This document discusses optimizing the cut surface quality of stainless steel using plasma arc cutting. It examines input parameters like cutting speed, current, standoff distance, and gas pressure. The output parameters analyzed are surface roughness, kerf width, and heat affected zone. Experiments were conducted using an L9 orthogonal array to minimize runs. ANOVA and genetic algorithms were used to analyze the results and determine the optimized parameter values that produce the best cut surface quality. Current was found to be the most significant input parameter affecting the output. The study concludes plasma arc cutting is well-suited for cutting hard metals and further development is expected in its application to other materials and mass production environments.

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2.  INTRODUCTION
 REVIEW OF LITERATURE
 OBJECTIVE AND METHODOLOGY
 EXPERIMENTATION
 INPUT AND OUTPUT PARAMETERS
 INITIATING GENETIC ALGORITHM
 RESULT AND DISCUSSION
 INITIATING GENETIC ALGORITHM
 ANALYSIS OF RESULTS
 CONCLUSION
 FUTURE SCOPE OF WORK
 NOMENCLATURE
 ABBREVATION
 REFERENCES
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3.  Modern industries now a days are trying to manipulate the metals and alloys due to their high hardness, ability to withstand
the high temperatures and other properties.
 Among these metals and alloys stainless steel is most frequent type that all industries work.
 In this review SS321(Austenitic Steel) has been selected for understanding the cut quality characteristics of stainless steel in
the view of industrial experimentation and material selection.
 Apart from this it also discuss briefly about the plasma fusion cutting and parameters involved in it.
 It has been selected that the test specimen as SS321 a type of stainless steel because it has lot of applications like ship din,
jet , aircrafts, etc,.
 At the same it will discuss about input parameters like cutting speed ,current, stand off distance, etc,. And Output parameters
like surface roughness, etc,. And ANOVA and other things.
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4. Year Of Publication Author Description
2010 Sanda Maria llii Variation of surface roughness in plasma arc cutting
2012 Salonitis Plasma arc cutting on S235 steel with o2 as plasma gas and air as shield
gas was used
2013 Seong-il Kim Drawback of plasma cutting in thick steel plates and concluded that heat
dissipation is directly proportional to current
2014 Milan Kumar Das The responses of MRR and surface roughness are proportional to gas
pressure and current respectively
2015 Gurwinder Singh Material removal rate in CNC plasma arc cutting using Taguchi L9
2015 Yong Xiang Used the Taguchi method for analyzing the mild steel
2015 Maity Effect of cutting parameters in AISI316SS
2015 Leander Scjleuss Plasma arc cutting with the technique on SEM using honey combed -
structured sheet
2016 Senthil kumar Analyzed the parameters in plasma arc cutting process by signal noise ratio
2017 Pawar S Analyzed SS 316 l pin plasma arc cutting process by grey relational
analysis
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5.  The main objective of this work is to analyze the cut surface quality of stainless steel at combination of direction of torch
arrangement
 This work is based on plasma cutting which has too may runs to perform the work.
 In order to overcome this Taguchi method is followed.
 Taguchi method is combinations of arrays.
 To minimize the runs of machine Taguchi L9 Orthogonal array is introduced.
 To compare the results a statistical method called ANOVA is used.
 Plasma arc cutting machine used in this work is Micro Step Spol S.R.O. plasma arc cutting machine
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6.  Selected material: SS321(Austenitic Stainless Steel)
Elements Composition
Carbon 0-0.08
Manganese 0-2.0
Silicon 0-0.75
Phosphorous 0-0.045
Chromium 17.0-19.0
Nickel 9.0-12.0
Nitrogen 0-0.10
Titanium 0-0.70
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7.  This experiment is to study the cut surface quality of stainless steel materials using plasma arc cutting
 First thing in this experiment is to select the parameters to give the input to cut the material.
 In this experiment there will be four input parameters each containing three trails.
 So it will consists a total of 3x3x3x3=81 runs. To minimize this runs Taguchi L9 Orthogonal array is introduced.
 9 samples are cut to take output parameters.
 Total three output parameters are collected.
 The surface quality of the sample is measured by using the surface roughness instrument TR.
 After collecting the output from all samples , compare them and analyze them with a statistical method called ANOVA.
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8. INPUT PARAMETERS:
 The following are the input parameters to be taken for
performing the work:
a. Cutting speed
b. Current
c. Stand off distance(SOD)
d. Gas pressure
 The following are the output parameters to be taken after
performing the sample cutting:
a. Surface roughness
b. Kerf width
c. Heat effected zone
OUTPUT PARAMETERS:
Sl.
No
Input parameters Range
1. Cutting speed 1500-4500
mm/min
2. Current 40-80 A
3. SOD 2-4 mm
4. Gas Pressure 700-800 Psi
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9.  Using Mat Lab genetic algorithm is developed to optimize the results developed through experiment.
 Genetic algorithm is a popular technique to find optimal solutions through the principle of natural genetics.
 In this searching process imitates the natural growth of biological feature and turns out to be an intelligent development of a
random search to find the optimal value of the result.
 Parameter setting of genetic algorithm in Mat Lab:
Population type Double vector
Population size 100
Scaling function Rank
Selection function Stochastic uniform
Elite count 2
Cross over probability 0.8
Generations 250
Cross over function Two point crossover
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10. Solid works design model of specimen to be cutted
Cut samples of specimen in plasma arc cutting 10/22
❖RESULTED MODEL OF EXPERIMENT:

11.  The below table shows the results obtained after performing experiment:
 Here output parameters are the results obtained through given input parameters.
Input parameters Output parameters
Sl
.N
o
Cutting
speed
(mm/mi
n)
Current
(Amps)
SOD
(mm)
Gas
pressure
(Psi)
Surface
roughness
Ra
(µm)
Kerf width
(mm)
Heat affected
zone
(mm)
1 1500 60 2 700 0.85 8.76 4.16
2 1500 40 3 800 0.62 10.09 2.95
3 1500 80 4 750 2.12 7.53 5.65
4 3000 80 3 800 1.56 7.43 5.49
5 3000 60 4 750 1.45 8.32 4.39
6 3000 40 2 700 0.51 9.87 2.92
7 4500 60 4 800 1.01 8.98 4.18
8 4500 40 2 750 0.76 9.20 3.10
9 4500 80 3 700 2.07 7.65 5.54
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❖RESULT OF EXPERIMENT:

12.  Analysis of results have been done by using ANOVA, genetic algorithm with the help of design expert 7.0 software./ design
expert 12.0 software
 Analysis of results is classified majorly into three categories based on output parameters.
 This analysis contains three ANOVA tables and nine 3D surface graphs ad other statistical graphs.
 The three ANOVA tables are :
a. ANOVA table for surface roughness
b. ANOVA table for Kerf width
c. ANOVA table for heat affected zone
 Apart from these this analysis shows 3d surface graphs of each output parameter generated with respect to different input
parameters.
 Based on the results generated from both ANOVA and genetic algorithm we will conclude the optimized value of the each
output parameter to get good cut surface quality.
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13. source
Sum of
squares df
Mean
square
F-
value
P-
value
Model 2.64 4 0.6610 8.43 0.0313
Cutting
speed
0.0104 1 0.0104 0.1329 0.7339
Current 1.14 1 1.14 14.61 0.0187
SOD 0.1408 1 0.1408 1.80 0.2511
Gas
pressure
0.0880 1 0.0880 1.12 0.3490
Residual 0.3135 4 0.0784 --- ---
Cor Total 2.96 8 --- --- ---
•Factor coding is Coded.
• Sum of squares is Type III - Partial
•The Model F-value of 8.43 implies the model is
significant. There is only a 3.13% chance that an F-value
this large could occur due to noise.
•P-values less than 0.0500 indicate model terms are
significant. In this case B is a significant model term.
Values greater than 0.1000 indicate the model terms are
not significant. If there are many insignificant model terms
(not counting those required to support hierarchy), model
reduction may improve your model.
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14. source
Sum of
squares df
Mean
square
F-
value
P-
value
Model 7.22 4 1.80 11.66 0.0177
Cutting
speed
0.0504 1 0.0504 0.3257 0.5987
Current 5.01 1 5.01 32.37 0.0047
SOD 0.0091 1 0.0091 0.0586 0.8206
Gas
pressure
0.0003 1 0.0003 0.0022 0.9651
Residual 0.6192 4 0.1548 --- ---
Cor Total 7.84 8 --- --- ---
•Factor coding is Coded.
• Sum of squares is Type III - Partial
•The Model F-value of 11.66 implies the model is
significant. There is only a 1.77% chance that an F-value
this large could occur due to noise.
•P-values less than 0.0500 indicate model terms are
significant. In this case B is a significant model term.
Values greater than 0.1000 indicate the model terms are
not significant. If there are many insignificant model terms
(not counting those required to support hierarchy), model
reduction may improve your model.
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15. source
Sum of
squares df
Mean
square
F-
value
P-
value
Model 9.92 4 2.48 182.13 <0.000
1
Cutting
speed
0.0006 1 0.0006 0.0441 0.8440
Current 6.29 1 6.29 462.08 <0.000
1
SOD 0.0114 1 0.0114 0.8379 0.4118
Gas
pressure
0.0038 1 0.0038 0.2793 0.6215
Residual 0.0545 4 0.0136 --- ---
Cor Total 9.97 8 --- --- ---
•Factor coding is Coded.
• Sum of squares is Type III - Partial
•The Model F-value of 182.13 implies the model is
significant. There is only a 0.01% chance that an F-value
this large could occur due to noise.
•P-values less than 0.0500 indicate model terms are
significant. In this case B is a significant model term. Values
greater than 0.1000 indicate the model terms are not
significant. If there are many insignificant model terms (not
counting those required to support hierarchy), model
reduction may improve your model.
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16.  To analyze the cut quality characteristics of stainless steel we used the mechanism of ANOVA and genetic algorithm.
 From the results we can observe that stainless steel materials have good hardness and strength eve at high temperatures.
 Plasma arc cutting is a thermal energy based process used to cut the hard materials which cannot be cut in a conventional
method.
 From the final results we have the contribution of input and resulted values of output parameters are as follows:
Input parameters Output parameters
Parameter value parameter value
Cutting speed 0.59% Surface roughness 0.28,
732µm
Current 93.75% Kerf width 7.3943mm
Stand off distance 3.5%
Heat effected zone 2.9242mm
Gas pressure 2.13%
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17.  Quality of cut surface is one of big issue in machining of metals ,in industries.
 For this issue we have solution through plasma fusion cutting.
 Cutting of metal is not always possible in a conventional method . Hence there will be a high dependent on non-conventional
methods of cutting to get required shape of cut inn future.
 At this kind of situations industries mostly use plasma arc cutting .
 Since future will have a gross development in manufacturing domain.
 There will be more development in technology of plasma arc cutting like usage of different gas for pressure and avoiding
metal reaction with gas chemically or physically.
 It may also be seen in all industries where we have mass production in a short time span.
 It also being developed to cut other materials like plastic , glass etc, with good cut surface quality
 So by this we can say that there will be a drastic development in usage of plasma cutting in future.
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18.  Stand of distance :
1. It is defined as the distance between work surface to tip of nozzle.
2. It is the safety distance that is maintained in the cutting machine to avoid unexpected accidents.
3. It also effects the cut surface quality
 Surface roughness:
It is defined as the term that says about the cut surface like presence of any peaks or valleys, etc,.
 Kerf width:
It is defined as the width of the material that is removed in a cutting process.
 Heat affected zone:
Heat-Affected Zone (HAZ) refers to a non-melted area of metal that has experienced changes in its material properties as a
result of exposure to high temperatures.
 Regression model:
1. Regression model or Regression analysis is a statistical analysis and predictive modeling technique which investigates the
relationship between a dependent and independent variables.
2. It is used in forecasting, time series modeling and finding the causal effect relationship between the variables.
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19.  ANOVA:
1. Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the
"variation" among and between groups) used to analyze the differences among group means in a sample.
2. It is used to analysis the experimental data.
 GENETIC ALGORITHM :
1. Genetic algorithm is a type of artificial intelligence system used to generate optimized results.
2. It will be using Meta ad Heuristic Knowledge to work.
3. It is used for solving both constrained and unconstrained optimization problems that is based on natural selection, the
process that drives biological evolution.
4. It repeatedly modifies a population of individual solutions
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20.  MRR: Material Removal Rate
 ANOVA: Analysis Of Variance
 SOD: Stand off distance
 S.R.O: Self Regulatory Organisation
 AISI: American Iron and Steel Institute
 SEM: Structural equation modelling
 Ra :Surface Roughness
 HAZ : Heat Affected Zone
 HTPAC: High-Tolerance Plasma Arc Cutting
 SV: Source of Variance
 df: Degree of Freedom
 GA: Genetic Algorithm
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21.  http://www.davidpublisher.org/index.php/Home/Article/index?id=13435.html
 https://www.esabna.com/us/en/education/blog/what-is-cutting-kerf.cfm
 https://www.youtube.com/watch?v=-yQb_ZJnFXw
 https://www.ispc-conference.org/ispcproc/ispc20/106.pdf
 https://www.researchgate.net/publication/256092680_Experimental_Investigation_of_the_Plasma_Arc_Cutting_Process
 https://onlinelibrary.wiley.com/doi/full/10.1002/ppap.201800118
 https://www.slideshare.net/dpksobs/plasma-arc-machining-process
 https://www.researchgate.net/publication/325038517_Developments_in_Plasma_Arc_Cutting_PAC_of_Steel_Alloys_A_Revi
ew
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22. 22/22
BY :
CHEKURU VISHNU SAI KUMAR
RAMAPURAM PAVAN KUMAR
KASTURI UMESH CHANDRA
UNDER GUIDANCE OF:
V. BALAJI
ASSISTANT PROFESSOR
Veltech Dr. RR & Dr. SR University