Master Thesis Analyzes Cutting Tool Insert Surface Topography

MASTERTHESIS Master´s Programme in Mechanical Engineering, 60 credits
Surface Topographical Analysis Of Cutting
Inserts
Zoel-fikar El-ghoul , Shobin John
Master Thesis 15 credits
Halmstad 2016-10-10

Preface
i
Preface
This study is a result of master’s thesis in mechanical engineering at Halmstad University in
collaboration with Sandvik Coromant during spring term 2016.
The main contribution of the present work focus on the development of a significant approach
to identify best possible surfaces finish strategy in terms of topographical study. The aim of
the thesis was to analyze, compare differently pre- and post-treated cutting tool inserts, and
correlate surface properties with the different treatment methods and to work out a method for
such analysis to be used by the company in the future.
We would like to emphasize our thanks Professor Bengt-Göran Rosén for his support
guidance, opportunely posed questions that raised new lines of thought and motive to get
good work on the thesis.
We would like to emphasis sincere thanks and gratitude to Isabel Källman to guide
throughout the thesis and support during urgent need.
We are grateful to other dissertation committee members Dr. Z. Dimkovski and Dr. Sabina
Rebeggiani for enlightening and inspiring discussion and their advice provided us guidelines
in difficult times.
We would like as a final word of appreciation to thank the people of functional surfaces
research group at Halmstad University for their thoughtful comments and suggestion, which
continually improve the quality of the dissertation.
Zoel-fikar El-ghoul Shobin John

Abstract
ii
Abstract
The following report conducted with collaboration of the University of Halmstad and AB
Sandvik Coromant.
The focus of the project is characterizing the surface topography of different surface treatment
variants before and after chemical vapor deposition (CVD).
As a part of improving the knowledge about the surface area characterization and accomplish
a better knowledge and understanding about surfaces and its relation to wear of uncoated
WC/Co cutting tools The project initiated in February 2016 and end date was set to May
2016.
The methodology used in this thesis based on the statistical analysis of surface topographical
measurements obtained from interferometer and SEM by using Digital-Surf-MountainsMap
software.
The finding from this thesis showed that Mean and Standard deviation method, Spearman’s
correlation analysis and Standard deviation error bar followed by ANOVA and T-test are
effective and useful when comparing between different variants.
The thesis resulted in a measurement approach for characterizing different surface
topographies using interferometer and SEM together with statistical analysis.
Keywords: 3D-Surfaces Texture, CVD coating inserts, Interferometer, Spearman’s correlation and
ANOVA & T-test.

Tables of Contents
iii
Tables of Contents
Preface ............................................................................................................................... i
Abstract.............................................................................................................................ii
Tables of Contents ...........................................................................................................iii
Symbols and Abbreviations.............................................................................................. v
1. INTRODUCTION ................................................................................................... 1
1.1 Background .......................................................................................................... 1
1.1.1. Presentation of the client............................................................................... 3
1.2 Aim of the study................................................................................................... 4
1.3 Problem definition................................................................................................ 4
1.4 Limitations ........................................................................................................... 4
1.5 Individual responsibility and efforts during the project....................................... 4
1.6 Study environment ............................................................................................... 5
2. METHOD ................................................................................................................ 6
2.1 Alternative methods ............................................................................................. 6
2.1.1 Average and Standard Deviation Method .................................................... 6
2.1.2 Spearman’s rank order correlation method .................................................. 7
2.1.3 Standard deviation error bar followed by Anova and T-test........................ 8
2.2. Chosen methodology for this project ................................................................... 11
2.3. Preparations and data collection........................................................................... 11
3. THEORY ............................................................................................................... 12
3.1. Summary of the literature study and state-of-the-art ........................................... 12
3.1.1 Function ...................................................................................................... 13
3.1.2 Manufacturing............................................................................................. 15
3.1.3 Characterization.......................................................................................... 15
4. RESULTS .............................................................................................................. 20
4.1 Presentation of experimental results of work package 1....................................... 20
4.1.1 Parameters Selection Methods.................................................................... 20
4.1.2 Average and Standard Deviation method................................................... 20
4.1.3 Spearman’s rank correlation method.......................................................... 23
4.1.4 Standard deviation Error Bar (EB) followed by Anova &T-test method .. 23
4.3. Presentation of experimental results of work package 2...................................... 25
4.3 Methods for selecting the parameters................................................................ 25

iv
5. CONCLUSIONS AND FUTURE WORK............................................................ 27
5.1 Conclusions........................................................................................................ 27
5.1.1 Work Package 1.......................................................................................... 27
5.1.2 Work Package 2.......................................................................................... 32
5.1.3 Recommendation to future activities .......................................................... 37
6. CRITICAL REVIEW ............................................................................................ 38
6.1 What factors affect the work been done differently........................................... 38
6.2 Environmental and sustainable development..................................................... 38
6.3 Health and Safety ............................................................................................... 38
6.4 Economy............................................................................................................. 39
6.5 Ethical aspects.................................................................................................... 39
REFERENCES ............................................................................................................... 40
TABLE OF CONTENT FOR APPENDICES................................................................ 43

Symbols and Abbreviations
v
Symbols and Abbreviations
WP 1: Work Package1
WP 2: Work Package 2
MSG: Name to represent different variants
CNMG120408-MM: Cutting inserts Specification
SEM: Scanning Electron Microscope
3D: Three Dimension
316L: Sanmac 316/316L is a molybdenum-alloyed austenitic chromium-nickel steel with
improved machinability
Ti(C, N): Titanium Carbon nitride
Al2O3: Aluminum Oxide
TiN : Titanium Nitride
Co : Cobalt
ANOVA: Analysis of Variance named for Fisher
WC: Tungsten carbide
SE: Standard Error
S.D: Standard Deviation
E.B: Error Bar
V: Number of Variants
NEBNO: Number of error Bar Not Overlapping
Si: Significant Values in ANOVA test
TRUES: Parameter is disjunct for variants with 95 percentage confident interval
CVD: Chemical Vapor Depositio

INTRODUCTION
1
1. INTRODUCTION
Surface integrity is defined as the inherent or enhanced condition of a surface produced by
machining or other generating operation. It contains not only the geometry consideration,
including surface roughness and accuracy, but also another surface/subsurface microstructure.
The success of the transformation is dependent on a number of variables such as surface
texture, wetting properties of the solid surface by the liquid and coating viscosity. Coatings
and painting applied to the surface; the purpose of such operations may be to improve their
chemical and mechanical properties. The existence of the correct functional groups in an
accessible position is an important factor to be identified and controlled. Thus, surfaces are
produced with a texture resembling a landscape, the determination and control the surface
area and surface composition are essential for the study of catalysts, even small variation of
properties may lead to unwanted results in production and can cause the rejection of the batch.
It is useful to modify the surface performance when it does not possess the specified
requisites; it is possible to change mechanical or visual properties of surfaces improvement
in sliding, thermal properties, corrosion, adhesion, wear, yield and appearance.
The wide variety of parameters that used in the characterization of surface finishing is a piece
of evidence of its magnitude. The characterization of surface finishing is usually
accomplished defining numerical 3D surface texture parameters (ISO-25178). Today
selections of appropriate parameters for analyzing the surfaces are widely investigated. The
detailed study about the surface (relation between manufacturing processes, directionality
etc.) by using the selected parameters is also highlighted of this study.
1.1 Background
The precise characterization of surface roughness is of paramount importance because of its
considerable influence on the functionality of manufactured products [1]. Modern technologies
depend for the Satisfactory functioning of their processes on special properties of some solids,
mainly the bulk properties, as an important group of these properties [2]. The behavior of
material depends on the surface of the material, surface contact area and environment under
which the material operates, to make a better understanding for the surface properties and their
influence on the performance of the various components, machines and units, surface science has
been developed. Surface science defined as a branch if science dealing with any type and any
level of surface and interactions between two or more entities, these interactions could be
chemical, physical mechanical, thermal and metallurgical [3]. Our important concern area is the
surface engineering which provides on the of most important means of engineering product
differentiations in terms of quality, lifecycle cost and performance, it is the definition of the
design of the surface and substrate together as a functionally graded system as a functionally
graded system to give a cost effective enhancement. The various manufacturing processes
applied in industry produce the desired shapes in the components within the prescribed
dimensional tolerances and surface quality requirements. Surface topography and texture is a
foremost characteristic among the surface integrity magnitudes and properties imparted by the
tools used in the processes, machining mostly, and especially their finishing versions. Surface

INTRODUCTION
2
quality and integrity can be divided in three main fields: surface roughness, microstructure
transformations and residual stress.
Surface integrity describes not only the topological (geometric) features of surfaces and their
physical and chemical properties, but also their mechanical and metallurgical properties and
characteristics [4]. Surface integrity is an important consideration in manufacturing
operations, because it influences such properties as fatigue strength, resistance to corrosion,
and service life. Most manufacturing process will have some impact on surface integrity,
when these processes performed using poor techniques, this can be responsible for inadequate
surface integrity and can lead to significant changes and defects, and these defects usually
caused by a combination of factors, such as:
 Improper control of the process parameter, (which can result surface deformation,
excessive stress, excessive heat, cold or speed or work can also lead to significant
changes).
 Defects in the original material.
 The method by which the surface produced, and manufactured.
More invasive procedures usually have some permanent effect on surface integrity. Almost
any chemical treatment, as well as excessive heat, can alter the material at its molecular level,
bringing about irreversible changes to its very structure. These changes can be positive or
negative. Positive changes are those that give the material the desired finish or appearance
also include those that improve properties like strength and hardness, while negative change
could mean that the material no longer be used as intended.
The surface topography and material characteristics can affect how two bearing part slide
together, how fluids interact with the part and how it looks and feel, the need to control and
hence measure surface become increasingly important [5]. The various manufacturing
processes applied in industry produce the desired shapes in the components within the
prescribed dimensional tolerances and surface quality requirements for the last five decades
the complex relationship between surface texture and adhesion has interested scientists and
engineers. Authors identify that types and degrees of surface texture appear to have
beneficial effects on adhesion. Surface profile parameters may potentially be restrictive and
misleading, In Particular cases of tribology the surface roughness influences adhesion,
brightness, wear, friction in wet and dry environment [6]. Very few adhesion researchers
have considered areal surface texture parameters to characterize surface texture over the
last ten years, a period of time within which equipment, data processing software and
published texts have provided access to the use of areal parameters. Whilst an example of the
use of the Arithmetic mean surface texture (Sa) parameter can be cited in the context of
adhesion little attempt has been made to consider the breadth of parameters (and consequently
surface disruption) available.
Surface topography greatly influences not only the mechanical and physical properties of
contacting parts, but also the optical and coating properties of some non-contacting
components. The characteristics of surfaces topography in amplitude, spatial distribution and
pattern of surface feature dominate the functional application, surface in contact, residual
stresses in the surface layer and oxides on the metal surface [7] as shown in Figure 1.

INTRODUCTION
3
Figure1.1: Metallic outer surface layers displaying the complex structure machined surface superimposed on
the base metal [8].
The areal characterization of surface texture plays an increasing important role in control the
quality of the surfaces of a work piece. Surface texture parameter, which is the profile
parameter, which developed to monitor the production process, as assessment we do not
usually see field parameter values but pattern of features such as hills and valleys. The
relationship between them and by detecting and the relationships between them, it can
characterize the pattern in surface texture, parameter that characterize surface features and
their relationships are termed feature parameter [9].
1.1.1. Presentation of the client
Sandvik Coromant headquartered in, Sweden. A Swedish company supplies cutting tools and
services to the metal cutting industry. It is part of the business area of Sandvik Machining
Solutions, which is within the global industry group Sandvik. In 2012 Sandvik was #58 on
Forbes list of the world's most innovative companies. Sandvik Coromant is a global company
with production facilities connected worldwide to three distribution centers in the US, Europe
and Asia. Sandvik Coromant is represented in more than 130 countries with some 8,000
employees worldwide; with extensive investments in research and development, they create
unique innovations and set new productivity standards together with their customers. These
include the world's major automotive, aerospace and energy industries. Their metal working
operations of Coromant mainly focus on milling, turning, boring and drilling.
Figure1.2: Sandvik product
Sandvik Coromant its large investment in research and development, as much as twice the R&D
spending every year of the average company in its industry.

INTRODUCTION
4
1.2 Aim of the study
The main objective of this study is the characterization of cutting insert (CNMG120408-MM)
surface topography. The geometry of the inserts is CNMG120408-MM; the characterization
divided into work packages one and two, which presented below:
 Work package 1: Surface characterization of uncoated WC-Co inserts surfaces
 Which parameters describing the topography of the variants are important to
look at when comparing the different variants?
 How well does the study of surface topography of variants correlate to the
manufacturing process?
 Is there any predominant direction of the topography of the different variants?
 Work package 2: Analysis of CVD coated surface treatment variants.
 Which parameters are important for comparing the different variants to each
other?
 Can a connection found between the treatment prior to coating and the outcome
of the treatment after coating?
 Is there any different measurement approach needed to evaluate the surface
roughness on variants in Work Package 2 compared to Work Package 1?
1.3 Problem definition
In the first meeting with Sandvik Coromant, the tasks were assigned and the authors started to
investigate about the surface topography of the variants by finding the appropriate method in
order to select the parameters when comparing between different variants.
In work package one, before the chemical vapor deposition; they manufactured three variants
MSG 157, MSG158 and MSG160. Variants MSG 157 and MSG158 had treated with two
different processes in order to find the effects of adhesion of the CVD coating. While the
variant MSG 160 treated by polishing in order to investigate if any predominant direction of
the topography.
In work package two, it is required to investigate the surface texture between five different
variants with different kinds of treatment.
1.4 Limitations
Due to the time limitation, the variants were measured by using Interferometer only, the
methods were found in order to compare surfaces of different variants after the coating. The
limitations consist of:
 Only discussed methodology and quantitative study of the surface integrity of the
variants
 Machining test needs more investigation.
1.5 Individual responsibility and efforts during the project
Both authors have put the same amount of the effort in this thesis. The amount of time spent
for measurements, analyzing the measurements and gathering information regarding the

INTRODUCTION
5
project, also the presentation with Sandvik Coromant including research and writing the
report.
1.6 Study environment
Both of the authors have worked on this thesis at different locations, practical and theoretical
framework of the thesis including writing the report at the Halmstad University.

METHOD
6
2. METHOD
This study (Quantitative and qualitative) is based on the topographic analysis of the Work
Package One (WP1) and Work Package 2 (WP2) of cutting inserts supplied by Sandvik
Coromant and surface topographical analysis occurring at Halmstad University. The impact of
surface topography on the performance in machining not fully understood and this is an
attempt to investigate and gain knowledge on the effect in a specific segment, turning in 316L
with CNMG120408-MM inserts. This work will mainly focus on characterizing the different
surface treatment variants before and after coating deposition. Variants MSG157, MSG158
and MSG 160 are the cutting inserts before coating and MSG186, MSG18, MSG189 and
MSG190 is the cutting inserts after the coating process.
The analysis of reading from the interferometer has different kind of methods. The methods
are:
 Average and Standard Deviation method
 Spearman’s rank correlation coefficient method
 Error bar followed by ANOVA and t-test method
The 3D surface texture parameters used in this thesis computed by MountainsMap 7software
from Digital Surf. 3D Roughness parameters defined by the following standards: ISO 25178-
2 define 30 parameters, the selected parameter. This section of results considered to single out
the surface topographical analysis of coated and uncoated cutting inserts. 3D surface texture
parameter and image analysis obtained from the equipment’s interferometer (readings with
10X and 50X magnifications) and SEM.
2.1 Alternative methods
2.1.1 Average and Standard Deviation Method
The average and standard deviation method analyses the variation of each parameter based on
the standard deviation and confidence intervals [10]. This method explained by using the
readings from the interferometer. The method summarized in the following steps:
 For each parameter s'i = ( s'i . . . s1n
i of class G and s′′
i =(s′′
i …s′′n
i ) of
class B, the average B, the average µ and the standard deviation σ is
calculated
𝜇′
𝑖 =
1
𝑛
∑ 𝑠′ 𝑘
𝑖
𝑛
𝑘=1
(1)
𝜇′′
𝑖 =
1
𝑛
∑ 𝑠′′ 𝑘
𝑖
𝑛
𝑘=1
(
(2)
𝜎′
𝑖 = √𝑣𝑎𝑟(𝑠′ 𝑖)
(
(3)
𝜎′′
𝑖 = √𝑣𝑎𝑟(𝑠′′ 𝑖).
(
(4)

METHOD
7
 For each parameter, an interval for good parts and for bad parts is calculated
with the coverage factor K,
𝐼′
𝑖 = 𝜇′
𝑖 ∓ 𝑘𝜎′
𝑖
(
(5)
𝐼′′
𝑖 = 𝜇′′
𝑖 ∓ 𝑘𝜎′
′𝑖 (6)
 If the intervals 𝐼′
and 𝐼′′
for a parameter Si are disjunctive, this parameter can
be used for thresholding and the significance Si of this parameter can be
computed
The parameter with the highest significance value is that which can be used for classification.
To find the most significant surface texture parameter, the significance values must be
comparable. This could achieve by normalizing them with the average values. The
significance S; is computed on the basis of the intervals and the means
𝑆 =
𝑑(𝐼′
𝑖, 𝐼′′
𝑖)
1
2
(𝜇′ 𝑖 + 𝜇′′ 𝑖)
(
(7)
 Check the ‘+’ significant value (disjunct entry-level) parameter. These non-
overlapping intervals of the parameters indicate highly significant for the
study. Select the parameters highly significant, analysis the parameter with
surface characteristics.
2.1.2 Spearman’s rank order correlation method
Spearman’s correlation coefficient is a statistical measure of the strength of a monotonic
relationship between paired data see figure 2.1, is denoted by
𝑟𝑠 − 1 ≤ 𝑟 ≤ 1
A monotonic function is one that either never increases or never decreases as its independent
variable increases. The following graphs illustrate monotonic functions: [13]-[14]
𝑃 = 𝑟𝑠 = 1 −
6 ∑ 𝑑𝑖
2
𝑁3 − ∑ 𝑑𝑖
2
𝑁
(8)
Where: P= Spearman rank correlation, di= the difference between the ranks of corresponding
values Xi and Yi, n= number of value in each data set
The formula to use when there are tied ranks is
P=
∑ (𝑋 𝑖𝑖 −𝑋)̅̅̅̅(𝑌 𝑖− 𝑌)̅̅̅
√∑ (𝑋 𝑖𝑖 −𝑋)̅̅̅̅2(𝑌𝑖− 𝑌)̅̅̅2
(
(9)
Where i = paired score.

METHOD
8
Fig 2.1 monotonically increasing monotonically decreasing not monotonic
If the correlation coefficient, 𝑟𝑠 , is positive, then an increase in X would result in an increase
in Y, however if r was negative, an increase in X would result in a decrease in Y. Larger
correlation coefficients, such as 0.8 would suggest a stronger relationship between the
variables, whilst figures like 0.3 would suggest weaker ones.
Correlation is an effect size and so we can verbally describe the strength of the correlation
using the following guide for the absolute value of 𝑟𝑠
 00 -0,19 Very weak
 0, 20-0,39 Weak
 0, 40 -0, 69 Moderate
 0, 70-0,89 strong
 0.90 1, 0 very strong
However, the correlation coefficient does not imply can satisfy that is it may show that two
variables which strongly correlated; however, it does not mean that they are responsible for
each other see figure 2.2.
Significance of Spearman's Rank Correlation Coefficient
Figure 2.2: The significance f the spearmen’s rank correlation coefficients and degree of freedom
http://geographyfieldwork.com/SpearmansRankSignificance.htm
2.1.3 Standard deviation error bar followed by Anova and T-test
Standard Deviation (SD) is the measure of spread of the numbers in a set of data from its
mean value. It has also called as SD and represented using the symbol σ (sigma). This can

METHOD
9
also be as a measure of variability or volatility in the given set of data (n). A low standard
deviation indicates that the data points tend to be very close to the mean, whereas high
standard deviation indicates that the data which spread out over a large range of values.
𝜎 = √
∑ (𝑋 − 𝜇)2𝑛
𝑖=1
𝑁
(
(10)
Error bars used on graphs to indicate the error, or uncertainty in a reported measurement.
Error bars often indicate one standard deviation of uncertainty, but may also indicate the
standard error. These quantities are not the same and so the measure selected should state
explicitly in the graph or supporting text. Error bars used to compare visually two quantities if
various other conditions hold. This can determine whether differences are statistically
significant. Error bars can also show how good a statistical fit the data has to a given function.
Standard error of the mean: The standard error of the mean (SE of the mean) estimates the
variability between Sample means that you would obtain if you took multiple Samples from
the same population [48]. The standard error of the mean estimates the variability between
Samples whereas the standard deviation measures the variability within a single Sample
σ 𝑀 =
𝜎
√𝑁
(
(11)
Where σ is the standard deviation of the original distribution and N is the Sample size. The
formula shows that the larger the Sample size, the smaller the standard error of the mean.
Confidence interval error bars: Error bars that show the 95% confidence interval (CI) is
wider than SE error bars. It does not help to observe that two 95% CI error bars overlap, as
the difference between the two means may or may not be statistically significant. Useful rule
of thumb: If two 95% CI error bars do not overlap, and the Sample sizes are nearly equal, the
difference is statistically significant with a P value much less than 0.05 [48].
Posttest following one-way ANOVA (Analysis of variance) it accounts for multiple
comparisons, so the yield higher P values than t -tests comparing just two groups. Therefore,
the same rules apply. If two SE error bars overlap, you can be sure that a posttest comparing
those two groups will find no statistical significance. However, if two SE error bars do not
overlap, you cannot tell whether a post-test will, or will not, find a statistically significant
difference
The T-test: T-test used to determine whether the mean of a population significantly differs
from a specific value (called the hypothesized mean) or from the mean of another population.
This analysis is appropriate whenever you want to compare the means of two groups, and
especially appropriate as the analysis for the posttest-only two-group randomized
experimental design. The formula for the t-test is a ratio. The top part of the ratio is just the
difference between the two means or averages. The bottom part is a measure of the variability
or dispersion of the scores [46]
t − value:
Signal
𝑁𝑜𝑖𝑠𝑒
=
𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑔𝑟𝑜𝑢𝑝 𝑚𝑒𝑎𝑛𝑠
𝑣𝑎𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑔𝑟𝑜𝑢𝑝
=
𝑋 𝑇̅̅̅̅−𝑋 𝑐̅̅̅̅
𝑆𝐸(𝑋 𝑇̅̅̅̅−𝑋 𝑐̅̅̅̅)
((12)
On the other hand, alternate formula for paired sample t-test is:
t =
∑ 𝑑
√ 𝑛(∑ 𝑑2) − (∑ 𝑑) 2
𝑛 − 1
(
(13)

METHOD
10
Figure.2.3: Flow chart, which explained the Error Bar, followed by ANOVA and t-test applied on WP 1 and WP 2 (Readings:
obtained from interferometer (50 X magnification) and MountainsMap software).
• V: Number of Variants
• NEBNO: Number of error Bar Not Overlapping
• Si: Significant Values in ANOVA test
• TRUE: Parameters are disjunctive for variants with 95% confident interval

METHOD
11
The procedure followed for this study explained in the above flow chart in Fig.2.3.
First, find all the mean and standard deviation of each variant by using the readings from the
interferometer. Draw the mean graph for each variants and apply the custom Error Bars
(Analysis on Microsoft excel 2010). For WP 1 check the condition NEBNO=V, then reject
the parameter otherwise select. WP2 shows all the error bars are overlapping, and then go to
the ANOVA test followed by t-distribution test.
Analysis of variance:
 Find the sum of parameters for each variant
 Find the mean(average) for each variant
 Find the difference between the observation and the mean (X-mean)
 Find the variance (X-mean)2
Sum of the square
 Find the total sum of the observation of the variants
 Find the total sum of the square between group and the sum within the group
 Find the degree of freedom between the group as well as with the group
 Divide the sum of squares between groups by the degree of freedom between groups
MSw, divide the sum of squares within groups by degree of freedom within groups
MSB
 Find F statistic ratio equal = MSw/ MSB
 F > (F Critical) and P value less than 0.05 (p < 0.05) with (95% confidence), and
degree of freedom between group <F < degree of freedom within group, means
variants interval are “disjunct” for particular parameter (TRUE).
2.2. Chosen methodology for this project
The different methods within the area evaluated accordance to the requirements and the goals
of the project. For analyzing work package one (WP 1), by using the method mean and
standard deviation method, Error Bar analysis and Spearman’s rank Correlations method are
used for select the relevant parameters. Error Bar followed by ANOVA and T-test,
Spearman’s correlation method used for analyzing the work package two (WP 2).
2.3. Preparations and data collection
 Appropriate literature study, articles, international journal and other study of similar
study.
 Collect the cutting insert (CNMG120408-MM) of work package 1 and work package
from Sandvik Coromant.
 Clean (Ultrasonic sterilizations) the surfaces of cutting inserts and take the
measurement by using interferometer and scanning electron microscope (SEM). Then
import the measurement to digital surf mountain software and analyze these readings
by different statistical method (ANOVA, T-test, Spearman’s rank correlation, F-test
etc. and software’s (IBM SPSS, MATLAB etc.).
 Plan for weekly meeting with Sandvik Coromant and data collected from experts from
Sandvik Coromant as well as Halmstad University.

THEORY
12
3. THEORY
The authors started with a literature research regarding the task topography and how
simulated surface topography being measured, the authors make a deep investigation relates
to the surface integrity. Surface texture and 3D surface texture parameter. Select the
appropriate parameters to analyses the surfaces and the literature research including books,
and other relevant documentation regarding measuring of surface structure and their analysis
Surface Texture characterization and evaluation related to machining.
3.1. Summary of the literature study and state-of-the-art
Surface integrity is an important consideration in manufacturing operations, because it
influences such properties as fatigue strength, resistance to corrosion, and services life,
which- strongly influenced, by the nature of the surface produced. Surface integrity achieved
by the selection and control of manufacturing processes, estimating their effects on the
significant engineering properties of work materials, such as fatigue performance.
Surface integrity is a measure of the quality of a machined surface that describes the actual
structure of both surface and subsurface. Severe failures produced by fatigue, creep and stress
corrosion cracking start at the surface of components. Therefore, in machining any
component, it is necessary to satisfy the surface integrity requirements. Micro hardness, micro
crack, surface roughness, and metallurgical structure are features that used to determine the
surface integrity as shown in Figure3.
.
Schematic section through a machined surface [15]
Therefore, in machining any component, it is necessary to satisfy the surface integrity
requirements. This study based on the idea of Surface integrity loop (figure 3.2) where
focusing on the post coated and pre coated surfaces. The loop introduced to highlight the
connection between function, manufacturing, and characterization of the surfaces. Function
gives an idea about impression of products, tribological properties [16]. Manufacturing
methodology influence the surface layer of inserts which have influence on practical
properties [17]. Characterization of the surface integrity stands for types of measurement
takes and analysis occurred.

THEORY
13
Figure.3.2: “The surface integrity loop explained the relationship between function, manufacturing, measurement
and characterization of surface” [18]
The surface control loop can explain the complexity of surface design, the three facets
manufacturing, Characteristics and Functions. The characterization and measurement of
surface is very complex because the character of a machined surface involves three dimension
of space, any numerical assessment of a surface finish will be influenced by the direction in
which measurements are taken in relation to the lay and arbitrary distinguish between
roughness and waviness.
The engineering surface achieves, after the relevant process, new properties and
characteristics compared to the initial one that constitute what we call surface integrity.
Surface integrity can be express by Surface character, which the integrity can be judged by
four main elements [8]
1. Topography and texture, which describes the geometric characteristics
2. Chemical properties such as reactivity at the surface
3. Metallography such as structure, orientation and grain size
4. Mechanics, describing states of stress at the surface
The quantitative 3D surface description and analysis gives an effective understanding of
phenomena. The detailed analysis of loop leads to the solution of WP 1 & WP2. The
directional properties affect the tribological function of the surface (frictional behavior, wear,
lubricant retention, etc.) also the state of anisotropy can change during function. The surface
integrity loop consists of three sections (Functions, Manufacturing and Characterization) is
explained below.
3.1.1 Function
Surface Integrity Issues on Coated Cemented Carbides
Successful functionality of a hard coating system depends not only on composition,
microstructure and architecture of the layer itself [19-20], but also on the surface integrity of
the supporting substrate as well as on the interface nature and strength. On the other hand,
only a few investigations address the influence of surface topography or subsurface integrity
resulting from changes induced at different manufacturing stages, particularly regarding those
implemented prior to coating deposition, i.e., grinding, lapping, polishing, blasting and
peening [21]-[22].
A cutting insert must have the following properties in order to produce economical and good
quality parts:
Function
Manufacturing Characterization

THEORY
14
 Hardness – The strength and hardness of inserts must maintain at elevated temperature
(hot hardness).
 Toughness– to resistance chip, fracture and crack during the manufacturing and
cutting operations.
 Wear resistance – to attain acceptable tool life.
 Corrosion resistance – to withstand from chemical reactions.
 Heat treatment capacity – to maintain the dimension stability while applying the heat
treatment.
T series (Tungsten type) cutting inserts are one of the commonly used in cutting inserts.
Titanium nitride is deposited on the tool does not affect the hardness (heat treatment) of the
tool being coated but it can extend the life or to allow the higher speed operations. The
hardness, tool life and high-speed operations of cemented tungsten carbide are greater than
other tool materials. In order to get better strength cobalt (Co) added as a binding agent to
Tungsten carbide (WC). The most commonly used coating materials are:
 Titanium Carbo- Nitride Ti(C,N)
 Ceramic coating
 Titanium Nitride
Titanium carbo-nitride black color coating, Titanium carbo nitride is commonly used
intermediate layer of multilayered coating. The duty of Ti (C, N) maintains the strong bond
between the other coating layer and cutting inserts. The Ceramic coating (Aluminum oxide)
is the one of the mainly used ceramic coating because of its higher hardness and brittleness,
less chances for producing scaly cut and hard spot in the work piece. Because of outstanding
resistance to abrasive wear, heat and chemical reaction of ceramic coating provide higher
cutting speed. The main disadvantage of ceramic coating is it subjected to failure by chipping.
The main advantages of Titanium nitride coating are resistance to cratering, abrasive wear
resistance, and high heat resistance at high cutting speed (cutting interface with less friction-
produce a smooth surface of the coating).
The condition of cutting inserts determined by the following factors [23]
 Microstructure – to maintain uniform crystal or grain structure, it is normally
recommended but is any variation in microstructure affects the machinability.
 Grain size- – Small and undistorted grains are more ductile and gummy. Hardness
of the material generally correlated with grain size. Large grain size is generally
associated with low strength, low ductility, and low hardness.
 Heat treatment – a material may be treated with cooling and heating leads to
reduce brittleness, remove stress, obtain ductility and toughness, to increase the
strength and to obtain definite microstructure.
Lay means for any predominant directionality of the surface texture of the cutting insert
surfaces. Usually the production method and geometry are determining the directionality
(lay). Surfaces produced having no characteristic directions are peening and grit blasting
(sometimes it has non-directional or protuberant lay). A smooth surface looks like more rough

THEORY
15
if it has strong lay and the rough surface looks like the more uniform weather it has no lay
[24].
3.1.2 Manufacturing
Abrasive slurry blasting is the type of wet abrasive slurry blasting of cutting insert coating
process. Fracture strength, hardness, the presence of impurities, density, type, and shape
(depends on the erosion and lubrication Properties-Void parameters) and size of abrasive
media has key roles in material selection of blasting process. The major problem related to
shot blasting related to method of process, defect of original materials and improper control of
parameters (stress temperature and surface deformations). The coating surfaces also depend
on the selection and matching of abrasive, nozzle, air pressure and abrasive/air mixing ratio
[25]-[26]. More Detail about the treatment, tool geometry and wear see appendix.7.
Chemical vapor deposition (CVD) is the generally used coating process in which coating
material introduced in the environmentally controlled chamber as a chemical vapor. Another
commonly used coating process is the Physical vapor deposition (PVD). The normal
thickness of CVD coating is 2µm to 15µm. Because of the high temperature 1000 ℃ using in
the CVD operations have high bonding between the tungsten carbide cutting inserts and
coating materials. The highest bonding leads to increase in toughness results in minimal
chipping and good surface finish [27].
The experienced polishers prepare coating by high-speed hand held rotary tools, abrasive
brushes and self-prepared carriers used for producing the smooth coated surfaces. Robot
assisted multi axis equipment’s are the ongoing development to achieve the effective surface
finish. Even though using different types of finishing process, the fine grain process is the
mandatory for producing smooth surfaces. This is the kind surface flow treatment in which
little hard rough particles are leads to small grooves and pits leads to the one directional
scratch. Now a days polishing treated as wear process in which abrasion, erosion, adhesion
and surface fatigue are normally occurred defects [28]. The grooves occurring on the surface
is mainly depends on the abrasive grain shapes of polishing. The angular shaped abrasive has
a higher wear rate with narrower and sharper grooves than the round edge shaped. Abrasive
rolling behavior (high load with low abrasive density) also effect on the groove formations
[29].
3.1.3 Characterization
The characterization of this study explained by following areas:
a. Region of interest:
All treatments had done on the rake face of the inserts; a worn edge of an insert as shown in
fig 3.3 and figure 3.4 below.

THEORY
16
’
Figure.3.3” The region of interest in rake face”.
Figure.3.4: “LOM image of worn edge of insert in region of interest”
b. Measurement Instrument:
In this thesis, there are two types of instruments used: optical interferometer and Scanning
Electron microscope (SEM).
Interferometer:
The MICROXAM 100 HR with objective of 10X and 50X magnification her were used
giving a measuring area of 0.8*0.6mm and 162*123μm. Interferometer is an instrument
taking the pictures with good accuracy and resolution. This is an optical technique providing
quantitative 3D data up to nanometer level. Interferometer meant dimensional metrology
rather than surface metrology. 5 X magnifications are overlapped the surfaces on rake face
[1]-[37]. The optical profilometer is an instrument that uses the interference patterns of light
to scan through a range of heights and create a three-dimensional profile of a desired surface
without physically touching it.
Scanning Electron Microscope (SEM)
A SEM of type JEOL JSM-6490LV used for taking images where produced by the secondary
electron detector and electron magnets with maximum of 5nm lateral resolution. Higher
resolution and large depth of field are the advantages of SEM [30]. SEM is intensively used
characterize surface topography and cross-sectional structure, as well as fractography of the
(coated) hard metals. SEM permits the observation of a variety of materials from micrometer
to nanometer scale. SEM capabilities variants extend from high resolution topographic
imaging to both qualitative and quantitative chemical analysis, the types of signals collected
from the interaction of the electron beam and the Sample surface include secondary electrons,
backscattered electrons, characteristic x-rays, and other photons of various energies, coming
from specific emission Sample volume [31].

THEORY
17
Figure 3.5 A SEM instrument of type JEOLJSM-6490LV
The table below explained about the summary of used instruments to measure the surfaces in
which mentioned about the magnifications, merit & demerits and comments of the equipment
Instrumentation Magnification Merits/Demerits Comments
Profilometric
3-D
measurement
Optical no contact
instrument:
Scanning
differential
interferometry
50 X and 10 X
magnification;
resolution in
micrometer
Measure small
area, easy to tune
the fringes
5 X
magnification
overlap the
edges
Scanning Electron
Microscope(SEM)
1KX,5KX & 10KX
magnification;
resolution in
micrometer
Better results; take
time for scanning
and operating
No need of
any
optimization
technique to
analysis
Table 3.1: Summary of used instruments for measurements [32]
c. Software used:
The software used for 3 D Surface texture parameters, profile and image analysis of SEM
pictures was the Digital surf MountainsMap 7 surface imaging and metrology [33] For
selecting the appropriate parameters of the surface having usage of several methods including
IBM SPSS, MATLAB and Microsoft excel. MountainsMap software is surface imaging and
metrology software published by the company Digital Surf. Its main application is micro-
topography, the science of studying surface texture and form in 3D at the microscopic scale.

THEORY
18
The software used mainly with stylus-based or optical Profilometer, optical microscopes and
scanning probe microscopes (SEM’s) and Raman and FT-IR spectrometers. These new
solutions added to an enhanced range of existing imaging and metrology software solutions
for areal 3D optical microscopes, scanning probe microscopes, 3D and 2D surface
Profilometer, and form measuring systems.
In this thesis used MountainsMap software Version 7 which introduces new imaging and
metrology solutions for scanning electron microscopes. All functions organized in groups and
sub-groups that clearly labeled. Groups and sub-groups associate related studies, operators
and editing tools.
d. Measuring Procedure and Analytical techniques
All the measurement (Reading) was precondition according to the software installation as
following:
 First step the inserts carried out by ultrasonic sterilization and then dried by using hair
dryer.
 The insets placed at the interferometer table and then take reading of 10 X and 50X
magnification see appendix 6, 20 readings taken for each inserts.
 The analysis computed by Mountains Map 7software.
 In MountainsMap7 load the reading
 Fill the non-measured points.
 Further, a form removal for 3D profiles by fitting a 2nd
degree polynomial to measured
data carried out.
 Filtering using cutoff wavelengths of 80 micrometers and the robust Gaussian filter
see appendix 2. The measurement located on the rake face of the cutting inserts
toward both co-linear direction of nose radius from the nose [34].
e. Featured characterization:
Surface texture parameter, which is the profile parameter and the real field parameters, use a
statistical basis to characterize the cloud of measurement points.
Profile parameter in particular were developed primarily to monitor the production process, as
assessment we do not usually see field parameter values but pattern of features such as hills
and valleys, and the relationship between them. By detecting and the relationships between
them, it can characterize the pattern in surface texture, parameter that characterizes surface
features and their relationships are termed feature parameters [35].
ISO 25178: Geometric Product Specifications (GPS) – Surface texture: areal is an
International Organization for Standardization collection of international standards relating to
the analysis of 3D areal surface texture [8]. Particularly in the academic field, there is a
growing number of works, which advocate the usage of three-dimensional measuring
elements. The search of a higher precision and resolution in measures, reduction in costs of
processing and storing systems and continuous progress in microscopy techniques are the
reasons of the emergence of these works.

THEORY
19
3D roughness parameters are defined by the following Standards: ISO 25178 define 30
parameters (appendix 1), EUR 15178N also define 30 parameters but some are identical to
those of ISO 25178. Only 16 parameters are the latest ones, however Sz (maximum height of
surface roughness) and Std (texture direction) are calculated differently in both standards [36]

RESULTS
20
4. RESULTS
Measurements with 10 X respectively 50 X magnification used, 20 different measurements
performed with each magnification on every sample. The data was collected and analysis
performed by MountainsMap to evaluate the surfaces more closely. The results had a few
unmeasured points, which easily solved in the software. The Same filter and operations later
performed for the other Samples this can followed in appendix 3. The analysis of reading
from the interferometer has different kind of methods.
The methods used in this thesis, Average and Standard Deviation method, Error bar followed
by ANOVA and t-test method, Spearman’s correlation matrix method. The standard ISO
25178 used for selecting the parameters from MountainsMap Software. This section of results
considered to single out the surface topographical analysis of coated and uncoated cutting
inserts. 3D surface texture parameter and image analysis obtained from the equipment’s
interferometer and SEM.
4.1 Presentation of experimental results of work package 1
4.1.1 Parameters Selection Methods
The parameter selected by using the methods, which explained in the methodology. The
methods are used for the optimizing the parameters of variants MSG157, MSG158 and MSG
160.
4.1.2 Average and Standard Deviation method
Parameters - According
To ISO 25178
Comparison between MSG157 and MSG 158
MSG157 MSG158
Mean SD Imax Imin Mean SD' I´max I´min
Smc (p = 10 %) 0,39 0,01 0,42 0,36 0,52 0,04 0,59 0,44
Vv (p = 10 %) 0,40 0,02 0,43 0,37 0,54 0,04 0,62 0,46
Vmc (p = 10 %, q = 80
%)
0,27 0,01 0,29 0,24 0,34 0,02 0,38 0,31
Vvc (p = 10 %, q = 80
%)
0,35 0,01 0,38 0,32 0,47 0,03 0,53 0,41
SD&SD': Standard deviation of MSG157 and MSG158 respectively
Table 4.1: shows the mean, standard deviation and I value for MSG157 and MSG158
A zoom in the comparison in table 4.1, highlights on the selected parameter . The variation of
each parameter based on the standard deviation, mean and confidence intervals. Where the
interval 𝐼′
and 𝐼′′
for the factor Si are disjunctive.
The mean or average calculated from the equation (1) and (2), as well as the variance from the
equations (3) and (4). The interval for good parts and for bad parts calculated from the
equations (5) and (6) with the coverage factor K (k=2). Then the significant factor computed
in equation (7).

RESULTS
21
Si between MSG157 and MSG158
to ISO 25178
Description of
Selected Parameter
Significant
Factor
Significant factor is '+'
and disjunct interval
Smc (p = 10%) Inverse areal material
ratio
0,054 Accepted
Vv (p = 10%) Void volume 0,049 Accepted
Vmc (p = 10%, q=80%) Core material volume 0,065 Accepted
Vvc (p = 10%, q =80%) Core void volume 0,092 Accepted
Table 4.2: shows the significant factor and accepted conditions for selected parameters
Table 4.2 showing the significance factor Si; is computed on the basis of the intervals and the
mean, the Select parameter have ´+´ve (disjunct) significant factor (Accepted).
Parameters - According to
ISO 25178(157and 160)
MSG157 MSG160
Mean SD Imax Imin Mean2 SD2 I´´max I´´min
Sa 0,25 0,01 0,28 0,23 0,19 0,01 0,22 0,16
Smc (p = 10%) 0,39 0,01 0,42 0,36 0,29 0,02 0,32 0,25
Sxp (p = 50%, q =96.5%) 0,71 0,04 0,79 0,63 0,52 0,04 0,61 0,44
Vv (p = 10%) 0,40 0,02 0,43 0,37 0,30 0,02 0,34 0,26
Vmc (p = 10%, q = 80%) 0,27 0,01 0,29 0,24 0,20 0,01 0,22 0,17
Vvc (p = 10%, q = 80%) 0,35 0,01 0,38 0,32 0,26 0,01 0,29 0,23
Table 4.3: Shows the mean, standard deviation and I value for MSG157 and MSG160
Table 4.3 shows the comparison between MSG 157 and MSG 160 on the selected parameter.
The variation of each parameter based on the standard deviation, mean and confidence
intervals. Where the interval 𝐼′
and 𝐼′′
for the factor Si are disjunctive. The mean or average
calculated from the equation (1) and (2), as well as the variance from the equations (3) and
(4). The interval for good parts and for bad parts calculated from the equations (5) and (6)
with the coverage factor K (k=2). Then the significant factor computed in equation (7)
Parameters According to ISO
25178-2
Description Of Selected
Parameters
Significant
Factor
Accepted/
Rejected
Sa Arithmetic Mean height 0,05 Accepted
Smc (p = 10 %) Inverse areal material ratio 0,1 Accepted
Sxp (p = 50 %, q = 97.5%) Extremepeak height 0,04 Accepted
Vv (p = 10 %) Void Volume 0,1 Accepted
Vmc (p = 10 %, q = 80 %) Core material volume 0,11 Accepted
Vvc (p = 10 %, q = 80 %) Core void volume 0,12 Accepted
Table 4.4 showing the Accepted parameter has ´+´ve (disjunct) significant factor

RESULTS
22
The above table (4.4) shows the selected parameters of the variants MSG157 and MSG 160
from equation (7), the results from equation (7) has ´+´ve (disjunct) significant factor, that
mean select the parameter or accept the parameters which has ´+´ve (disjunct) significant
factor. Table 4.5 and table 4.6 shows the comparison between MSG 158 and MSG 160, the
selected parameters calculated from the equation (1) and (2), as well as the variance from the
equations (3) and (4). The interval for good parts and for bad parts calculated from the
equations (5) and (6) with the coverage factor K (k=2). Then the significant factor computed
in equation (7).
Parameters -
According To ISO
25178
Comparison Between MSG158 and MSG160
MSG158 MSG160
Mean SD I´max I´min Mean2 SD2 I´´max I´´min
Sa 0,33 0,03 0,40 0,27 0,19 0,01 0,22 0,16
Smc (p = 10%) 0,52 0,04 0,59 0,44 0,29 0,02 0,32 0,25
Sxp (p= 50%,q =96.5%) 0,88 0,09 1,07 0,70 0,52 0,04 0,61 0,44
Vv (p = 10%) 0,54 0,04 0,62 0,46 0,30 0,02 0,34 0,26
Vmc(p=10%,q=80%) 0,34 0,02 0,38 0,31 0,20 0,01 0,22 0,17
Vvc(p=10%,q= 80%) 0,47 0,03 0,53 0,41 0,26 0,01 0,29 0,23
Table 4.5: Shows the mean, standard deviation& I value for MSG158 and MSG15
to ISO
25178(MSG157and
MSG160)
Description of selected
parameters
Comparison Between MSG158
and MSG160
Significant
Factor
Accepted/Rejected
Sa Arithemetic Mean Height 0,20 Accepted
Smc (p = 10%) Inverse areal material ratio 0,29 Accepted
Sxp (p = 50%,q =97.5%) Extreme Peak height 0,13 Accepted
Vv (p = 10%) void volume 0,29 Accepted
Vmc (p = 10%, q =80%), Core material volume 0,32 Accepted
Vvc (p = 10%, q = 80%) Core void volume 0,35 Accepted
Table 4.6: Shows the Significant factor and accepted conditions for selected parameter

RESULTS
23
to ISO 25178
Significant factor
between MSG157 and
MSG158
Significant Factor
between MSG158
and MSG160
Significant Factor
between MSG157 and
MSG160
Sa (Arithemetic Mean
Height)
Si Factor ´-´ve Rejected 0,2 0,05
Smc (p = 10%) (Inverse
Areal Material Ratio
0,05 0,29 0,11
Sxp (p=50%,q=96.5%)
Extreme Peak Height
Si Factor ´-´ve Rejected 0,13 0,04
Vv (p = 10%)(Void
Volume)
0,05 0,29 0,1
Vmc (p = 10%, q = 80%
Core Material Volume
0,07 0,32 0,11
Vvc (p = 10%, q = 80%)
Core Void Volume
0,09 0,35 0,12
Table4.7: shows the significant values for selected parameters
The parameters selected from the above table according to significant value with disjunct
interval (‘+’ve value). Sa and Sxp shows ´-´ve Si factor in this case reject the parameters,
while comparing between MSG 157 and MSG158.The selected parameters gives idea about
topographical difference between three variants.
4.1.3 Spearman’s rank correlation method
Spearman’s rank correlation method to select the parameters explained in method section
2.1.2. The selected Parameters as shown in table 4.8, which has highest correlation factor
calculated from the equation (8).
Selected parameters correlations Smc Sq Vm Vv Vmc Sdq
Sxp 0,96
Sa 0,96
Vmp 1
Vmc 0,96
Vvc 0,99 0,99
Sdr 0,99
Table 4.8 the correlation for selected parameters in work package 1
The Parameters Sxp and Smc have very strong correlation (0, 96) means that these parameters
are significant for comparison between the variants. The parameters Sa and Sq shows highly
correlation in which select the Sa because both readings represent the Same sense. Vmp Vm,
Sdr and Sdq show strong correlations. Again, the parameters Vmc and Vv, Vvc and Vv, Vmc
are also showing strong correlation, more details explained in appendix 5.
4.1.4 Standard deviation Error Bar (EB) followed by Anova &T-test method
The error bar method can use as primary analyzing method to optimize the parameters. The
EB method involves calculating the mean, standard deviation (SD) from equation (10) for
each parameter, and 20 readings from interferometer.

RESULTS
24
Table 4.9: Error-Bar method for selecting 3D parameters (Mean and SD).
Tables 4.9 highlight the selected parameters, by using Excel to plot the mean graph for each
parameter then plot the custom error of each variant by using excel sheet as shown down in
Figure 4.1, or by using equation (10), (11) and (12) explained in Method.
Figure 4.1: Custom Error Bars on the different Variants of mean graph for selected parameters
In the above graphs Error Bar (Dark caped lines) with mean graphs of parameters having
disjunctive (Non-overlapped Error bar) can be selected. Standard deviation used to measure
the dispersion of the mean value. The low SD value indicates data are close to the mean,
while large values of SD indicate data has spread out over a wide range. Error bars give an
idea about statically significant parameters in which experimental data are falling far outside
of the range of standard deviation are considered as significant (Example Software Version:
Microsoft ® Excel 2010 in Windows® 7). The parameters Sa, Smc, Sxp, Vv, Vmc and Vvc
Sa
Smc (p =
10%)
Sxp (p =
50%, q =
97.5%)
Vv (p =
10%)
Vmc (p =
10%, q =
80%)
Vvc (p =
10%, q =
80%)
MSG157 0,25 0,39 0,71 0,40 0,27 0,35
MSG158 0,33 0,52 0,88 0,54 0,34 0,47
MSG160 0,19 0,29 0,52 0,30 0,20 0,26
0,00
0,20
0,40
0,60
0,80
1,00
1,20
Mean
Standard Deviation Error Bar chart for WP 1
Parameters -
According to
ISO 25178-2
DescriptionF
or Selected
parameter
Units
Error Bar Method
Mean Standard Deviation
MSG
157
MSG
158
MSG
160
MSG
157
MSG
158
MSG
160
Sa
Arithmetic
mean height
µm 0,25 0,33 0,19 0,01 0,18 0,01
Smc(p=10%)
Inverse areal
material ratio
µm 0,39 0,52 0,29 0,01 0,04 0,02
Sxp(p=50%,
q = 96.5%)
Extreme peak
height
µm 0,71 0,88 0,52 0,04 0,09 0,04
Vv(p= 10%) Void Volume µ3
/µ2
0,4 0,54 0,3 0,02 0,04 0,02
Vmc(= 10%,
q = 80%)
Core material
volume
µ3
/µ2
0,27 0,34 0,2 0,01 0,02 0,01
Vvc(p=10%,
q = 80 %)
Core void
volume
µ3
/µ2
0,35 0,47 0,26 0,01 0,03 0,01

RESULTS
25
are the chosen parameters which have disjoint Error Bar; remaining parameters are explained
in the appendix 1.
.
4.3. Presentation of experimental results of work package 2
4.3 Methods for selecting the parameters
While applying Custom error bar on variants of work package two show that most of the error
bars are overlapping. Then we shift to our study to one-way analysis of variance followed to
t-test. Procedures are:
 Check the Error Bars of different variants are overlapped
 Find the variance and analysis of variance for single factor
 Check the condition that F value >> F critical value; F between the degree of freedom
and p<0, 05, if parameter show this condition means that variants are significantly
varied between each other.
 All these values calculated from excel sheet. F=Mean square of the model/mean
square of the error (large value indicates that not over lapping), P value indicates the
likelihood of observing a value of the F condition statistics as or more extreme.
 Then make the table which showing below in which find the probability value for t-
test in which TRUE means P (T=t) two tail < (0, 05 /5) (condition from t test) which
indicates comparison between the variants are highly significant (95% confident entry-
level). FALSE indicates comparisons between the variants are not significant.
 Selected parameters have highest number of trues (greater than variant number, 5)
 The important comparison between the variants also can find out by using this method
(show in the green highlight) see table 4.10.
PARAMETERS
MSG186and187
MSG186and189
MSG186and190
MSG186and191
MSG187and189
MSG187and190
MSG187and191
MSG189and190
MSG189and191
MSG190and191
Sq F T F F F F F T T F
Ssk T F T F F T T T T F
Sku F F T T F T T T T F
Sp F T F F F F F T T F
Sv F T F F T F F T T F
Sz F T F F T F F T T F
Sa F T T T F T T T T F
Smr T T F F T T F T T F
Smc T T T T F T T T T F
Sxp F T F T F F F T T F
Sal T F T F T T T F F F
Str F T T F F F F T T F
Std F F F F F F F F F F

RESULTS
26
Sdq F T F F T F F T T F
Sdr F T F F T F F T T F
Vm F T F F F F F T T F
Vv T T T T F T T T T F
Vmp F T F F F F F T T F
Vmc T T T T F T T T T F
Vvc T T T T F T T T T F
Vvv F T F F T F F T T F
Spd F F T T F T T T T F
Spc F T T T T F F T T F
F: FALSE T: TRUE
Table 4.10: show the result from ANOVA &t-test (Selected parameters and important comparisons are in green color)
TRUE P(T<=t) two-tail
<(0,05)
Parameter is disjunct for variants with
95%confident interval
FALSE P(T<=t) two-tail
>(0,05)
Parameter is non-disjunct for variants with 95%
confident interval
Table 4.11: show physical meaning of TRUE and FALSE values in Table 11
PARAMETERS (ISO25178,WP2) NumberTRUES
(Row)>6
Accept/ Reject
Sa(Arithemefic Mean Height) 7 Accept
Smc (InverseAreal Material Ratio) 8 Accept
Vv(Void Volume) 8 Accept
Vmc (Core Material Volume) 8 Accept
Vvc(Core Void Volume) 8 Accept
Table4.12: Selected Parameters in which number of TRUES (row)>6
ComparisnBetweenDifferentVariants(WP2) Number of
TRUES(Coulumn)
>15
SignificantI
Not Significant
Comparison between NESG186& 189 18 Significant
Comparison between MSG189& 190 22 Significant
Comparison between MSG189& 191 22 Significant
Table 4.13: Significant comparison in which number of TRUES >15
Table 4.11 and table 4.12 explained the results obtained from the ANOVA followed by the T-
test in which plotted the number of TRUES and FALSE of each parameters with different
types of comparison. Table 4.13 explained about how pick the important parameters to
compare between different variants in which number of trues greater than 6 are selected
(Statistically significant different to compare between different parameters). Here chose
number six is arbitrary, once need more parameters change the limits and pick the more
parameters for comparison. The significant comparison between the variants also find out by
using the Same method that explained in table 4.13 The comparison between the variants
having number of trues greater than 15 selected.

CONCLUSIONS AND FUTURE WORK
27
5. CONCLUSIONS AND FUTURE WORK
5.1 Conclusions
5.1.1 Work Package 1
 Which parameters describing the topography of the variants are important to look at
when comparing the different variants?
 The parameters which are important to look at when comparing the different
variants to each other are arithmetic mean height(Sa), extreme peak height(Smc),
void volume(Vv), Core material volume(Vmc), Core void volume(Vvc) and Area
height difference(Sxp).
 The methods used for selecting the appropriate parameters are Mean and standard
deviation method, Error bar method and Spearman’s correlation method
Table 5.1 described the effect of selected parameters on different variants in work package
one. The comparison of different variants with selected parameters also explained below. The
colour code of the table is based on the visual estimations [47].
Table 5.1: Comparison between different variants with selected parameters (comparison based on the visual estimation,
B: blasting, FGB: fine grain blasting, P: polishing) [47]
SURFACE
TEXTURE ANALYSIS
Comparison only for WP 1
variants
Description for highest
values
Paramete Selected IS025178)
Sa
Arithemetic
Mean
Height
Sxp
(p = 50%),
(q=97.5%)
Smc
(P=10%)
Vv
(p =10%)
Vmc
(p=10%)
(q=80%)
Vvc
(p=10%,
q= 80%)
Units µm µm µm µm³/µm² µm³/µm² µm³/µm²
Smooth <0,20 <0,60 <0,30 < 0,30 <0,02 <0,30
Medium 0,20-0,30 0,6-0,80 0,30-0,40 0,30-0,50 0,20-0,30 0,30-0,40
Rough >0,30 >0.80 >0.50 >0,50 >0,30 >0,40
MSG157
( B)
Higher bearing
of the material
frompeak, More
Texture.
0,25 0,71 0,39 0,40 0,27 0,35
MSG158
(B-FGB)
Higher overall
texture, Higher
Bearing area.
Higher amount
fluid retention.
0,33 0,88 0,52 0,54 0,34 0,47
MSG160
(B.P)
Widespace
texture,
Comparatively
smooth
0,19 0,52 0,29 0,30 0,20 0,26

28
 Arithmetic Mean Height, (Sa)
The arithmetic mean height or Mean surface roughness defined as the arithmetic mean of
the absolute value of the height within Sampling area and which show measure of overall
texture. In the observation MSG158 and MSG157 shows more overall texture (Sa).
MSG160 show more surface finish (less value of Sa) as shown below in figure 5.1
Figure 5.1 Sa parameter with the values for work package 1
 Peak Extreme Height, (Sxp)
Peak extreme height is defined the peak characterized difference between two material
ratio between 2.5% and 50% (ISO25178-3 2011). The peak height characterized upper
part of the surface without taking account of small percentage of peak height. The peak
extreme height is high for MSG157 and MSG158 and low for MSG160.
 Inverse Areal Material Ratio, (Smc)
Inverse material ratio is the just opposite of the material ratio in which evaluates the
height value c corresponding to the material ratio p.
 Void Volume (Vv)
The parameter stands for the surface texture of component, which contact with other
surface. For MSG158, Vv=0,5 µ3/µ2 which means 0,5µm thick film over the measured
area would provide the Same volume fluid needed to fill to the lowest valley
corresponding to the material ratio.
 Core Material Volume (Vmc)
MSG 157,Sa=0,31µm MSG158,Sa=0,34 µm MSG 160,Sa=0,23µm

29
This parameter gives an idea about part of the material, which does not interact with other
surface in contact and not significant for lubrication. Core Material Volume can be
defined as the difference between material volume at mr2=80% and mr1=10%. This
parameter stands for amount of material removed from the peaks of the surface (Figure
4.2). Variants MSG157 and MSG158 have value Vmc=0,3 µ3/µ2 means these variants
have high material is available for load support once the top levels of a surface are worn
away.
 Core Void Volume (Vvc)
The core void volume is the difference in void volume between the mr1=10% (Void
volume corresponding to the peak at 10% of material ratio) and mr2=80% (Void volume
corresponding to the material ratio 80%). For MSG158, Vvc= 0, 5 µ3/µ2 means high
amount of material available for seal engagement (more fluid entrapment). The variants
MSG157 and MSG160 are Same Vvc value (Figure 5.2).
Figure 5.2: Core Parameters for MSG157, MSG158 and MSG160’
In the above figure 5.2, Vmc curve stands for the bearing curve (material beard from the
peaks during the operations) provide the idea about the wearing occurring on the variants
surfaces. MSG158 variants show higher curve values (figure 5.2) indicates higher wear
occurred on that surface.
 How well does the study of surface topography of variants correlate to the
manufacturing process?
 MSG158 (Blasted followed by fine grain blasting) show more texture, MSG160
(blasting followed by the polishing) shows smoother Surface and MSG157
(Blasted) surface characteristics in between MSG158 and MSG160. Materials are
brittle so hardness test does not work for comparing the variants. Machining test
preferred to get exact result see table 5.2
Variants Manufacturing Process pre
treatment
Comments obtained from the
parameter
MSG157 Blasting Higher bearing of the material from peak,
More Texture.
MSG158 Blasting followed by fine grain
blasting
Higher overall texture, Higher Bearing
area. Higher amount fluid retention.
MSG160 Blasting followed by polishing Wide space texture, Comparatively
smooth
Table 5.2 show variants and comments obtained from the parameter
 Is there any predominant direction of the topography of the different variants?
0 20 40 60 80 100 %
µm
0
1
2
3
4
5
6
7
Vmp
Vmc
Vvc
Vvv
10.0 %
80.0 %
0 20 40 60 80 100 %
µm
0
1
2
3
4
5
6
7
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %
0 20 40 60 80 100 %
µm
0
1
2
3
4
5
6
7
8
9
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %

30
Spatial Parameters (Directionality) [9], [28], [2]
Variants (WP 1)
Description for
parameters
Spatial Parameters(IS025178)
Sal(S=0.2) (Auto
correlation length
Str (S=0.2)
(Texture
aspect ratio)
Std
(Reference
angle = 0')
UNIT um Degree
MSG157(Blasting)
Texture as suggesting
highly isotropic
texture, without any
lay. Uniform surfaces
texture in all direction
3,5 0,7 93
MSG158
(Blasting-Fine
Grain Blasting)
Surface has a medium
anisotropic texture,
indicates or the
presence of a
dominating pattern in
certain directions.
3,6 0,4 94
MSG160
(Blasting-
Polishing)
Surface shows a high
amount of
directionality,
Antistrophic
which again points to a
high amount of wear
on the surface
4,3 0,3 33
Table 5.3: Spatial Parameters of variants MSG157, MSG158 and MSG160 (50X magnification)
SEM image Analysis from interferometer.
Figure 5.3: Shows grooves occurred on MSG160 readings (50 X magnifications)
MSG160 extracted area (SEM image analyzed from interferometer by Mountain Map software.)
The spatial parameters Std, Sal and Str are of variants in work package 1 explained in Table
5.1. The descriptions of parameters mentioned below. Figure 5.3, shows the highest grooves
occurring on MSG160 (Extracted area). The SEM image shows that there is no predominant
lay in direction of the three variants but in MSG 160 shows some scratches over the surfaces.

31
 Autocorrelation Length, Sal
The Sal parameter is a quantitative measure of the distance along the surface in which a
texture that is statically different from the original location. MSG 160 shows higher
value, MSG157 and MSG158 are almost same value. It is the horizontal distance of the
Auto Correlation Function (ACF) (tx, ty) which has fastest decay to specified values
“S”. ACF (tx, ty) is the autocorrelation function which is used for studying periodicity
and check the isotropy of a surface. The specified value for smooth surface is taken as
(0,2) (ISO25178-2) for a practical application. Sal is perpendicular to the surface lay for
anisotropic surface.
 Texture Aspects Ratio, Str
Texture aspects ratio, Str is defined as the ratio between rmin and rmax where rmin and rmax
are the minimum and maximum radius on the central lobe of the ACF respectively. The
Str value lies between 0 and 1(0% and 100%). Str is used for evaluating surface texture
isotropy. Str varies in between 0 and 1, with values closer to 1 suggest isotropic features
without any lay and values close to 0 suggest directionality of the surface texture [41].
Experts agree that a Str > 0.5 means a surface has an isotropic texture whereas a value
below 0.3 shows a high amount of directionality. MSG 157 surface has an isotropic
texture while MSG 160 shows a high amount of directionality see figure 5.4a, figure
5.4b and figure 5.5 for more details.
Figure 5.4a show the texture isotropy direction of variants in WP 1 (readings from interferometer)
Figure 5.4b SEM images (Source Sandvik Coromant) for WP1 showing texture directions
0.200
Parameters Value Unit
Isotropy 90.3 %
Periodicity ***** %
Period ***** µm
Directionof period ***** °
0.200
Isotropy 59.1 %
Periodicity ***** %
Period ***** µm
Direction of period ***** °
0.200
Isotropy 84.5 %
Periodicity ***** %
Period ***** µm
Directionof period ***** °
MSG 160MSG 157 MSG 158

32
Figure 5.5 MSG 157 shows isotropy (Str=0,7) MSG 158 showsanisotropy(Str=0,4)
MSG160 shows high amount of directionality (Str=0,3)
 Texture Direction, Std
The texture direction is the angle between 0degree and 180degree of the spectrum,
which derived from the Fourier spectrum. Std parameters showing scratches and
oriented texture direction, which gives idea about the directionality of the variants.
Three variants MSG157, MSG158 and MSG160 show almost same Texture direction
(Std almost equal to 90 degree). Appendix “3”explain Fourier polar spectral graph of
directionality.
 For MSG157, MSG158 show Same Surface texture direction.
 MSG 157 shows larger ratio values i.e. Str 0.5, indicate isotropy or uniform
surface texture in all directions.
 MSG 158 indicates anisotropy or the presence of a dominating pattern in certain
directions.
 MSG 160 Str= 0,3 value shows small value; indicate anisotropy or the presence of
a dominating pattern in certain directions. It shows high amount of directionality.
See appendix 4. The surface shows high amount of directionality.
5.1.2 Work Package 2
 Which parameters are important for comparing the different variants to each other?
 Parameters Sa, Smc, Vv, Vmc and Vvc are selected by using the Error bar
followed by ANOVA and t-test.
 The parameters which are important to look at when comparing the different
variants to each other are arithmetic mean height (Sa) see figure 5.6 for more
explanation extreme peak height (Smc), void volume (Vv), Core material
volume (Vmc) and Core void volume (Vvc) , more about core parameter see
figure 5.7 and figure 5.8.
0 50 100 150 200 µm
µm
0
50
100
µm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 50 100 150 200 µm
µm
0
50
100
µm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

33
Figure 5.6 Sa parameters for work package 2
µm
3.865
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Roughness (Gaussian filter, 80 µm)
µm
7.706
0
1
2
3
4
5
6
7
µm
11.736
0
1
2
3
4
5
6
7
8
9
10
11
µm
5.378
0
1
2
3
4
5
µm
5.005
0
1
2
3
4
MSG186, Sa=0,20 µm medium texture properties MSG187, Sa =0,32 µm higher texture properties
MSG189, Sa =0,37 µm higher texture properties MSG190, Sa =0,17 µm medium texture properties
MSG191, Sa =0,19 µm medium texture properties as MSG 190

34
Figure 5.7: Core Parameters for MSG 186, MSG 187and MSG 189
Figure 5.8: Core Parameters for MSG 190 and MSG 191
 Error bar followed by the ANOVA and T-test and Spearman’s correlation
method can use for selecting the parameters.

Table 5.4 describes the effect of selected parameters on different variants in work package
two. The comparison of different variants with selected parameters also explained below.
Color in the table based on visual estimation
PARAMETERS Selected From ISO 25718-2
Sa Smc (p =
10%)
Vv (p = 10%) Vmc (p
= 10%,
q =
80%)
Vvc (p =
10%, q =
80%)
SURFACE TEXTURE ANALYSIS
Comparison only for WP2 variants
Description for highest values
Units µm µm µm³/µm² µm³/µm² µm³/µm²
Smooth <0,2 <0,25 <0,25 <0,20 <0,20
Medium 0,2-0,35 0,25-0,45 0,25 -0,50 0,2-0,30 0,20-0,35
Rough >0,35 >0,45 >0,50 >0,30 >0,35
Variant Surface
MG186 B-0-B
High bearing of
materials from peak
0,20 0,30 0,32 0,19 0,26
0 20 40 60 80 100 %
µm
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %
0 20 40 60 80 100 %
µm
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %
0 20 40 60 80 100 %
µm
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %
0 20 40 60 80 100 %
µm
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %
0 20 40 60 80 100 %
µm
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Vmp
Vmc Vvc
Vvv
10.0 %
80.0 %

35
MSG187 B-FGB-B
High fluid retention
and scrap entrapment,
Much material beard
away during process,
high bearing area
0,32 0,46 0,47 0,28 0,39
MSG189 B-P-B
High overall texture,
high bearing of
material from peaks,
more fluid retention,
more wetted surface
0,37 0,49 0,52 0,26 0,40
MSG190 B-P-B, P
Surface in good
condition, smooth flat
surfaces
0,17 0,26 0,24 0,15 0,19
MSG191 B-0-B,P
Surface in good
condition, smooth
and flat surfaces
0,19 0,22 0,21 0,15 0,17
B: Blasting; FGB: Fine Grain Blasting; P: Polishing
Table 5.4: Comparison between different variants with selected parameters (The comparison based
On visual estimation) [47]
Highlights at the selected parameters of work package two in table 5.4. Compare between the
different variants, the parameter arithmetic mean height (Sa) means the overall texture of the
surface. Sa is insensitive in differentiating peaks, valleys and the spacing of the various
texture features. The remaining volume parameter (Vv, Vmc and Vvc) indicates the material
beard from the highest peak and entrapped in the valley, fluid retention, wetted surface etc.
The comparison is in the decimal place are not that much stable but we can say the
comparison is important because most of the parameters shows the same result. Out of the
five variants MSG190 and MSG 191 shows more smooth and flat surfaces. The variants
MSG190, MSG189 and MSG186 show area, which has more bearing from the peaks and
more fluid retention.
 If any connection found between the treatment prior to coating and the outcome of the
treatment after coating?
The table 5.5 below shows variants MSG 186, MSG 187, MSG 189, MSG 190 and MSG
191and the manufacturing process, also the comments obtained from the selected parameter
from work package two.

36
Variants
ER
Method
Pre coating
treatment
Post coating
treatment
Comments obtained from the
parameter
MSG 186 Blasting Blasting High bearing of materials from peak
MSG 187 Blasting
Fine grain
blasting Blasting
High fluid retention and scrap
entrapment, Much material beard away
during process, high bearing area
MSG 189 Blasting Polishing Blasting
High overall texture, high bearing of
material from peaks, more fluid
retention, more wetted surface
MSG 190
Blasting Polishing
Blasting,
Polishing
Surface in good condition, smooth flat
surfaces
MSG 191
Blasting
Blasting,
Polishing
Surface in good condition, smooth and
flat surfaces
Table 5.5 shows the comments obtained from the parameter
 MSG 186 shows medium texture as shown in figure 5.5
 MSG189 and MSG187 show higher texture properties out of five variants see
Figure 5.5
 MSG190 and MSG191 show same surface property see figure 5.5
The comparison between the MSG186M-MSG189, MSG189-SG190 and
MSG189-MSG191 are the highly significant comparison
 Is there any different measurement approach needed to evaluate the surface roughness
on variants in Work Package 2 compared to Work Package 1?
 Spearman’s correlation methods followed by ANOVA and T-test and
Spearman’s correlation method are effective to compare roughness between
different variants between the work packages
PHASE 1 PHASE 2 PHASE 3
cc
Flow chart representing a process for variants surface treatment
𝑆 𝑎 = 0,31um
MSG 157
Blasting,
MSG 158
Blasting-Fine Grain
Blasting
Sa=0,34um
𝑆 𝑎 = 0,23um
MSG 160
Blasting-Polishing
𝑆 𝑎 = 0,2um
MSG 186
Blasting-0-Blasting
MSG 187
Blasting-Fine Grain
Blasting-Blasting
Sa=0,32um
MSG 189
Blasting-Polishing-
Blasting
𝑆 𝑎 =0,37um
𝑆 𝑎 = 0,19um
MSG 190
Blasting-Polishing-
Blasting, Polishing
𝑆 𝑎 = 0,17um
MSG 191
Blasting-0-Blasting,
Polishing

37
In the above chart, in phase 1 MSG 160 shows less texture (Sa=0,23um) value compare to
MSG 157 (Sa=0,31um) and MSG 158 (Sa=0,34um).
In phase 2, MSG189 (modification of MSG 160) shows higher texture (Sa=0,37um) while
MSG 186 (modification of MSG 157) shows lower texture (Sa=0,2um). MSG
187(modification of MSG 158) show almost the same surface texture value (Sa=0,32um).
In Phase 3 MSG 191(modification of MSG 186) shows almost the same surface texture
(Sa=0,19um) as MSG 190 (modification of MSG 189), maybe because of both surfaces were
polished. For a proper conclusion before and after machining test, analysis is required.
5.1.3 Recommendation to future activities
 Factors need consideration, to specify the same point during measuring by either the
white interferometer or SEM, it is important to have enough constraints to avoid error.
 Identify the changes in displacement characteristics due to tool wear condition for
worn tool by online tool condition monitoring.
 Further detail study about the manufacturing process, we recommended for fix the
measurement approaches to measure the surface roughness on variants in WP 1 and
WP2.
 Investigate the Same texture properties propagated from WP 1 to WP2 by regression
method
 Without pre-coating polishing process lead to get good result.
 MSG187 showing better surface finish than MSG186 (Post treatment of MSG187
may get good surface finish than MSG191.

CRITICAL REVIEW
38
6. CRITICAL REVIEW
This critical review of thesis based on the self-emphasize, explained following:
6.1 What factors affect the work been done differently
The environmental aspect has not taken into consideration during the study. The choose
equipment’s are used for a long time may be affect the measurement accuracy and drying of
specimen after the cooling using normal procedure. The equipment table should be more
stable because some equipment’s are sensitive to vibrations. Mostly journals and Scientific
articles have been reviewed and a few books. The subject is good new research area; thus, it is
still in the experimental future. Other critical point of view the software is which used for the
study. The interferometer readings and SEM image analysis are obtained from the
interferometer software started with no knowledge within the area has emerged during the
studies. SPSS software, which helps to analyzing the parameters readings and work with
different analytical methods, more reasonable idea about the software helps, is necessary to
maintain reliable data.
6.2 Environmental and sustainable development
Understand the effect of the cutting parameters on surface finish, material removal rate and
energy consumption. The surface roughness influenced by cutting environment and the kind
of tool, in many studies; it was found that the tool type, feed and cutting velocity, influences
the material removal rate. In order to obtain this result our purpose was to investigate the
surface roughness and to evaluate the manufacturing process of the cutting inserts, which
cutting inserts had the better surface finish that will affect the cutting inserts tool life as well
as the lubrication of the process. The lubrication has its role both for the electrical power
consumption of the machining process than for the treatment of the scraps at the end of the
machining Process. By achieving the desired surface quality is of great importance for the
functional behavior of a part, that will lead to a significant design specification, which
influence on the properties such as wear resistance, coefficient of friction, wear rate, etc. The
quality of surface finish is a factor of importance in the evaluation of machine tool
productivity
6.3 Health and Safety
This part is very important and being sensitive due to the responsibility of human life. It is
very important to indicate this part, it is a multidisciplinary field concerned with the health,
Safety of people at work. Workplace hazards also present risks to the health and Safety of
people at work. Machining leads to environmental pollution mainly because of use of cutting
fluids [42, 43]. Fluids often contain chlorine (Cl), sulfur (S), or other extreme-pressure
additives to improve the lubricating performance. These chemicals present health hazards.
Furthermore, the cost of treating the waste liquid is high and the treatment itself is a source of
air pollution.
Skin exposure to cutting fluid can cause various skin diseases [44]. Inhalation of mists or
aerosols, airborne inhalation diseases have been occurring with cutting fluid aerosols exposed
workers for many years. Bennett and Bennett [45] stated that during machining operations,

39
workers could be exposed to cutting fluids by skin contact and inhalation, in response to these
health effects through skin contact or inhalation, Diseases include lipid pneumonia, asthma,
acute airways irritation, chronic bronchitis, hypersensitivity pneumonitis and impaired lung
function [44].
6.4 Economy
The cost of preparing these materials into cutting inserts is relatively high and continuing to
increase, as well as the cost of carbide and other tool material. It is very important to choose
tool inserts wisely. Surface roughness is a widely used index of product quality, performance
and surface life of any machined component is influencing by surface integrity of that
component.
Tool life improvement is essential to reduce the cost of production as much as possible.
6.5 Ethical aspects
The ethical value is one of the most important factors in human being life not only in the field
of science, as a member of this profession; the authors exhibit the highest standards of
honesty and integrity, the authors handled the equipment’s and the data collected carefully.
This considered from a critical point of view since the knowledge of the software and the
equipment is limited.

REFERENCES
40
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TABLE OF CONTENT FOR APPENDICES
43
[44] Thornburg, J., Leith, D. (2000). “Mist Generation During Metal Machining”,
[45] Bennett E.O., Bennett D.L. (1987). “Minimizing Human Exposure to Chemicals in
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TABLE OF CONTENT FOR APPENDICES
APPENDIX 1 Surface Parameter -ISO25178
APPENDIX 2 Template used in MountainsMap 7 software
APPENDIX 3 Fourier Polar Spectrum of Work Package
APPENDIX 4 ANOVA & T-test for Work Package 2
APPENDIX 5 Spearman’s rank correlation for WP 1 and WP2
APPENDIX 6 Interferometer Readings
APPENDIX 7 Insert Geometry and wear

Appendix: 1 Surface Parameter -ISO25178
44
Surface texture Parameters according ISO25178)
Function Parameters unit Name of parameter
Height Parameter ( amplitude) Uits
Sq μm Root mean square height
Ssk _ Skewness
Sku _ Kurtosis
Sp μm Maximum peak height
Sv μm Maximum pit height
Sz μm Maximum height
Sa μm Arithmetical mean height
Functional Parameter (stratified Surfaces)
Smr (c = 1 µm under the highest peak) μm Inverse areal material ratio
Smc (p = 10%) μm Extreme peak height
Sxp (p = 50%, q = 97.5%) μm Areal height difference
Spatial parameters
Sal (s = 0.2) μm Auto-correlation length
Str (s = 0.2) Texture-aspect ratio
Std (Reference angle = 0°) ° Texture direction
Hybrid parameters
Sdq _ Root mean square gradient
Sdr ° Sdr % Developed interfacial area ratio
Spatial parameters
Sal (s = 0.2) μm Auto-correlation length
Str (s = 0.2) Texture-aspect ratio
Std (Reference angle = 0°) ° Texture direction
Function Parameter (Volume)
Vm (p = 10%) μm³/ μm² Material volume
Vv (p = 10%) μm³/ μm² Void volume
Vmp (p = 10%) μm³/ μm² Peak material volume
Vmc (p = 10%, q = 80%) μm³/ μm² Core material volume
Vvc (p = 10%, q = 80%) μm³/ μm² Core void volume
Vvv (p = 80%) μm³/ μm² Pit void volume
Feature Parameter
Spd (pruning = 5%) 1/ μm ² Density of peaks
Spc (pruning = 5%) 1/ μm Arithmetic mean peak curvature
S10z (pruning = 5%) μm Ten point height
S5p (pruning = 5%) μm Five point peak height
S5v (pruning = 5%) μm Five point pit height
Sda (pruning = 5%) μm ² Mean dale area
Sha (pruning = 5%) μm ² Mean hill area
Sdv (pruning = 5%) μm ² Mean dale volume
Shv (pruning = 5%) Mean hill volume
Table 1.2: 3D roughness parameters calculated and analyzed in this study

45
3D roughness parameters defined by the following Standards: ISO 25178 define 30
parameters, EUR 15178N also define 30 parameters but some are identical to those of ISO
25178. Only 16 parameters are the latest ones, however Sz (maximum height of surface
roughness) and Std (texture direction) are calculated differently in both standards.
The 3D roughness parameters (see Table 1) can be classified into the following groups:
a. Height Parameter(Amplitude)
Sq Root
mean square height
Standard deviation of the height distribution or RMS surface roughness
Computes the standard deviation for the amplitudes of the surface (RM
Ssk Skewness Skewness of the height distribution. Third statistical moment, qualifying the
symmetry of the height distribution. A negative Ssk indicates that the surface
is composed with principally one plateau and deep and fine valleys. In this
case, the distribution is sloping to the top. A positive Ssk indicates a surface
with lots of peaks on a plane. The distribution is sloping to the bottom. Due to
the big exponent used; this parameter is very sensitive to the Sampling and to
the noise of the measurement.
Sku Kurtosis Kurtosis of the height distribution. Fourth statistical moment, qualifying the
flatness of the height distribution. Due to the big exponent used, this
parameters very sensitive to the Sampling and to the noise of the measurement
Sp Maxiumu peak
height
Height between the highest peak and the mean plane.
Sv Maximum pit height Depth between the mean plane and the deepest valley.
Sz Maximum height
Height between the highest peak and the deepest valley.The definition of the
(ISO 25178) Sz parameter is different from the definition of the (EUR
15178N) Sz parameter. The value of the (EUR 15178N) Sz parameter is
always smaller than the value of the (ISO 25178) Sz parameter. The (ISO
25178) Sz parameter replaces the (EUR 15178N) St parameter.
Sa
Arithmetical mean
height
Mean surface roughness. is parameter is
deprecated and shall be replaced by Sq in the
future

46
b. Spatial parameter Parameters (ISO 25178) (Surface)
Spatial parameters describe topographic characteristics based upon spectral analysis.
They quantify the lateral information present on the X- and Y-axes of the surface.
Sal Auto-
correlation
length
Horizontal distance of the autocorrelation function (tx, ty) which
has the fastest decay to a specified value s, with 0 < s < 1. The
default value for s in the software is 0.2.This parameter
expresses the content in wavelength of the surface. A high value
indicates that the surface has mainly high wavelengths (low
frequencies).
Str Texture-
aspect
ratio
This is the ratio of the shortest decrease length at 0.2 from the
autocorrelation; on the greatest length. This parameter has a
result between 0 and 1. If the value is near 1, we can Say that the
surface is isotropic, i.e. has the Same characteristics in all
directions. If the value is near 0, the surface is anisotropic, i.e.
has an oriented and/or periodical structure.

47
Std Texture
direction
This parameter calculates the main angle for the texture of the
surface, given by the maximum of the polar spectrum. This
parameter has a meaning if Str is lower than 0.5.
If the surface has a circular texture (turning, Sawing), this
parameter will give a wrong direction near to the tangential of
the circle. In case the surface has two or more main directions,
the Std parameter will give the angle of the main direction.
The angle is given between 0° and 360° counterclockwise, from
a reference angle. The reference angle may be set to another
value than 0°.
Note: The (ISO 25178) Std parameter and the (EUR 15178N)
Std parameter are calculated the Same way, but the angle is
given differently.
Calculation of the Str and Sal Parameters
1. Auto-correlation function of the surface.
b) Thresholding of the Auto-correlation at a
height s (the black spots are above the
threshold).
2.
c) Threshold boundary of the central
threshold portion.
d) Polar coordinates leading to the auto-
correlation lengths in different directions.
c. Functional Parameters (ISO 25178) (Surface)
Functional parameters are calculated from the Abbott-Firestone curve obtained by the
integration of height distribution overall surface.
Hybrid Parameters (ISO 25178) (Surface)

Master Thesis Analyzes Cutting Tool Insert Surface Topography

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