ANALYSIS AND PARAMETRIC OPTIMIZATION OF PLASMA ARC

CUTTING(PAC) WITH MATHEMATICAL MODELLING

STUDENTS:

PATEL CHINTAN (100780119001)

PATEL BRIJESH (100780119061)

PATEL SAMIR (100780119012)

PATEL HARDIK (100780119025)

GUIDENCE BY:

MR. H C PATEL1

PLASMA ARC CUTTING

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WHY SELECT THIS PROJECT?

• The focus on this project is to obtain an optimum condition (setting) for plasma cutting on suitable material.

• to accurately justify the machine’s operational costs.

• To improve the efficiency of PAC machine

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PROJECT BACKGROUND• Because of the growing need for manufacturing functional metallic

parts, rapid Manufacturing processes have become the focus of increasing research and development.

• Manufacturing processes based on material removal (i.e., drilling, milling, turning, and Cutting) have been used for many years.

• The recent advancements in manufacturing technology have enabled Manufacturers to make parts and products faster, with better quality, and more Complexibility.

• The process of plasma cutting was introduced in 1950. Since then, the Manufacturing industries are using this process extensively because of its wide Applications.

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PROBLEM STATEMENT

Plasma arc cutting can be characterized in terms of two different speeds.

• At higher cutting speeds , the plasma jet does not cut through metal plate.

• At lower speeds , the molten metal from the kerf sticks to the bottom of the plate. so, we have to optimize proper parameters (gas pressure , currents, cutting speed, arc gap) for plasma arc cutting.

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What is Plasma?

• Plasma means a low-ionized gas, in which the individual atoms get ionized. In other words, plasma is a gas that is heated to a higher temperature and is ionized so as to become electrically conductive.

• In short plasma is a state of matter like a solid, liquid or gas.

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Working of plasma cutting

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OBJECTIVE

• To study about the influence of Plasma Arc Cutting Parameters on Suitable material.

• To design a series of experiment using the help of Design of Experiments (DOE) layout in order to study about Plasma Arc Cutting (PAC).

• To study about the best combination of solution for optimum condition (setting) parameters.

• To improve effectiveness of product.

• To reduce the time consumption.

• To reduce material waste during cutting process.

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SCOPE OF PROJECT

• Based on this work many improvements can be made and the scope can also be winded Following are suggestion for future work:

1. Study for manual calculation for other method in DOE to improve knowledge and skills.

2. Using Plasma Arc Cutting system, add the parameter such as current, material dimension, and change advance material such as brass and bronze then compare the result obtained.

3. Also side clearance and thermal effect on material and work piece like Heat Affected Zone (HAZ) can also be considered to study the effect on properties of work piece.

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METHODOLOGY

Find out and case study

Parameter selection

Design of experiments(DOE)

Experimental setup

Experimentation analysis

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METHODOLOGY(cont’d)

ANOVA analysis

Mathematical modeling

Parametric optimization

Conclusion

End

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Work Preparation

INPUT PARAMETERS

Current flow rate

Arc gap

Cutting speed

Response parameters

Surface roughness(Ra)

Kerf width

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MATERIAL SELECTION

AISI H11 alloy steel

it known as ....

1) hot die tool steel

2)medium carbon steel

(0.30-0.50%)

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Why we choose AISI H11?

• AISI H11 is a special alloy steel, categorized as chromium tool steel

• high toughness and hardness

• well suited for hot work applications involving very high loads.

• Provides better dimensional accuracy

• It can increase the working life of dies and tools

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Chemical Composition of AISI H11 steel

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Element Weight %

C 0.33-0.43

Mn 0.20-0.50

Si 0.80-1.20

Cr 4.75-5.50

Ni < 0.3

Mo 1.10-1.60

V 0.3-0.6

Cu < 0.25

P < 0.03

S < 0.03

Application of H11

• hot-work forging

• extrusion dies

• helicopter rotor blades (hot shear blades)

• For longer life of machine

• To get higher design accuracy

• tools for manufacture of screws, nuts, revets etc

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Material order

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• Material- AISI H11 hot die tool steel

• Dimensions- 800mm*300mm*25mm

• QTY- 3 plates

parameters Level 1 Level 2 Level 3

Current flow rate(A)

180 200 260

Cutting speed (mm/min)

575 700 850

Arc gap (mm) 5 5.5 6

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parameters Range of parameter in literature reviews

Current flow rate (A) 180-260

Cutting speed (mm/min)

500-850

Arc gap (mm) 5-6

INPUT PARAMETER LEVEL SELECTION

HyPerformance HPR260XD

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Machine specification

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Nozzle selection

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Surface roughness tester

Experiment setup

EXP. NOCurrent

Flow (Amp.)

Cutting Speed(mm/sec)

Arc Gap(mm)

SR (µm) KW (mm)

A B C mean SR A B Cmean

KW

1 180 575 5 2.09 2.39 2.55 2.34 3.26 3.24 3.27 3.26

2 180 700 5.5 2.67 2.12 2.25 2.35 3.24 3.50 3.30 3.35

3 180 850 6 2.27 2.72 1.56 2.18 3.42 3.50 3.42 3.45

4 180 575 5.5 1.57 2.09 2.02 1.89 3.37 3.36 3.13 3.29

5 180 575 6 1.56 1.67 1.55 1.59 3.70 3.78 3.68 3.72

6 180 700 5 1.67 1.78 2.12 1.86 3.45 3.39 3.46 3.43

7 180 700 6 1.35 1.27 1.55 1.39 3.67 3.84 3.63 3.71

8 180 850 5 1.19 1.73 1.98 1.63 3.46 3.45 3.19 3.37

9 180 850 5.5 1.35 1.03 1.00 1.13 3.31 3.31 3.37 3.33

10 260 575 5 1.38 2.16 2.33 1.96 4.35 4.35 4.24 4.31

11 260 575 5.5 1.68 1.99 1.79 1.82 4.43 4.53 4.57 4.51

12 260 575 6 1.48 2.43 2.74 2.22 4.70 4.68 4.68 4.69

13 260 700 5 2.25 1.60 1.46 1.77 4.29 4.48 4.57 4.45

14 260 700 5.5 1.45 1.28 1.27 1.33 4.69 4.41 4.48 4.53

EXP. NOCurrent

Flow (Amp.)

Cutting Speed(mm/sec)

Arc Gap(mm

)

SR (µm) KW (mm)

A B C mean SR A B Cmean

KW

15 260 700 6 2.14 1.26 1.59 1.66 4.25 4.68 4.70 4.54

16 260 850 5 2.29 2.20 2.45 2.31 4.38 4.37 4.28 4.34

17 260 850 5.5 2.12 2.36 2.45 2.31 4.26 4.48 4.26 4.33

18 260 850 6 2.49 2.98 2.73 2.73 4.50 4.48 4.36 4.45

19 200 575 5 2.81 2.73 2.12 2.55 4.02 4.48 4.53 4.34

20 200 575 5.5 1.24 2.01 2.74 2.00 4.39 4.48 4.33 4.40

21 200 575 6 2.10 1.24 2.04 1.79 4.26 4.20 4.19 4.22

22 200 700 5 2.05 1.86 1.24 1.72 4.10 4.52 4.22 4.28

23 200 700 5.5 1.56 1.98 1.18 1.57 4.39 4.55 4.29 4.41

24 200 700 6 1.27 1.14 1.33 1.25 4.35 4.25 4.13 4.24

25 200 850 5 2.48 1.36 1.24 1.69 4.20 4.25 4.48 4.31

26 200 850 5.5 1.18 1.12 1.14 1.15 4.77 4.48 4.53 4.59

27 200 850 6 1.48 1.66 1.24 1.46 4.72 4.92 4.82 4.82

ANOVA ANALYSIS WITH MINITAB 16

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Print screen of ANOVA analyzer software for surface roughness

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Print screen of ANOVA analyzer software for kerf width

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Factorial analysis of SR

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Source Of

Variation D.O.F

Sum

Of SquaresPercentage

Contribution (P)%

Factor A 2 0.4850 10.19%

Factor B 2 0.5944 26.27%

Factor C 2 0.3039 17.31%

Error O 20 3.3749 46.23%

Total 26 4.7582 100%

R-sq = 29.07 %

Factorial analysis of SRSource Df Seq SS Percentage

contribution

current flow 2 0.48499 10.20%

cutting speed 2 0.59440 12.50%

arc gap 2 0.30389 06.40%

current flow*cutting

speed

4 1.49799 31.50%

current flow*arc gap 4 0.41038 08.60%

cutting speed*arc gap 4 0.78046 16.40%

Error 8 0.68580

Total 26 4.75820

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R-sq = 85.89%

Factorial analysis of KW

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Source Of

Variation D.O.F

Sum

Of Squares

Percentage

Contribution (P)

Factor A 2 5.9936 90.20%

Factor B 2 0.0042 00.26%

Factor C 2 0.1727 02.59%

Error 20 0.4738 07.12%

Total 26 6.6442 100%

R-sq= 92.87%

Factorial analysis of KWSource Df Seq SS Percentage

contribution

current flow 2 5.99630 90.20%

cutting speed 2 0.00415 00.06%

arc gap 2 0.17271 02.60%

current flow*cutting

speed

4 0.18491 02.78%

current flow*arc gap 4 0.09141 01.37%

cutting speed*arc gap 4 0.01501 00.22%

Error 8 0.18242 2.75%

Total 26 6.64420

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R-sq= 97.25 %

Main plot for Mans for SR

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Main plot for Mans for KW

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Regression analysis

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The regression equation isSR = 2.53 + 0.00312 current flow(A) - 0.000571 cutting speed(mm/s)

- 0.173 arc gap(mm)

Regression analysis

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The regression equation isKW = 0.80 + 0.0101 current flow(A) + 0.000101 cutting speed(mm/s)

+ 0.194 arc gap(mm)

Grey relational optimization with Taguchi method

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• The integrated Grey based Taguchi method combines the algorithm of Taguchi method and grey relational analysis to determine the optimum process parameters with multiple performance characteristics.

Taguchi method

• The loss function Lij for lower the better,

n - number of repetition; K – number of tests; Yijk – experimental value of the ith performance in the jth experiment at the kth tests.

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1

1²

n

ij ijkL Y k

n

• The S/N ratio for both LB and HB is given by

• The normalization of S/N values can be done by the following equation

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log( )ij ijL

1

ij

n

iji

NSN

• Yarn unevenness indexes (CVm%) and hair number of yarn per meter, which are the lower-the-better can be expressed as:

• The grey relational coefficient ξi(k) can be calculated as:

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( ) min ( )( )

max ( ) min ( )

i ii

i i

y k y Kx k

y k y k

min max( )

( ) maxi

i

ko k

• the grey relational grade Ύi can be obtained as:

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1

1( ) ( )

n

i ikk k

n

Grey relational coefficient for KW

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Grey relational coefficient for SR

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Grey relational Grade (GRG)

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GRG plots

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Parametric optimization

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Optimum parameter

Currentflow(Amp)

Cuttingspeed(mm/s) Arc gap (mm)

Surface roughness(µm)

Kerf width (mm)

180 850 5.5 1.12667 3.33

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conclusion

• value of Delta rank is higher than the percentage contribution of this factor will be more and effect to response parameter will be higher than other factor. So analyses that Arc Gap will more effect on Surface Roughness.

• value of Delta rank is higher than the percentage contribution of this factor will be more and effect to response parameter will be higher than other factor. So analyses Cutting Speed will more effect on Kerf Width.

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• In above analysis error find to be more so to reduce error, interaction of input parameter will be taken and same analysis will be done again its shows that the percentage contribution of input parameters and its interaction to SR and Model error will be minimized and R-sq. value will be increased upto 85.89%.

• Analysis for KW its indicates that the percentage contribution of input parameters and its interaction to KW and model error will be minimized and R-sq value will be increased 97.25 %.

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• Grey Relational Analysis method used to find Optimum Parametric setting for PAC of H11. GRC and GRG value of experiment will find and its higher value of GRG shows that the optimum Setting will be the exp. no-9 and its parametric setting will be listed below:

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Current

flow(Amp)

Cutting

speed(mm/s)Arc gap (mm)

Surface

roughness(µm)

Kerf width

(mm)

180 850 5.5 1.12667 3.33

References[1] Durga Tejaswani Vejandla (May, 2009) – “Optimizing the automated plasma cutting process by design of experiment”, San Marcos, Texas.

[2]vivek singh(2011) - " analysis of process parameters of plasma arc cutting using design of experiment" from National Institute of Technology, Rourkela.

*3+ Chuck Landry. “Plasma Arc Cutting, Tips for optimizing cut quality”, Welding Design and Fabrication, September 1997.

[4] Hatala Michal Faculty of Manufacturing Technologies of the Technical University of Kosice Sturova “The Principle of Plasma Cutting Technology and Six Fold Plasma Cutting”.5th International Multidisciplinary Conference.

[5] J.A. Hogan and J.B. Lewis, "Plasma Processes of Cutting and Welding".(Project Report by Bethlehem Steel Corporation in cooperation with U.S. Maritime Administration 1976).

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[6] Ramakrishnan S., Gershenzon M., Polivka F., Kearney T. N., Rogozinski M. W., “Plasma Generation for the Plasma Cutting Process”, IEEE Transactions on Plasma Science, Vol. 25, No. 5, 1997, pp. 937-946

[7] Jeffus Larry (2003) “Welding Principles and Applications” Sixth Edition: Inc; p. 182-203.

[8]ArsenNarimanyan(2007) -"Unilateral conditions modelling the cut front during plasma cutting: FEM solution".

[9]Abdul kadir Gullu, UmutAtici (2005) -"Investigation of the effects of plasma arc parameters on the structure variation of AISI 304 and St 52 steels"Department of Mechanical Education, Gazi University,Turkey. International Journal of Material and Design 27: 1157-1162

[10]w.j.xu, j.c.fang, y.s. lu (2002) - "study on ceramic cutting of plasma arc"School of Mechanical Engineering , Dalian Univercity of Technology Dalian116023, PR China. Journal of materials processing technology 129 (2002) 152-156.

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[11] E. Gariboldi, B. Previtali (2004) - "High tolerance plasma arc cutting of commercially pure titanium"

[12] R. Bini, B.M. Colosimo, A.E. Kutlu, M. Monno (2007) - "Experimental study of the features of the kerf generated by a 200A high tolerance plasma arc cutting system “Department of Mechanical Engineering, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy”.

[13]Jia Deli, You Bo (2010) - "An intelligent control strategy for plasma arc cutting technology" Harbin University of Science and Technology, Harbin 150080, China

[14] Daniel J. Thomas (2011) - "The influence of the laser and plasma traverse cutting speed process parameter on the cut-edge characteristics and durability of Yellow Goods vehicle applications “Materials Research Centre, College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, United Kingdom”.

[15] M. Boutinguizaa, J. Poua, F. Lusquinosa, F. Quinteroa, R. Sotoa, M. Perez-Amora, K. Watkinsb, W.M. Steenb (2001)- "CO2 laser cutting of slate" from Department of Engineering, Laser Engineering Group, The University ofLiverpool, Brownlow Street, Liverpool, U.K.

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