A Statistical study on effects of fundamental machining parameters on surface topography

IDL - International Digital Library Of
Education & Research
Volume 1, Issue 3, Mar 2017 Available at: www.dbpublications.org
International e-Journal For Education And Research-2017
IDL - International Digital Library 1 | P a g e Copyright@IDL-2017
A Statistical study on effects of fundamental
machining parameters on surface topography
Manikandan H 1
(Author), Saurabh Jagtap2
(Author), Tufan Chandra Bera3
(Author)
Dept.of Mechanical Engineering,Birla Institute of Technology and Science,Pilani,Rajasthan,India
1
Birla Institute of Technology and Science,Pilani,Rajasthan,India
2
3
Pilani,India
1
manikandan.h@pilani.bits-pilani.ac.in
Abstract: Roughness consists of the irregularities
of the surface texture, usually including those
irregularities that result from the actions involved in
the production process. Surface roughness is an
important measure of the quality of a machined
product and a factor that greatly determines
manufacturing cost. In this work,in order to estimate
surface quality and dimensional precision properties in
advance, theoretical models are employed making it
feasible to do prediction in function of operation
conditions and machining parameters such as feed
speed and depth of cut etc. The need for statistical
method like DOE for studying the relationship
between the machining parameters is because of this
need for prediction. It is a analysis technique which
uses the regression method to find out the relationship
between various factors in a DOE setup depending
upon the interactions of the predictor variables and the
response variables which is performed in the
experiments. The research in this domain will help
advance further investigations into the relationship
between the machining factors and the surface quality
of the machined components. The DOE using
Taguchi’s method and statistical study of the
experimental data helps to understand the interaction
between various factors like speed, feed and depth of
cut in the machining.
Keywords: Surface Roughness, Taguchi Method,
Orthogonal Arrays, Design of experiments,
Response Surface Method
1. INTRODUCTION
The surface finish of machined components has
considerable impact on some properties such as wear
and fatigue strength. Thus, the quality of the surface is
truly important in the evaluation of the productivity of
machine tools and mechanisms of production, and
mechanical components. Fixing a proper cutting
condition is really important regarding this because
these determine surface quality of manufactured
components. In order to know surface quality and
dimensional precision properties in advance, it is
always better to employ mathematical models making
it easier to do prediction in function of operation
conditions.The mechanism for the finalisation of
surface roughness is very dynamic, complicated, and
process dependent so that it is very difficult to
calculate its value through theoretical or calculation
based analysis.This presents the need for statistical
method like DOE for studying the relationship
between the machining parameters and roughness of
surfaces.

Nomenclature
Ra Roughness average of the measured surface.
h Height of the wall specimen used
t Thickness of the wall specimen used
l Length of the wall specimen used
DOE Design of Experiments
RSM Response Surface Method
Roughness consists of the irregularities of the surface
texture, usually including those that result from the
actions included in the manufacturing process. There
are number of parameters that can be used to define
the surface roughness but we choose Roughness
Average.
Figure 1. Parameters in Surface Roughness
1.1 Literature Review
Many researchers have investigated the
impact of machining parameters on the surface quality
of various materials by using numerous methods of
DOE (design of experiments) to find out the empirical
relationship between various factors affecting the
surface quality of a machined surface.
Süleyman Nes eli , Süleyman Yaldız and
Erol Türkes determined RSM method [1] to estimate
the surface roughness in turning of mild steel by
making use of Taguchi L27 orthogonal array.
Mohamed A. Dabnun, M.S.J. Hashmi and M.A. El-
Baradie [3] developed a response model (surface
roughness) utilizing factorial design of experiment and
response surface methodology. They used 2^3
factorial design with a centre composite design- 12
experiments altogether.Yusuf Sahin and A. Riza
Motorcu [2]used Taguchi method with L18 orthogonal
array 3 factors and 5 level CCD First-order and
second-order model predicting equations for surface
roughness have been established by using the
experimental data.
Julie Z. Zhang , Joseph C. Chenb, and E.
Daniel Kirby [4]used Taguchi method using L9 array
and ANOVA analysis.They were using Taguchi
design application to optimize surface quality in a
CNC face milling operation.M.Y. Noordin, V.C.
Venkatesh , S. Sharif , S. Elting and A. Abdullah
worked with DOE[5] using RSM in 2^3 factorial
design to establish a 2nd order model using Least
square method and ANOVA method.
The research in this domain will help advance
further investigations into the relationship between the
machining parameters and the surface quality of the
machined components. The DOE using Taguchi’s
method and statistical analysis of the experimental
data helps to understand the interaction between
various factors in the machining.
1.2 Roughness average (Ra)
This parameter is also known as the
arithmetic mean roughness value, AA (arithmetic
average) or CLA (center line average). Ra is
universally recognized and the most used international
parameter of roughness.
Where Ra = the arithmetic average deviation from the
mean line
L = the sampling length

1.3 Factors affecting surface roughness
The depth of cut ,the feed rate per cutter ,the
cutting ,the engagement of the cutting tool (ratio of
cutting width to cutting tool diameter,the cutting tool
,the use of cutting and the three components of the
cutting force.
2. METHODOLOGY
2.1 DOE- Design of Experiments
The response surface methodology (RSM)
and Taguchi techniques for design of experiments
(DoE) are most wide-spread techniques for the
prediction of surface roughness.
2.2 Full Factorial Method
When there are two or more factors each at
two or more levels, a treatment is defined as a
combination of the levels of each factor. In a factorial
experiment, all possible combination of the levels of
each factors are represented for each complete
experimentation. The number of experiments is equal
to the product of the number of factor levels and can
therefore become very big when either the factors are
more or the levels are numerous.
Method includes
Planning phase:
State the problem.
State the objectives of the experiment.
Select the factors that may influence the quality
characteristics.
Select levels for the factors.
Determine the number of experiments to be carried
out.
Execution phase:
Conduct the experiment as described by the full
factorial design.
Analysis phase:
Analyse the experimental results, e.g. using analysis of
variance (ANOVA), response surface method (RSM).
Conduct a confirmation experiment.
2.3 Taguchi Method using Orthogonal Array
Orthogonal arrays are special standard
experimental design which requires only a small
number of experimental trials to find the main effects
of factors on output. It is a highly fractional
orthogonal design that is based on a design proposed
by Genichi Taguchi and allows you to consider a
selected subset of combinations of multiple factors at
multiple levels. Taguchi Orthogonal arrays are
balanced to ensure that all levels of all factors are
considered optimally.
Planning phase:
State the problem.
State the objectives of the experiment.
Select the quality characteristics and the measurement
systems.
Select the factors that may influence the quality
characteristics.
Select levels for the factors.
Select the appropriate Taguchi fractional matrices or
orthogonal arrays (OAs).
Select the interactions that may influence the response
variable.
Assign factors to OAs and locating interactions.
Select and mention the noise factors.
Execution phase:
Conduct the experiment as described by the OAs.
Analysis phase:
Analyse the experimental results, e.g. using analysis of
variance (ANOVA), response surface method (RSM).
Conduct a confirmation experiment.
2.4 RSM – Response Surface Methods
RSM is a statistical technique based on
multiple regressions. With RSM, the effect of two or
more parameters on quality criteria can be calculated
and real values are obtained in RSM. In this way, the
values that are not actually tested using experimental
sets of values themselves can be estimated and the
combinations help in doing this. The results can
expressed in 3D series or counter map.It is a analysis
technique which uses the regression method to find
out the relationship between various factors in a DOE
setup.

2.5 DOE using Minitab 16
Minitab 16 is a useful tool for design and
analysis of experiments. It is possible to setup a DOE
if one has decided the number of factors and there
corresponding levels. Following flowchart represents
the method for setup of DOE in Minitab 16.
2.6 Flowchart
3. EXPERIMENTAL DETAILS
3.1 The sample data
The sample data is used (for check only) from
[1] where it presents the DOE analysis using RSM for
establishing the empirical relationship between the
machining factors like Nose radius (r), Approach angle
(k) and Rake angle (y).
Table 1. Independent Variables and Levels for Model
Body
Symbol Factor Unit
r Nose Radius mm
k Approach Angle Degree( 0
)
y Rake Angle Degree( 0
)
Symbol Level 1 Level 2 Level 3
r 0.4 0.8 1.2
k 60 75 90
y -9 -6 -3
3.2 Experiments
Step 1
Select the Stats DOE Taguchi Create Taguchi
design
Figure 2. Selecting the levels in minitab
Step 2
Select the Levels factors select the design L27
enter the values of factors
Figure 3. Entering the values of factors3

Step 3
Following experimental data is selected
Table 2. Experimental Data
r k y Ra
0.4 60 -9 2.025
0.4 60 -6 2.283
0.4 60 -3 2.892
0.4 75 -9 2.358
0.4 75 -6 2.85
0.4 75 -3 3.962
0.4 90 -9 3.509
0.4 90 -6 4.099
0.4 90 -3 4.876
0.8 60 -9 4.225
0.8 60 -6 5.142
0.8 60 -3 5.692
0.8 75 -9 4.308
0.8 75 -6 6.066
0.8 75 -3 6.563
0.8 90 -9 5.01
0.8 90 -6 7.944
0.8 90 -3 7.99
1.2 60 -9 4.475
1.2 60 -6 5.065
1.2 60 -3 5.967
1.2 75 -9 4.796
1.2 75 -6 7.662
1.2 75 -3 8
1.2 90 -9 5.874
1.2 90 -6 8.665
1.2 90 -3 8.951
Step 4
Select the Stats DOE Response Surface
Method Define Custom Response Surface design
select the factors select the response variable ok.
Figure 4. Selecting the response variable
4.Design,Modelling and Manufacturing of
Experimental specimen.
With the literature review and the study of the DOE
models it was planned to carry out the experimentation

to determine the effects of the following machining
parameters.
Radial depth of cut
Axial depth of cut
Feed per tooth
Each factor to have 3 levels. Taguchi model for DOE
is selected with L9 orthogonal array.
The experimental model is designed to
acquire data for the statistical analysis and establish
the relationship between the machining parameters.
The thin wall model is considered with aluminium as
the material parameters of design as defined below
For thin wall consideration
Wall height in mm
Wall thickness in mm
Wall length in mm
With the available material block we have wall length
of 75 mm, wall thickness of 5mm and height as 40
mm as per the above mentioned formula.The detailed
design of the block is mentioned below with
dimensions (all dimensions in mm)
Figure 5(a) Drawing of the part
Figure 5(b). Model
Figure 5(c). Specimen
Figure 5(d).Experimental Setup( including Renishaw
touch trigger Probe)
4. RESULTS AND DISCUSSION

Step 6
Results of the RSM analysis are obtained as seen
Figure 6. Results
Figure 7. Results
To get the graphs of main effect
Select the Stats DOE Taguchi Define Custom
Taguchi design select the factors select the
response variable ok.
Select the Stats DOE Taguchi Analyse the
Taguchi design select the factors select the
response variable ok.
Figure 8. Plots
Step 8
To get the graphs of Interaction effects
Select the Stats ANOVA Interaction Plots
select the factors select the response variable select
full interaction matrix ok.
Step 9
To get the Response Surface plots

Select the Graphs 3D surface plot select the
factors select the response variable ok.
Figure 9. Ra Plots
Figure 10. Ra Plots
Figure 11. Ra Plots
5.CONCLUSION
Tasks were completed during experimentation and
analysis.Initially a study about the design of
experiments and application of same was conducted.
Then selection of DOE method depending on the
number of factors and levels were carried out,
afterwhich the design of workpiece for
experimentation was done.MINITAB analysis of data
generated from sample data set was performed and the
relationship between the machining parameters and
roughness was established. So predicting optimal
value of surface roughness for given set of machining
parameters became possible.
6.ACKNOWLEDGEMENTS
The author thank and acknowledge the Science and
Engineering Research Board (SERB), Department of
Science and Technology, Government of India for
financial support to carry out this research work
(Project No: SERB/ETA-0003/2013).
7.REFERENCES
[1] Neşeli, S., Yaldız, S., & Türkeş, E. (2011).
Optimization of tool geometry parameters for turning
operations based on the response surfac
methodology.Measurement, 44(3), 580-587.

[2] Sahin, Y., & Motorcu, A. R. (2005). Surface
roughness model for machining mild steel with coated
carbide tool. Materials & design, 26(4), 321-326.
[3] Dabnun, M. A., Hashmi, M. S. J., & El-Baradie,
M. A. (2005). Surface roughness prediction model by
design of experiments for turning machinable glass–
ceramic (Macor). Journal of Materials Processing
Technology, 164, 1289-1293.
[4] Zhang, J. Z., Chen, J. C., & Kirby, E. D. (2007).
Surface roughness optimization in an end-milling
operation using the Taguchi Arbizu, I. P., & Perez, C.
L. (2003). Surface roughness prediction by factorial
design of experiments in turning processes. Journal of
Materials Processing Technology, 143, 390-396.
[5] Noordin, M. Y., Venkatesh, V. C., Sharif, S.,
Elting, S., & Abdullah, A. (2004). Application of
response surface methodology in describing the
performance of coated carbide tools when turning
AISI 1045 steel. Journal of Materials Processing
Technology, 145(1), 46-58.
[6] Amitava Mitra ,Fundamentals of Quality Control
and Improvement 3rd edition. Wiley publication.
[7] Mohanta D K(2012).Prediction of Surface
roughness in turning of low carbon steel with coated
and uncoated inserts.International journalof advanced
materials manufacturing and characterization March
2012 Vol 1 Issue 1
[8] Vikas,Apurba Kumar Roy,Kaushik
Kumar(2014).Effect and Optimization of various
machine process parameters on the surface roughness
in EDM for an EN41 material using Grey-
Taguchi.Third International Conference on Materials
Processing and Characterization(ICMPC 2014),385-
390

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