Surface Topographical  Analysis Of Cutting Inserts

MASTER THESIS 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

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 Al

O

: 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

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.

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

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 . . . s

of class G and s

=(s

…s

) of class B, the average B, the average µ and the standard deviation σ is calculated

𝜇

𝑖 = 1

𝑛 ∑

𝑠′

𝑘=1

(1)

𝜇

𝑖 = 1

𝑛 ∑

𝑠′′

𝑘=1

( (2)

𝜎

𝑖 = √𝑣𝑎𝑟(𝑠

𝑖) (

(3)

𝜎

𝑖 = √𝑣𝑎𝑟(𝑠

𝑖). (

(4)

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

𝑟

− 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 = 𝑟

= 1 − 6 ∑ 𝑑

𝑁

− ∑ 𝑑

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_{𝑖 𝑖}−𝑋)̅̅̅̅²(𝑌_𝑖−𝑌)̅̅̅²

(

(9)

Where i = paired score.

METHOD

8

Fig 2.1 monotonically increasing monotonically decreasing

If the correlation coefficient, 𝑟

, 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 𝑟

 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

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)

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

= 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:

=

𝑣𝑎𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑔𝑟𝑜𝑢𝑝

=

̅̅̅̅−𝑋𝑇 ̅̅̅̅)_𝑐

((12) On the other hand, alternate formula for paired sample t-test is:

t = ∑ 𝑑

√𝑛(∑ 𝑑

) − (∑ 𝑑)

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

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)

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 MS

 Find F statistic ratio equal = MSw/ MS

 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. S urface 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.

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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

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].

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 2

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.

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).

21

Si between MSG157 and MSG158 Parameters - According

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)

Comparison between MSG157 and MSG 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)

Comparison between MSG157 and MSG 160 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

Parameters - According 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

23 Parameters - According

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 S

Sq V

Vv V

S

S

0,96

Sa 0,96

V

1

V

0,96

V

0,99 0,99

S

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 µ

/µ

0,4 0,54 0,3 0,02 0,04 0,02 Vmc(= 10%,

q = 80%)

Core material

volume µ

/µ

0,27 0,34 0,2 0,01 0,02 0,01

Vvc(p=10%, q = 80 %)

Core void

volume µ

/µ

0,35 0,47 0,26 0,01 0,03 0,01

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.

PAR AM E T E RS M S G186 an d 187 M S G186an d 189 M S G186an d 190 M S G186an d 191 M S G187an d 189 M S G187an d 190 M S G187an d 191 M S G189an d 190 M S G189an d 191 M S G190an d 191

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