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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 1 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: 2 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 3 - 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 4 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 5 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 6 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 7 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 8 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 11 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 12 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 13 - 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 14 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 15 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 16 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 18 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
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