En nghiên cứu tối ưu hóa một số thông số công nghệ và bôi trơn tối thiểu khi phay mặt phẳng hợp kim ti 6al 4v

MINISTRY OF INDUSTRY AND TRADE
HANOI UNIVERISTY OF INDUSTRY

------------------------

NGUYEN VAN CANH

OPTIMIZATION OF CUTTING AND MINIMUM QUANTITY
LUBRICATION PARAMETERS IN FACE MILLING OF TI-6AL-4V

Major: Mechanical Engineering
Code: 9.52.01.03

SUMMARY OF DESERTATION IN ENGINEERING
Hanoi, 2024

This desertation has been completed at:
HANOI UNIVERSITY OF INDUSRY

Scientific supervisors:
1. Assoc. Prof. Dr. Hoang Tien Dung
2. Prof. Dr. Pham Van Hung

Reviewer 1:
Reviewer 2:
Reviewer 3:

The desertation was defended at the Doctoral Evaluating Council at
University level, held at Hanoi University of Industry at …., date…..
20…..

The desertation can be found at:
- The library of Hanoi University of Industry
- Vietnam National Library

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INTRODUCTION
1. Reason for research topic selection
Titanium alloy is an essential material widely utilized in various industries,
particularly in aerospace, space technology, and engine manufacturing,
constituting approximately 84% of the total. Among these alloys, Ti-6Al-4V
alloy accounts for over 50% of the global consumption of titanium alloys [1].
During the machining process of Ti-6Al-4V alloy, significant heat is generated
due to friction between the cutting tool and the workpiece at the cutting
interface, directly affecting machining accuracy, surface quality, and tool
durability. To increase machining speed and reduce heat and cutting force, a
cooling lubricant is commonly employed.
The most prevalent method of lubrication and cooling using a cooling
lubricant is through "flood cooling," where the nozzle directs the coolant into
the cutting zone. However, flood cooling has certain limitations concerning
economic efficiency, environmental impact, and user health. Specifically, the
cost of purchasing and disposing of coolant after use is substantial [2], and the
coolant is non-biodegradable and toxic [3]. Currently, to address the limitations
of flood cooling, Minimum Quantity Lubrication (MQL) has been researched
and implemented. This method allows for minimizing the amount of coolant
lubricant and using environmentally friendly lubricants while not
compromising the operator's health [4]. Therefore, MQL is considered a
suitable lubrication and cooling method for machining processes in line with
the principles of "sustainable machining" and "green manufacturing."
Following the principle of "Economic Development in Harmony with

Environmental Protection" [9], several studies on MQL in machining SKD 11
tool steel [10] or 9XC steel (9CrSi) [11] have been researched and published.
Investigating the flat surface milling of Ti-6Al-4V alloy using the MQL
method will provide conditions for the development of MQL in machining
various materials in Vietnam… Thus, the doctoral student has chosen the
research topic "Optimization of some technological parameters and minimum
lubrication in milling flat surfaces of Ti-6Al-4V alloy" aiming for green and
sustainable manufacturing..
2. Research Objectives
General Objective:

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To optimize certain technological parameters and minimum lubrication in
milling flat surfaces of Ti-6Al-4V alloy.

Specific Objectives:
- Establish and integrate an MQL system for milling flat surfaces of Ti-6Al-

4V alloy.
- Evaluate the influence of technological parameters (cutting speed, feed rate,

depth of cut) on surface roughness, tool wear, and cutting force under dry
machining, minimum lubrication, and flood cooling conditions.

- Optimize cutting and MQL parameters using the SVR-NSGA II-TOPSIS
combination for milling flat surfaces of Ti-6Al-4V alloy.
3. Research Subject, Scope
3.1. Research Subject
Milling flat surfaces of Ti-6Al-4V alloy under minimum lubrication conditions.

3.2. Research Scope:
Investigate the surface quality and machining productivity of the milling

process of Ti-6Al-4V alloy under MQL condition, including Vc=60÷240
m/min; ap=0.1÷0.9 mm; fz=0.02÷0.10 mm/tooth, Q=50÷150 ml/h; P=1÷5 bar.

4. Research Content
(1). Overview of milling titanium alloy Ti-6Al-4V under minimum
lubrication conditions; (2). Study of characteristic parameters during milling
flat surfaces under minimum lubrication conditions;(3). Research on methods,
experimental equipment, and comparative experiments;(4). Experimental
results and optimization of the milling process of Ti-6Al-4V titanium alloy.
5. Research Methodology
Theoretical research: Analyze and predict the effects of milling process
parameters on milling characteristics, thus developing an experimental model
with Ti-6Al-4V alloy.
Experimental research: Establish an experimental model and conduct
experiments. Utilize advanced data processing methods to derive regression
equations and optimize the milling process of Ti-6Al-4V alloy.
6. Scientific and Practical Significance of the Research
6.1. Scientific Significance
- Development of multi-objective optimization algorithms for the milling
process of Ti-6Al-4V alloy based on the application of the SVR-NSGA II -
TOPSIS combination.

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- The research outcomes can serve as valuable reference materials for related
studies in the fields of minimum lubrication, machining of Ti-6Al-4V
alloy, and multi-objective optimization of machining processes.

6.2. Practical Significance
The research results aid engineers in selecting appropriate technological

parameters and minimum lubrication for achieving quality objectives in the
milling process of Ti-6Al-4V alloy based on multi-objective optimization
algorithms.

7. Novel Contributions of the Research
- Establishment and integration of an MQL minimum lubrication system for

the milling of Ti-6Al-4V alloy flat surfaces.
- Investigation and development of regression models regarding the

relationship between machining process parameters (Vc, fz, ap) and
minimum lubrication system technological parameters (P, Q) with criteria
(Ra, Fc, MRR).
- Development of mathematical models and optimization problems for
technological parameters in the milling process of Ti-6Al-4V alloy under
minimum lubrication conditions.
8. Structure of the Thesis.
Apart from the Introduction, Conclusion, and Future Research Directions, the
research content is presented in 4 chapters:
Chapter 1. Overview of milling Ti-6Al-4V alloy under minimum lubrication
conditions.
Chapter 2. Characteristic parameters during milling flat surfaces under
minimum lubrication conditions.
Chapter 3. Establishment of experimental models and survey experiments.
Chapter 4. Optimization of technological parameters in milling flat surfaces of
Ti-6Al-4V alloy under minimum lubrication conditions.
CHAPTER 1: OVERVIEW OF MACHINING TITANIUM ALLOY

TI-6AL-4V UNDER MINIMUM LUBRICATION CONDITIONS
The content of Chapter 1 focuses on related research regarding
(1) Introduction to titanium and common titanium alloys; (2) Applications
of Ti-6Al-4V alloy in various fields; (3) Machinability of Ti-6Al-4V alloy; (4)
Characteristics of the machining process of Ti-6Al-4V alloy; (5) Minimum

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lubrication and its application in machining titanium alloys; (6) Research status
at home and abroad.

From there, the following conclusions are drawn:
Milling flat surfaces of Ti-6Al-4V alloy under minimum lubrication
conditions using cylindrical end mills equipped with carbide inserts is highly
topical, scientifically rigorous, and practical.
CHAPTER 2: CHARACTERISTIC PARAMETERS DURING MILLING
FLAT SURFACES UNDER MINIMUM LUBRICATION CONDITIONS
Some of the contents studied and presented in Chapter 2 include:
2.1. Khái quát về quá trình phay
2.1. Overview of the milling process;
2.2. Kinematics of the milling process;
2.2.1. Cutting forces in milling Ti-6Al-4V alloy;
2.2.1.1. Cutting force models in milling;
2.2.1.2. Factors influencing cutting forces in milling;
2.2.2. Vibration in milling Ti-6Al-4V alloy;
2.2.2.1. Vibration in milling processes;
2.2.2.2. Causes of vibration;
2.2.2.3. Characteristics of vibration in Ti-6Al-4V milling;
2.3. Cutting heat in milling under minimum lubrication conditions;
2.3.1. Heat generation in milling;

High temperatures generated during cutting processes can affect the surface
quality of products, the durability of cutting tools, and their lifespan, as well as
cause product deformation.
2.3.2. Factors affecting cutting heat;
In metal cutting, metal is removed by the cutting edge of the tool, which
cuts the workpiece material. The energy used in deforming the metal is
released, mainly in the form of heat, in the primary and secondary cutting
zones..
2.4. Tool Wear in Machining
During cutting, the workpiece slides against the front face of the tool,
causing significant wear on both the front and back faces of the cutting tool.

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2.5. Characteristics and Surface Quality After Milling
Surface quality not only affects the dimensional accuracy of machined parts
but also influences their properties and performance during use.
2.5.1. Surface Roughness After Milling
Surface roughness reflects the stability of the machined surface. Material
deformation, cutting forces, vibration, and tool wear all impact the surface
roughness of machined parts. Cutting conditions directly affect the surface
roughness of machined parts.
2.5.2. Factors Affecting Surface Roughness
{Vc, fz, ap, P, Q}
2.6. Milling of Ti-6Al-4V Alloy Under MQL Conditions
2.6.1. Characteristics of Milling Ti-6Al-4V Alloy
In common metal machining, about 90% of heat is generated from plastic
deformation. However, the main difference of titanium alloy compared to other
metal alloys is its low thermal conductivity.
2.6.2. Application of Minimum Lubrication in Machining Ti-6Al-4V Alloy

2.6.3. Characteristics of Cutting Tools in Machining Ti-6Al-4V Alloy
2.7. Conclusion of Chapter 2
The research content has summarized the theoretical basis of milling
process characteristics, including:
- Theoretical physics of cutting processes, cutting force theory, vibration,
and cutting heat phenomena, tool wear in milling, and factors influencing these
characteristic parameters.
Researching the characteristics of cutting forces, vibration, chip formation,
surface quality, and tool wear when milling Ti-6Al-4V alloy.
CHAPTER 3: CONSTRUCTION OF EXPERIMENTAL MODELS
AND EXPERIMENTAL INVESTIGATION

In this chapter, the following main contents are carried out:
3.1. Purpose and Requirements of Experimental Research
Purpose: To establish a method, experimental equipment system, and
experiments to evaluate the effectiveness of MQL compared to dry machining
and flood cooling conditions based on criteria for Ra, Fc, and Vb.
Requirements: Convenient MQL lubrication system for fabrication, installation,
and operation during the experimental process.

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The system should have lubrication equipment capable of adjusting the
pressure and flow rate of the lubricant.
Stability of pressure in the minimum lubrication system during the
experimental process.
3.2. Construction and Integration of Experimental Equipment System
3.2.1. Object of Experimental Research
Experimental milling of Ti-6Al-4V alloy under minimum lubrication
conditions using cutting oil.

- Milling method: Flat surface milling.
- Technological parameters of the milling process used in the study include

cutting speed (Vc), feed per tooth (fz), and depth of cut (ap).
- Technological parameters of the minimum lubrication system include air

supply pressure (P) and lubricating oil flow rate (Q).
- Quality criteria surveyed include surface roughness (Ra), cutting force (Fc),

and tool flank wear (Vb).

3.2.2. Experimental Equipment

3.2.2.1. Flow rate adjustment range

The technical specifications of the MQL equipment are presented in Table 3-1.

Table 3-1. Technical Specifications of the Minimum Lubrication System

No. Parameter Value

1 Oil tank volume 3 liters

2 Maximum air supply pressure 8 bar

3 Flow rate adjustment range 0-1000 ml/h

3.2.2.2. Machine and Cutting Tools

- Machine: DMG Mori Seiki DMU50 5-axis machining center.

- Cutting tools: Sandvik cutting tools with TiCN+Al2O3+TiN coating.

3.2.2.3. Measurement Equipment and Tools

Surface roughness measurement: Mitutoyo Surftest JS-210; Cutting force
measurement: Kistler 9139AA; Tool wear measurement: Keyence VHX-7000
optical microscope..

Fig 3- 5. Surftest JS-210; Fig 3- 6. Kistler 9139AA; Fig 3- 7. VHX-7000

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3.3. Evaluation of the Influence of Different Lubrication Environments on
Surface Roughness, Cutting Force, and Tool Wear

To assess the influence of different technological parameters (Vc, fz, ap) on
surface roughness Ra, cutting force Fc, and tool flank wear Vb, the Taguchi
L27 matrix has been chosen for the experimental study.

3.3.1. Experimental Matrix

Table 3-5. Investigated Variables for Tool Wear with Corresponding Levels of Values

Var Unit Description Level 1 Level 2 Level 3

CL - Lubrication Mode Dry MQL Flood

ap mm Depth of Cut 0.1 0.5 0.9

Vc m/min Cutting Speed 60 150 240

fz mm/z Feed per tooth 0.02 0.06 0.10

Kết quả thực nghiệm được tổng hợp trong bảng 3-6.

3.3.2. Tiến hành thực nghiệm
The experiments were conducted following the sequence described in Fig 3-8.

Figure 3- 8. Sequence of Experiments

Table 3-7. Experimental Matrix Evaluating the Influence of Technological Modes on Cutting
Force, Surface Roughness, and Tool Wear under Different Machining Conditions

Varian Response

No Condition Technological Parameters

CL Vc fz ap Fc Ra Vb

- (m/min) (mm/r) (mm) N µm µm

1 Dry 60 0.02 0.1 314.54 1.17 136.65

2 Dry 60 0.06 0.5 198.03 0.87 167.35

3 Dry 60 0.10 0.9 228.77 0.70 127.03

4 Dry 150 0.06 0.1 184.35 0.29 210.67

5 Dry 150 0.10 0.5 226.44 0.48 136.26

6 Dry 150 0.02 0.9 123.26 0.75 152.77

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Varian Response

No Condition Technological Parameters

CL Vc fz ap Fc Ra Vb

- (m/min) (mm/r) (mm) N µm µm

7 Dry 240 0.10 0.1 279.30 0.44 292.54

8 Dry 240 0.02 0.5 159.16 0.67 130.49

9 Dry 240 0.06 0.9 329.89 0.75 182.45

10 MQL 60 0.02 0.1 156.44 0.25 39.98

11 MQL 60 0.06 0.5 209.97 0.41 106.15

12 MQL 60 0.10 0.9 274.45 0.69 79.20

13 MQL 150 0.06 0.1 161.30 0.48 119.70

14 MQL 150 0.10 0.5 208.06 0.62 155.21

15 MQL 150 0.02 0.9 136.00 0.51 108.35

16 MQL 240 0.10 0.1 203.56 0.35 64.73

17 MQL 240 0.02 0.5 116.15 0.39 107.46

18 MQL 240 0.06 0.9 238.79 0.77 95.85

19 FLood 60 0.02 0.1 152.44 0.30 131.16

20 FLood 60 0.06 0.5 201.69 0.70 140.11

21 FLood 60 0.10 0.9 369.46 0.89 184.08

22 FLood 150 0.06 0.1 163.93 0.72 165.60

23 FLood 150 0.10 0.5 237.86 0.73 180.84

24 FLood 150 0.02 0.9 133.34 0.50 107.36

25 FLood 240 0.10 0.1 261.72 0.52 168.03

26 FLood 240 0.02 0.5 139.31 0.43 138.64

27 FLood 240 0.06 0.9 263.24 1.09 107.91

3.3.3. Results and Discussion

3.3.3.1. Influence on Surface Roughness (Ra)

The ANOVA analysis results show that the surface roughness of the
machined parts under minimum lubrication conditions is the smallest; the

surface roughness under dry machining conditions is higher in most

experiments (Figure 3-10).

The surface of the machined parts reveals variations in surface roughness

among different machining conditions, as depicted in Figure 3-11. The
coloration in the image of the workpiece after machining under minimum

lubrication conditions (Figure 3-11b) illustrates uniformity in color, indicating

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more consistent surface heights compared to machining under flood coolant
conditions.

Figure 3-10. Comparison of Surface Roughness Ra under Different Lubrication Conditions

This can be attributed to the mist form of the coolant, which can penetrate
better into the cutting zone. Conversely, the image of the surface of the
machined part under dry machining conditions (Figure 3-11a) shows significant
differences in surface peaks and valleys. This is a common phenomenon in dry
machining, where high cutting temperatures result from inadequate cooling
during cutting operations.

Fig 3-11. Surface images of the wp after machining under different milling conditions.

Figure 3-12 illustrates the Ra value decreasing when transitioning from dry

machining to minimum quantity lubrication (MQL) conditions, and increasing
again when switching to flood coolant mode.

The surface roughness (Ra) values decrease significantly under dry
machining conditions (CL=1, Figure 3-11) as cutting speed (Vc) increases from

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60 m/min to 150 m/min, with a slight further increase as Vc reaches 240
m/min. Conversely, the Ra values remain stable under minimum quantity
lubrication and flood coolant conditions, corresponding to a cutting speed of
150 m/min..

Figure 3- 12. Correlation between input parameters and surface roughness values Ra

Table 3-8. Response table for the standard deviation of surface roughness. Ra

Level CL Vc fz ap
1 0.2100 0.2540 0.1999 0.2232

2 0.1761 0.1450 0.2706 0.2547
0.1712
3 0.2630 0.2502 0.1786

Delta coefficient 0.0869 0.1090 0.0920 0.0835
Ranking 3 1 2 4

3.3.3.2. Impact on cutting force Fc

The analysis results in Figure 3-12 show that Fc is the lowest when

machining under minimum lubrication conditions and the highest under dry

machining conditions in most experiments.

Figure 3-13. Comparison of cutting force Fc values under different lubrication conditions

The influence of Vc on Fc is the highest, followed by CL and fz. The
influence of ap on Fc is negligible.

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Figure 3-14. Interaction plot for cutting force Fc values

The trend of Fc variation (Figure 3-14) under dry cutting, minimum

quantity lubrication, and flood conditions is quite similar, where the cutting

force decreases as Vc increases from level 1 to level 2, and Fc increases again

as Vc increases to level 3.

Table 3-8. Response table for the standard deviation of cutting force Fc.

Level CL Vc fz ap

1 180.29 77.75 149.10 145.41

2 134.35 118.48 157.75 165.35

3 177.53 295.94 185.32 181.40

Delta coefficient 45.93 218.19 36.22 35.99

Ranking 2 1 3 4

3.3.3.3. Impact on flank wear Vb

The analysis results show that flank wear Vb is minimal when machining
under minimum quantity lubrication conditions in most experiments.
Conversely, the flank wear Vb is higher when machining under dry cutting
conditions in most experiments (Figure 3-15).

Figure 3-15. Comparison of flank wear (Vb) under different machining conditions

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The analysis results, as shown in Table 3-10 and Figure 3-16, indicate that

the lubrication condition CL has the most significant impact on Vb, followed

by fz and ap. The trend of Vb variation when machining under minimum

quantity lubrication and flood conditions is similar (Figure 3-16), where Vb

tends to increase as cutting speed (Vc) increases from 60 to 150 m/min, and

decreases when further increasing cutting speed to 240 m/min.

Table 3-10. Response table for the standard deviation of flank wear Vb

Level CL Vc fz ap

1 47.63 27.55 20.13 20.34

2 26.60 26.92 29.29 29.83

3 20.36 40.12 45.17 44.42

Delta 27.26 13.20 25.04 24.08
coefficient

Ranking 1 4 2 3

Figure 3-16. Interaction plot for the flank wear Vb

The Vb increases as Vc increases from 60 to 150 m/min, and continues to
increase as Vc increases to 150 m/min. This is because the cutting heat
concentration is high in the machining zone during dry machining, leading to a
continuous increase in tool wear as the cutting speed increases.
3.5. Conclusion of chapter 3
Based on the experimental results, machining of Ti-6Al-4V alloy reveals that:

- Machining under MQL conditions allows achieving smaller values of Ra,
Fc, and Vb compared to dry machining and flood machining. Meanwhile,
the values of surface roughness (Ra), cutting force (Fc), and tool wear (Vb)
are highest when machining under dry conditions.

- The surface roughness (Ra) value when milling under minimal lubrication
conditions is less than 27% compared to flood machining and less than

31.5% compared to dry machining.

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- The cutting force (Fc) value when milling under minimal lubrication
conditions is less than 12.8% compared to flood machining and less than
16.6% compared to dry machining.

- The tool wear (Vb) value when milling under minimal lubrication
conditions is less than 42.9% compared to flood machining and less than
51% compared to dry machining.

CHAPTER 4: OPTIMIZATION OF SOME PROCESS PARAMETERS
WHEN MILLING FLAT SURFACE OF TI-6AL-4V ALLOY UNDER

MINIMUM LUBRICATION CONDITION

4.1. Objectives and Research Content
4.1.1. Objectives:

The objectives of this chapter are twofold: (1) to quantitatively analyze the
influence of some process parameters and minimum lubrication conditions
when milling flat surface of Ti-6Al-4V alloy, and (2) to optimize the set of
process parameters {Vc, fz, ap} and minimum lubrication parameters {P, Q}
using the combination of SVR-NSGA II-TOPSIS.

4.1.2. Research Content:
- The study investigates the effects of process parameters including cutting

speed Vc, feed rate per tooth (fz), depth of cut (ap), and minimum

lubrication parameters including lubricant flow rate (Q) and source air
pressure (P) on some performance indicators of the milling process for flat
surface, including surface roughness (Ra), cutting force (Fc), and material
removal rate (MRR).
- Multi-objective optimization of the milling process for flat surfaces of Ti-
6Al-4V alloy.
- In this research content, the value of tool wear is not investigated and
optimized because tool wear is a performance indicator that is difficult to
quantify accurately.
4.2. Construction of experimental matrix and organization of experiments
4.2.1. Determination of experimental parameters
In this study, the influence of technological parameters {Vc, fz, ap} and
minimum lubrication {P; Q} on the criteria (Ra, Fc, MRR) will be investigated.
Technological parameters of the milling process

4.2.2. Construction of the experimental matrix
In this study, the Taguchi method was chosen to minimize the number of
experiments while ensuring the reliability of the study. With 5 variables at 3

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levels investigated, the L27 experimental matrix was constructed as shown in
Table 4-2.

Table 4-1. Summary table of investigated variables and their corresponding levels

Var Unit Description Level 1 Level 2 Level 3

P Bar Supply Air Pressure 1 3 5

Q ml/h Oil flow rate 50 100 150

ap Mm Depth of cut 0.1 0.5 0.9

Vc m/min Cutting speed 60 150 240

fz mm/z Feed per tooth 0.02 0.06 0.10

4.2.3. Organization of experiments

The experiments were conducted sequentially according to the experimental
matrix, where each experiment consisted of 2 layers: (1) rough cutting – with
the same cutting conditions and depth of cut for all experiments, to ensure the
flatness of the workpiece in the finishing cut. (2) finishing cutting, which was
performed with cutting conditions corresponding to the sequence number in the
experimental matrix.

4.2.4. Experimental Results

Table 4- 3. Taguchi L27 (3^13) Experimental matrix

No P Q Vc fz ap Ra Fc MRR
Bar ml/h m/min mm/z N mm3/min
mm µm 85.863
156.055 54.60
1 1 50 60 0.02 0.1 0.157 265.870 272.98
142.638 491.36
2 1 50 60 0.02 0.5 0.170 264.657 409.46
453.540 2047.32
3 1 50 60 0.02 0.9 0.164 116.257 3685.17

317.218 1091.90
4 1 100 150 0.06 0.1 0.234 628.896 5459.51
118.840 9827.12
5 1 100 150 0.06 0.5 0.207 141.163 682.44
211.536 3412.19
6 1 100 150 0.06 0.9 0.239 149.629 6141.95
237.397 218.38
7 1 150 240 0.1 0.1 0.481 391.824 1091.90
126.610 1965.42
8 1 150 240 0.1 0.5 0.545 243.248 163.79
818.93
9 1 150 240 0.1 0.9 0.653

10 3 50 150 0.1 0.1 0.350

11 3 50 150 0.1 0.5 0.416

12 3 50 150 0.1 0.9 0.476

13 3 100 240 0.02 0.1 0.184

14 3 100 240 0.02 0.5 0.197

15 3 100 240 0.02 0.9 0.276

16 3 150 60 0.06 0.1 0.212

17 3 150 60 0.06 0.5 0.220

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No P Q Vc fz ap Ra Fc MRR
Bar ml/h m/min mm/z
mm µm N mm3/min

18 3 150 60 0.06 0.9 0.204 379.741 1474.07

19 5 50 240 0.06 0.1 0.247 92.917 655.14

20 5 50 240 0.06 0.5 0.388 100.872 3275.71

21 5 50 240 0.06 0.9 0.763 136.224 5896.27

22 5 100 60 0.1 0.1 0.650 159.149 272.98

23 5 100 60 0.1 0.5 0.695 267.386 1364.88

24 5 100 60 0.1 0.9 0.663 454.039 2456.78

25 5 150 150 0.02 0.1 0.168 101.847 136.49

26 5 150 150 0.02 0.5 0.173 173.709 682.44

27 5 150 150 0.02 0.9 0.179 266.895 1228.39

4.3 Influence of cutting and MQL parameters on Ra, Fc, MRR

4.3.1. Influence of cutting and MQL parameters on surface roughness (Ra)

Experimental data on the influence of cutting conditions and minimum

lubrication on surface roughness were analyzed using MiniTab 19 software.
The results of the ANOVA analysis of surface roughness (Ra) with cutting
process parameters and lubrication process are presented in Table 4-4.

The ANOVA analysis results show that Vc has the greatest influence on Ra
with a weight of approximately 58.2%. Next are the influences of P and ap with
weights of 6.37% and 4.79% respectively. Along with these are the cross-
effects between parameters, notably the cross-effect between Vc and ap,
accounting for 4.88%. The influence of other parameters is negligible.

Table 4-4. ANOVA analysis of surface roughness Ra

Source DF Seq SS Contribution Adj SS Adj MS F- P-
Value Value
Regression 14 0.97425 96.02% 0.974251 0.069589 20.66 0.000
P 1 0.06464 6.37% 0.040683 0.040683 12.08 0.005
Q 1 0.00480 0.47% 0.013180 0.013180 3.91 0.071
Vc 1 0.01991 58.20% 0.084440 0.084440 25.07 0.000
fz 1 0.59057 1.96% 0.000716 0.000716 0.21 0.653
ap 1 0.04864 4.79% 0.003498 0.003498 1.04 0.328
P*P 1 0.05402 5.32% 0.054017 0.054017 16.04 0.002
Q*Q 1 0.00971 0.96% 0.009707 0.009707 2.88 0.115
Vc*Vc 1 0.07304 7.20% 0.073041 0.073041 21.69 0.001
fz*fz 1 0.02531 2.49% 0.025307 0.025307 7.51 0.018
ap*ap 1 0.00142 0.14% 0.001421 0.001421 0.42 0.528
P*ap 1 0.01060 1.04% 0.010603 0.010603 3.15 0.101
Q*ap 1 0.01870 1.84% 0.018696 0.018696 5.55 0.036
Vc*ap 1 0.04949 4.88% 0.049494 0.049494 14.69 0.002
fz*ap 1 0.00340 0.34% 0.003400 0.003400 1.01 0.335

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Source DF Seq SS Contribution Adj SS Adj MS F- P-
Model Summary Value Value

S R-sq R-sq(adj) PRESS R-sq(pred) AICc BIC
91.37% 0.271227 73.27% -12.59 -46.26
0.0580364 96.02%

The ANOVA analysis results indicate that Vc has the most significant
influence on Ra, accounting for approximately 58.2%. This is followed by the
influences of P and ap, with weights of 6.37% and 4.79% respectively.
Additionally, there are cross-effects between parameters, notably the cross-
effect between Vc and ap, which accounts for 4.88%. The influence of other
parameters is negligible.

The influence of cutting speed (Vc): Besides geometric factors, Vc has the
greatest impact on the amount of tool wear when milling Ti-6Al-4V alloy. This
can also be attributed to the significant increase in temperature at the cutting
zone when increasing the cutting speed, leading to increased tool wear and
consequently an increase in surface roughness.

Figure 4-5 shows that Ra decreases as Vc increases from 60 m/min to 150
m/min and continues to increase as Vc increases to 240 m/min. This change is
due to significant tool wear and mechanical deformation, as well as heat at high
temperatures. Additionally, increasing Vc can lead to uneven transmission of
lubricating oil droplets, reducing lubrication efficiency, increasing friction and
cutting heat, thereby increasing the value of Ra.

Hình 4- 5. 95% Confidence Interval Chart of Ra with Vc

The effect of gas supply pressure (P): P plays an important role in the
formation of lubricating oil droplets and has a significant impact on the ability
to deliver oil droplets to the lubrication zone, thereby enhancing lubrication
efficiency, reducing friction, and cutting heat in the machining process overall.

17

Figure 4-6. 95% Confidence Interval Chart of Ra with P

Figure 4-6 shows the trend of Ra decreasing as P increases from 1 to 3 bar

and then increasing again as P continues to rise to 5 bar. This indicates that the

appropriate pressure, sufficient to distribute lubricating oil onto the surface,

significantly affects the value of surface roughness. Too low gas pressure may

result in insufficient dispersion of lubricating oil over the machining area or

insufficient gas pressure to deliver lubricating oil droplets to the cutting zone.

Therefore, the selection of appropriate process parameters and lubrication,

especially cutting speed (Vc), and compressed air pressure (P), plays a crucial
role in machining surface quality.

4.3.2. Influence of cutting and MQL parameters on cutting force (Fc)

The ANOVA results in Table 4-5 indicate that ap has the most significant

impact on Fc, with a contribution level of 51.39%; followed by the influences
of Q, P, and fz, with respective contribution ratios of 12.79%, 5.38%, and
4.01%. Meanwhile, the impact of Vc is negligible.

The effect of the cutting depth (ap) on cutting force (Fc) is illustrated in

Graph 4-7. It can be observed that there is an increasing trend in cutting force

as the cutting depth value increases. This aligns with the theory of cutting

processes, where a larger cutting depth leads to a larger cutting area, resulting

in an increase in the plastic deformation required to remove the larger material

layer, thus requiring a higher cutting force.

Table 4-5. ANOVA analysis of cutting force Fc

Source DF Seq SS Contribution Adj SS Adj MS F- P-
Value Value

Regression 14 458244 96.60% 458244 32731.7 24.35 0.000

P 1 25535 5.38% 156 156.2 0.12 0.739

Q 1 60678 12.79% 31544 31544.5 23.47 0.000

Vc 1 61 0.01% 5839 5838.9 4.34 0.059

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Source DF Seq SS Contribution Adj SS Adj MS F- P-
Value Value
fz 1 19038 4.01% 2173 2173.2 1.62 0.228
ap 0.55 0.471
P*P 1 243792 51.39% 744 743.8 0.47 0.507
Q*Q 26.11 0.000
Vc*Vc 1 627 0.13% 627 627.3 4.31 0.060
fz*fz 1.81 0.203
ap*ap 1 35101 7.40% 35101 35101.2 3.16 0.101
P*ap 15.52 0.002
Q*ap 1 5798 1.22% 5798 5798.1 23.43 0.000
Vc*ap 0.30 0.591
fz*ap 1 2436 0.51% 2436 2435.9 6.07 0.030

S 1 4247 0.90% 4247 4247.2 AICc BIC

36.6625 1 20859 4.40% 20859 20858.6 335.62 301.96

1 31499 6.64% 31499 31499.1

1 410 0.09% 410 409.7

1 8163 1.72% 8163 8162.7

Model Summary

R-sq R-sq(adj) PRESS R-
sq(pred)

96.60% 92.63% 116189 75.51%

Furthermore, due to its high thermal resistance and hardness characteristics,
during the machining process of Ti-6Al-4V alloy, the material hardness
remains even as cutting speed and cutting zone temperature increase, requiring
greater cutting force to separate the material. This is a distinguishing factor
when machining Ti-6Al-4V alloy compared to machining conventional steel
alloys.

The influence of P on cutting force Fc is depicted in Figure 4-7, with a 95%
confidence interval (CI) showing that increasing pressure (P) tends to decrease
cutting force (Fc).

Figure 4-7. 95% Confidence Interval Chart of Fc with ap.

During the face milling process of titanium alloy Ti-6Al-4V, pressure (P)
plays a crucial role in forming oil droplets, aiding in reducing friction between