

    7.24

    15

    0

    -1

    0

    1

    2553.43

    2.06

    1.33

    7.60

    16

    0

    1

    0

    1

    1973.82

    2.07

    1.66

    4.24

    17

    -1

    0

    0

    -1

    1278.17

    2.30

    1.92

    9.48

    18

    1

    0

    0

    -1

    1833.25

    2.34

    2.17

    4.94

    19

    -1

    0

    0

    1

    2553.60

    2.22

    1.15

    8.48

    20

    1

    0

    0

    1

    3188.66

    2.03

    1.37

    3.66

    21

    0

    -1

    -1

    0

    1419.64

    2.06

    1.49

    6.58

    22

    0

    1

    -1

    0

    1447.53

    2.31

    2.05

    5.83

    23

    0

    -1

    1

    0

    1367.31

    2.20

    1.78

    8.98

    24

    0

    1

    1

    0

    1471.40

    2.12

    1.65

    6.33

    25

    0

    0

    0

    0

    2101.64

    2.10

    1.62

    7.16

    26

    0

    0

    0

    0

    1789.94

    2.05

    1.59

    8.46

    27

    0

    0

    0

    0

    1700.17

    2.06

    1.57

    6.76

    Runs

    Input Parameters

    Response Parameters

    Current

    SOD

    Pressure

    Speed

    MRR

    TKW

    BKW

    BA

    mm3/min

    mm

    mm

    Degree

    1

    -1

    -1

    0

    0

    1783.90

    2.24

    1.66

    9.13

    2

    1

    -1

    0

    0

    1852.22

    2.06

    1.65

    5.37

    3

    -1

    1

    0

    0

    1257.01

    2.32

    1.76

    6.56

    4

    1

    1

    0

    0

    1854.39

    2.20

    1.94

    4.21

    5

    0

    0

    -1

    -1

    897.10

    2.27

    2.21

    9.22

    6

    0

    0

    1

    -1

    778.91

    2.30

    2.24

    7.75

    7

    0

    0

    -1

    1

    1982.60

    2.10

    1.42

    7.93

    8

    0

    0

    1

    1

    2685.80

    2.03

    1.47

    8.77

    9

    -1

    0

    -1

    0

    1804.81

    2.20

    1.48

    10.92

    10

    1

    0

    -1

    0

    1944.89

    2.06

    1.82

    4.21

    11

    -1

    0

    1

    0

    1241.80

    2.17

    1.42

    11.25

    12

    1

    0

    1

    0

    2364.58

    2.03

    1.69

    5.65

    13

    0

    -1

    0

    -1

    862.87

    2.25

    2.06

    7.57

    14

    0

    1

    0

    -1

    1011.73

    2.40

    2.36

    7.24

    15

    0

    -1

    0

    1

    2553.43

    2.06

    1.33

    7.60

    16

    0

    1

    0

    1

    1973.82

    2.07

    1.66

    4.24

    17

    -1

    0

    0

    -1

    1278.17

    2.30

    1.92

    9.48

    18

    1

    0

    0

    -1

    1833.25

    2.34

    2.17

    4.94

    19

    -1

    0

    0

    1

    2553.60

    2.22

    1.15

    8.48

    20

    1

    0

    0

    1

    3188.66

    2.03

    1.37

    3.66

    21

    0

    -1

    -1

    0

    1419.64

    2.06

    1.49

    6.58

    22

    0

    1

    -1

    0

    1447.53

    2.31

    2.05

    5.83

    23

    0

    -1

    1

    0

    1367.31

    2.20

    1.78

    8.98

    24

    0

    1

    1

    0

    1471.40

    2.12

    1.65

    6.33

    25

    0

    0

    0

    0

    2101.64

    2.10

    1.62

    7.16

    26

    0

    0

    0

    0

    1789.94

    2.05

    1.59

    8.46

    27

    0

    0

    0

    0

    1700.17

    2.06

    1.57

    6.76

    Table 5: Experimental runs

    xm

    (1), xm

    (2),….xm

    (n)

    Where,

    xi (k)

    is the value of the number i experiment

    run and the number k response factors.

    Usually, three kinds of influence factors are included, they are:

    1. Benefit type factor (the bigger the better),

    2. Defect type (the smaller the better)

      1. Setting up eigenvalue matrix, input original data

      2. Standardized data transformation, formulas:

        1. the bigger the better (2),

        2. the smaller the better (3), or

        3. nominal-the best (4)

      3. Calculation of Grey relational degree:

      • getting absolute difference of compared series and referential series using formula (5)

      • find out minimum and maximum

      • choose the constant p (set to 0.5)

      • calculation of relational coeficient and relational degree

      4. Set up the ranking of software projects based on influence factors

      1. Setting up eigenvalue matrix, input original data

      2. Standardized data transformation, formulas:

        1. the bigger the better (2),

        2. the smaller the better (3), or

        3. nominal-the best (4)

      3. Calculation of Grey relational degree:

      • getting absolute difference of compared series and referential series using formula (5)

      • find out minimum and maximum

      • choose the constant p (set to 0.5)

      • calculation of relational coeficient and relational degree

      4. Set up the ranking of software projects based on influence factors

    3. Medium type, or nominal-the-best (the nearer to a certain standard value the better).

  5. GREY RELATIONAL ANAYSIS

    A. Grey theory steps

    The information that is either incomplete or undetermined is called Grey. The Grey system provides multidisciplinary approaches for analysis and abstract modeling of systems for which the information is limited, incomplete and characterized by random uncertainty [14].

    Figure 2: The generation of Grey relation degree

    It is difficult to compare between the different kinds of factors because they exert a different influence. Therefore, the standardized transformation of these factors must be done. Three formulas can be used for this purpose.

    xi (k)

    xi (k) min xi (k) max x (k) min x (k)

    (2)

    C. Grey Relational Optimization for Plasma Arc Cutting process

    i i Based on the theory and procedure of grey analysis

    discussed above the grey relational analysis for plasma arc

    The first standardized formula is suitable for the benefit type factor.

    cutting of Structural Steel (IS 2062 E250 Br) is carried out. The result of data Grey Relational generating is shown in table 6.

    xi (k)

    max xi (k) xi (k) max x (k) min x (k)

    (3)

    The determination of grey relational co-efficient is carried out for each quality parameters considering value of

    i i distinguishing coefficient as 0.5. The Grey Relational grade

    is calculated and rank is given as shown in table 7.

    The second standardized formula is suitable for defect type factor.

    Plasma arc cutting is used as a primary cutting process to obtain rough dimension size for components. The component edges should not be very highly taper giving

    i

    i

    x (k )

    xi (k ) x0 (k )

    larger requirement of post processing. The MRR should be

    max xi (k ) x0

    (k )

    (4)

    relatively high for primary cutting process. At the same time the kerf width should be as small as possible to reduce metal loss. To apply grey analysis similar weight is given to MRR,

    The third standardized formula is suitable for the medium

    type factor.

    The grey relation degree can be calculated by steps as follows:

    Ex. No.

    MRR

    TKW

    BKW

    BA

    Ideal

    1.0000

    1.0000

    1.0000

    1.0000

    1

    0.4171

    0.4324

    0.5785

    0.2793

    2

    0.4454

    0.9189

    0.5868

    0.7747

    3

    0.1984

    0.2162

    0.4959

    0.6179

    4

    0.4463

    0.5405

    0.3471

    0.9275

    5

    0.0490

    0.3514

    0.1240

    0.2675

    6

    0.0000

    0.2703

    0.0992

    0.4611

    7

    0.4995

    0.8108

    0.7769

    0.4374

    8

    0.7913

    1.0000

    0.7355

    0.3267

    9

    0.4257

    0.5405

    0.7273

    0.0435

    10

    0.4839

    0.9189

    0.4463

    0.9275

    11

    0.1921

    0.6216

    0.7769

    0.0000

    12

    0.6580

    1.0000

    0.5537

    0.7378

    13

    0.0348

    0.4054

    0.2479

    0.4848

    14

    0.0966

    0.0000

    0.0000

    0.5283

    15

    0.7364

    09189

    0.8512

    0.4809

    16

    0.4959

    0.8919

    0.5785

    0.9236

    17

    0.2072

    0.2703

    0.3636

    0.2332

    18

    0.4375

    0.1622

    0.1570

    0.8314

    19

    0.7365

    0.4865

    1.0000

    0.3650

    20

    1.0000

    1.0000

    0.8182

    1.0000

    21

    0.2659

    0.9189

    0.7190

    0.6153

    22

    0.2775

    0.2432

    0.2562

    0.7141

    23

    0.2442

    0.5405

    0.4793

    0.2991

    24

    0.2874

    0.7568

    0.5868

    0.6482

    25

    0.5489

    0.8108

    0.6116

    0.5389

    26

    0.4196

    0.9459

    0.6364

    0.3676

    27

    0.3823

    0.9189

    0.6529

    0.5916

    Ex. No.

    MRR

    TKW

    BKW

    BA

    Ideal

    1.0000

    1.0000

    1.0000

    1.0000

    1

    0.4171

    0.4324

    0.5785

    0.2793

    2

    0.4454

    0.9189

    0.5868

    0.7747

    3

    0.1984

    0.2162

    0.4959

    0.6179

    4

    0.4463

    0.5405

    0.3471

    0.9275

    5

    0.0490

    0.3514

    0.1240

    0.2675

    6

    0.0000

    0.2703

    0.0992

    0.4611

    7

    0.4995

    0.8108

    0.7769

    0.4374

    8

    0.7913

    1.0000

    0.7355

    0.3267

    9

    0.4257

    0.5405

    0.7273

    0.0435

    10

    0.4839

    0.9189

    0.4463

    0.9275

    11

    0.1921

    0.6216

    0.7769

    0.0000

    12

    0.6580

    1.0000

    0.5537

    0.7378

    13

    0.0348

    0.4054

    0.2479

    0.4848

    14

    0.0966

    0.0000

    0.0000

    0.5283

    15

    0.7364

    0.9189

    0.8512

    0.4809

    16

    0.4959

    0.8919

    0.5785

    0.9236

    17

    0.2072

    0.2703

    0.3636

    0.2332

    18

    0.4375

    0.1622

    0.1570

    0.8314

    19

    0.7365

    0.4865

    1.0000

    0.3650

    20

    1.0000

    1.0000

    0.8182

    1.0000

    21

    0.2659

    0.9189

    0.7190

    0.6153

    22

    0.2775

    0.2432

    0.2562

    0.7141

    23

    0.2442

    0.5405

    0.4793

    0.2991

    24

    0.2874

    0.7568

    0.5868

    0.6482

    25

    0.5489

    0.8108

    0.6116

    0.5389

    26

    0.4196

    0.9459

    0.6364

    0.3676

    27

    0.3823

    0.9189

    0.6529

    0.5916

    1. The absolute difference of the compared series and the referential series should be obtained by using the following formula:

      reduction of kerf width and obtaining straight parallel cut edges. This work is mainly concerned with studying the effect of process parameters on MRR of Structural Steel using Plasma Arc Cutting. MRR is given more weight of 70%. Top kerf width, bottom kerf width and bevel angle is given the weight of 10% each. These weights are used to calculate grey relational grade and its order in optimization process as shown in Table 8.

      x (k) x (k) x (k)

      (5)

      Table 6: Grey Relational Generating

      i 0 i

      and the maximum and the minimum difference should be found.

    2. The distinguishing coefficient p is between 0 and 1. Generally, the distinguishing coefficient p is set to 0.5.

    3. Calculation of the relational coefficient and relational degree by (6) as follows.

    In Grey relational analysis, Grey relational coefficient

    can be expressed as follows:

    min p max

    i

    i

    i (k) x (k) p max

    and then the relational degree follows as:

    ri w(k) (k)

    (6)

    (7)

    In equation (7), is the Grey relational coefficient, w (k) is the proportion of the number k influence factor to the total influence indicators. The sum of w (k) is 100%. The result obtained when using (6) can be applied to measure the

    effectiveness of the experimental run.

    Ex. No.

    MRR

    TKW

    BKW

    BA

    Grade

    Rank

    Weight

    0.7000

    0.1000

    0.1000

    0.1000

    1

    0.4617

    0.4684

    0.5426

    0.4096

    0.4652

    18

    2

    0.4741

    0.8605

    0.5475

    0.6894

    0.5416

    9

    3

    0.3841

    0.3895

    0.4979

    0.5668

    0.4143

    22

    4

    0.4745

    0.5211

    0.4337

    0.8734

    0.5150

    12

    5

    0.3446

    0.4353

    0.3634

    0.4057

    0.3617

    26

    6

    0.3333

    0.4066

    0.3569

    0.4813

    0.3578

    27

    7

    0.4998

    0.7255

    0.6914

    0.4706

    0.5386

    10

    8

    0.7055

    1.0000

    0.6541

    0.4262

    0.7019

    2

    9

    0.4654

    0.5211

    0.6471

    0.3433

    0.4769

    16

    10

    0.4921

    0.8605

    0.4745

    0.8734

    0.5653

    7

    11

    0.3823

    0.5692

    0.6914

    0.3333

    0.4270

    20

    12

    0.5938

    1.0000

    0.5284

    0.6560

    0.6341

    5

    13

    0.3413

    0.4568

    0.3993

    0.4925

    0.3737

    24

    14

    0.3563

    0.3333

    0.3333

    0.5146

    0.3675

    25

    15

    0.6548

    0.8605

    0.7707

    0.4906

    0.6705

    3

    16

    0.4979

    0.8222

    0.5426

    0.8674

    0.5718

    6

    17

    0.3868

    0.4066

    0.4400

    0.3947

    0.3949

    23

    18

    0.4706

    0.3737

    0.3723

    0.7478

    0.4788

    15

    19

    0.6548

    0.4933

    1.0000

    0.4405

    0.6518

    4

    20

    1.0000

    1.0000

    0.7333

    1.0000

    0.9733

    1

    21

    0.4052

    0.8605

    0.6402

    0.5652

    0.4902

    14

    22

    0.4090

    0.3978

    0.4020

    0.6362

    0.4299

    19

    23

    0.3981

    0.5211

    0.4899

    0.4163

    0.4214

    21

    24

    0.4123

    0.6727

    0.5475

    0.5870

    0.4694

    17

    25

    0.5257

    0.7255

    0.5628

    0.5202

    0.5488

    8

    26

    0.4628

    0.9024

    0.5789

    0.4415

    0.5162

    11

    27

    0.4473

    0.8605

    0.5902

    0.5504

    0.5133

    13

    Ex. No.

    MRR

    TKW

    BKW

    BA

    Grade

    Rank

    Weight

    0.7000

    0.1000

    0.1000

    0.1000

    1

    0.4617

    0.4684

    0.5426

    0.4096

    0.4652

    18

    2

    0.4741

    0.8605

    0.5475

    0.6894

    0.5416

    9

    3

    0.3841

    0.3895

    0.4979

    0.5668

    0.4143

    22

    4

    0.4745

    0.5211

    0.4337

    0.8734

    0.5150

    12

    5

    0.3446

    0.4353

    0.3634

    0.4057

    0.3617

    26

    6

    0.3333

    0.4066

    0.3569

    0.4813

    0.3578

    27

    7

    0.4998

    0.7255

    0.6914

    0.4706

    0.5386

    10

    8

    0.7055

    1.0000

    0.6541

    0.4262

    0.7019

    2

    9

    0.4654

    0.5211

    0.6471

    0.3433

    0.4769

    16

    10

    0.4921

    0.8605

    0.4745

    0.8734

    0.5653

    7

    11

    0.3823

    0.5692

    0.6914

    0.3333

    0.4270

    20

    12

    0.5938

    1.0000

    0.5284

    0.6560

    0.6341

    5

    13

    0.3413

    0.4568

    0.3993

    0.4925

    0.3737

    24

    14

    0.3563

    0.3333

    0.3333

    0.5146

    0.3675

    25

    15

    0.6548

    0.8605

    0.7707

    0.4906

    0.6705

    3

    16

    0.4979

    0.8222

    0.5426

    0.8674

    0.5718

    6

    17

    0.3868

    0.4066

    0.4400

    0.3947

    0.3949

    23

    18

    0.4706

    0.3737

    0.3723

    0.7478

    0.4788

    15

    19

    0.6548

    0.4933

    1.0000

    0.4405

    0.6518

    4

    20

    1.0000

    1.0000

    0.7333

    1.0000

    0.9733

    1

    21

    0.4052

    0.8605

    0.6402

    0.5652

    0.4902

    14

    22

    0.4090

    0.3978

    0.4020

    0.6362

    0.4299

    19

    23

    0.3981

    0.5211

    0.4899

    0.4163

    0.4214

    21

    24

    0.4123

    0.6727

    0.5475

    0.5870

    0.4694

    17

    25

    0.5257

    0.7255

    0.5628

    0.5202

    0.5488

    8

    26

    0.4628

    0.9024

    0.5789

    0.4415

    0.5162

    11

    27

    0.4473

    0.8605

    0.5902

    0.5504

    0.5133

    13

    Table 7: Gray relational coefficients of the individual quality characteristics, Grey Relational Grade and its Order

  6. CONCLUSION

The effect of selected input parameters on the output responses like MRR, top kerf width, bottom kerf width and bevel angle are studied by experimentation performed using Response Surface Methodology.

Grey relational analysis helps to grade the experimental levels for each of the individual variables and to find the most suitable levels for weighted combination of response variables. Here, for the selected weighted combination of responses, higher levels of speed and current; and medium levels of stand-off distance and pressure are observed to be the optimum levels.

After calculating grey relational grade and its order in optimization process the effect of each level of each parameter is calculated and the results are listed in Table 8 and shown in Figure 3.

Table 8: Response table for grey relational grade

Factor

Grey Relational Grade

Max-Min

Level 1

Level 2

Level 3

Current

0.4717

0.4888

0.6180

0.1463

SOD

0.4938

0.5427

0.4613

0.0814

Pressure

0.4771

0.5331

0.5019

0.0560

Speed

0.3891

0.4952

0.6846

0.2955

Figure 3: Effect of plasma arc cutting process parameters on multi- performance characteristics

REFERENCES

  1. Plasma working group at Linde AG, Linde Gas Department, and specialists from the company Kjellberg Finsterwalde Elektrodenund Maschinen GmbH, Facts about plasma technology and plasma cutting, 2011.

  2. H.Ravi kumar and S.Thileepan, Analysis in plasma arc cutting of 21Cr ferritic stainless steel, International Journal of ChemTech Research, Vol.8, No.11 pp 474-483, 2015.

  3. Milan kumar,Kaushik kumar, Tapan barman, Prasanta sahoo, Optimization of process parameter in plasma arc cutting of EN31 steel based on MRR and multiple roughness characteristic using grey relational analysis, International conference on advances in manufacturing and material engineering, pp 1550-1559, 2014.

  4. K. P. Maity & Dilip Kumar Bagal, Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach, International Journal of Advance Manufacturing and Technology, Springer-Verlag London 2014.

  5. J. KECHAGIAS, P. STAVROPOULOS, S. MAROPOULOS and K. SALONITIS, On the multi parameter optimization of CNC plasma-arc cutting process quality indicators using Taguchi Design of Experiments, Recent Advances in Electrical Engineering, pp 128- 133, December, 2014.

  6. Subbarao Chamarthi, N.Sinivasa Reddy, Manoj Kumar Elipey, D.V. Ramana Reddy, Investigation Analysis of Plasma arc cutting Parameters on the Unevenness surface of Hardox-400 material, International Conference On DESIGN AND MANUFACTURING, IConDM 2013.

  7. Yahya Hisman Celik, Investigating the Effects of Cutting Parameters on Materials Cut in CNC Plasma, Materials and Manufacturing Processes, 28: 10531060, 2013.

  8. K. Salonitisa, S. Vatousianos, Experimental Investigation of the Plasma Arc Cutting Process, 45th CIRP Conference on Manufacturing Systems 2012.

  9. BEGIC Derzija; KULENOVIC Malik; CEKIC Ahmet & DEDIC Eldin, Some experimental studies on plasma cutting quality of low alloy steel, Annals & Proceedings of DAAAM International , Volume 23, No.1, 2012.

  10. John Kechagias, Michael Billis, Stergios Maropoulos, A parameter design of CNC plasma-arc cutting of carbon steel plates using robust design, Int. J. Experimental Design and Process Optimization, Vol. 1, No. 4, 2010.

  11. Abdulkadir Gullu, Umut Atici, Investigation of the effects of plasma arc parameters on the structure variation of AISI 304 and St 52 steels, Materials and Design 27, pp 1157-1162, 2006.

  12. Lai, H.H., Lin, Y.C., Yeh, C.H., Form design of product image using grey relational analysis and neural network models, Computers & Operational Research 32, 2005, pp2689-2711.

  13. Liu S., Lin Y., Grey Information: Theory and Practical applications with 60 Figures, Springer London, 2006.

  14. Deng, J., Control problems of grey systems, System and Control Letters 1, 1982, pp288- 294.

  15. Advance modeling and optimization of manufacturing process by R. Venkata Rao, International Research and Development, Springer, 2011, ISBN 978-0-85729-014-4.

  16. L. Sifen, J. Forrest, The Current Developing Status on Grey System Theory, The Journal of Grey System, vol. 2, pp. 111-123, 2007.

  17. Design and analysis of experiment, Douglas C. Montgomery, Eighth Edition, Wiley India Private Limited, pp 1-23 and 478-496.

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