Research on Machining Titanium with Advanced Tool

Baiqi cheng

School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University PO Box 71, Bundoora, Victoria 3083, Australia

Abstract

In this paper two FEM models were developed in order to simulate the complex thermomechanical behaviour of titanium alloy turning process. Deformed workmaterial along with generated heats are visualized through node variables and can be plotted against experimental results. For ductile material such as Ti-6Al-4V, material model in dependence of strain, strain rate and temperature has to be implemented during simulation. Several assumptions can be made based on simulated results in terms of temperature and chip formation. It gives physical comprehension of heat generation at the time of turning process. Heat conduction and heat partition at tool-chip interface is observed via FEM analysis, even though it’s hard to measure and record data from experimental test. Mesh and contact constraints govern the FEM simulation by controlling elements and its behaviours during calculations. It’s found that element distortions are easy formed at the beginning of simulation due to interactions and its cooperated element meshing techniques.

Keywords: Turning, Titanium alloy, Polycrystalline diamond (PCD) inserts, Finite element method (FEM), chip formation, temperature

NomenclatureA Initial yield stress (Mpa)

B Hardening modulus (Mpa)

C Strain rate dependency coefficient

D Overall damage variable

D1…D5Coefficients of Johnson-Cook material shear failure initiation criterion

E Young modulus (Gpa)

ν Poisson’s ratio

m Thermal softening coefficient

n Work-hardening exponent

ε pl Von Mises equivalent plastic strain

ε̇ pl Von Mises equivalent plastic strain rate (s-1)

ε̇ o Reference equivalent plastic strain rate (s-1)

Tt

Absolute temperature, respectively, of the workpiece and the cutting tool at the tool-workpiece interface (oC)

Tm Workmaterial melting temperature (oC)

∆ ε Increment of equivalent plastic strain

ε f Equivalent plastic strain at failure

P Hydrostatic pressure (Mpa)

σ Von Mises plastic equivalent stress (Mpa)

Tr Room temperature (oC)

ρ Density (Kg/m3)

CP Specific heat (J/kgoC)

λ Thermal conductivity (W/moC)

α d Expansion coef., (um/m/oC)

β Inelastic heat fraction

α o Flank angle (or clearance angle) (deg)

γo Rake angle (deg)

Introduction

Titanium and its alloys have been initially recognized and manufactured by aerospace industries in aero-engine and airframe manufacture. Through decade’s developments, the applications of titanium alloys have been extended into industrial sectors, which include petroleum refining, chemical and food processing, surgical implantation, nuclear waste storage, automotive and marine applications (Amin, Ismail et al. 2007). One of the most popular titanium alloys is Ti-6AL-4V due to its several inherent properties, such as low thermal conductivity, low elastic modulus, its capability to withstand varying loads at higher temperatures (Kumar, Eakambaram et al. 2014) and its excellent corrosion and fracture resistance(Rao, Dandekar et al. 2011). Even though the titanium alloys found its way to industrial applications, it has been stated as difficult-to-cut materials regarding to machinability. On the one hand, titanium alloys tend to maintain its high strength properties at elevated temperature and also their tendency to form localized shear bands during machining (Amin, Ismail et al. 2007). On the other hand, the main elements present in titanium alloy (Ti and Al) have high chemical affinity with most commercially available cutting tool materials such as high speed steel, cemented carbide, ceramics and ultra-hard; polycrystalline cubic boron nitride (PCBN) and polycrystalline cubic diamond (PCD)(da Silva, Machado et al. 2013). Due to these facts, the tools have short tool life and wear rapidly during machining, and increased manufacturing cost and reduced production rate. Surface integrity is other reason why tool material is critical for the machining of titanium alloys. In such applications require high-level of reliability and resistance to failure, there must be minimum sub-surface defects such as cracks, laps, visible tears of shear

deformation and possible work hardening effect (Su, Liu et al. 2012). To achieve this requirement, a higher tool life and lowest tool wear rate is needed.

The present approach aims at the performance of PCD inserts under turning condition through developed 3D/2D orthogonal models and simulated by Finite Element Method to provide physical comprehension of the formation of chips. Mechanical parameters and thermal properties are taken into consideration during simulations. The obtained results are compared with those outcomes from experimental tests, which is done by SEM and EDX regarding to attrition, adhesion and diffusion of tools; an optimal design for given conditions are validated and possible improvements are concluded.

Literature review

FEM analysis of machining Ti-6Al-4V

Before considering the geometric models of simulated workpiece and tool, material model should be determined. One of the most common materials models or connoted as material constitutive and damage law is Johnson-Cook model. It was developed in mid 1980s a damage model, which takes into account the large strain and strain rate as well as the thermal softening due to the heat generated by the plastic work(Ayed, Germain et al. 2014). The constitutive equation (1) presents the equivalent flow stress as:

σ f=(A+B ∙ε pln) ∙[1+C ∙ ln( ε̇ plε̇o )] ∙[1−( T−T t

Tm−T t)m] (1)

In the equation above, the first term that includes coefficients A, B and n represents the hardening or elasto-plastic term; the second term contains parameter C represents the effect of strain rate as a function of C; the last term presents the softening due to high temperature. The determination of these five parameters requires quasi-static and dynamic tests at different strain rates and temperature, and usually done by compression tests by means of a split-Hopkinson pressure bar(Schulze and Zanger 2011). The obtained parameters are varied due to different configurations of tested bars. Table 1 shows four common used Johnson-Cook material coefficients.

Table 1: Four Johnson-Cook material coefficients of Ti-6Al-4V.

Material coefficients Units Lee and Lin

(a)Lee and Lin

(b)Shivpuri et

al.Meyer and

Kleponis

A Mpa 782.7 724.7 870.0 896.0

B Mpa 498.4 683.1 990.0 656.0

C N/A 0.028 0.035 0.011 0.0128

n N/A 0.28 0.47 0.25 0.5

m N/A 1 1 1 0.8

Also, a damage model that takes account into the sensitivity to strain rate, temperature and hydrostatic pressure should be used in orthogonal cutting simulations. Equations (2) and (3) present the damage initiation criterion as:

D=∑ ∆εε f

(2)

ε f=[D1+D2exp (D3Pσ )] ∙[1+D4 ln ( ε̇ε̇0 )] ∙[1+D5(

T−T room

T fusion−T room)] (3)

Where ∆ ε is the increment of the equivalent plastic strain, ε f is the equivalent strain at fracture, and the initiation of damage occurs when D=1(Ayed, Germain et al. 2014). The calculation of the rate of tri-axiality is shown in first term, and the sensitivity to the strain rate and temperature is presented in second and third term, respectively. The hydrostatic pressure increases, the fracture strain decreases. The damage is based on the stiffness degradation of the material until failure point. Table 2 shows damage law parameters of Ti-6Al-4V.

Table 2: Johnson-Cook damage law parameters of Ti-6Al-4V.

Material D1 D2 D3 D4 D5

Ti-6Al-4V -0.09 0.25 -0.5 0.014 3.87

After the input of Johnson-Cook constitutive and damage laws, Hillerborg’s fracture energy is used to introduce model damage evolution lest unstable failure process by controlling the material degradation after the damage initiates. And also, catch high strain localization during chip segmentation even for relatively large size element(Zhang, Mabrouki et al. 2011). It is adopted to reduce the mesh dependency by creating a stress-displacement response after initiation rather than continuing to use the stress-strain relation that causes a strong mesh dependency based on strain location leads to an inaccurately representation of material behavior(Mabrouki, Girardin et al. 2008). Mentioned stress-strain relation of a ductile metal is used as damage model of material in simulation process. A typical uniaxial stress-strain response has been exploited and shown in figure 1 and table 3.

Table 3: connoted stress-strain relationship.

Latters Explanations

O-A Presents Linear elastic behavior

A-B Presents Plastic yielding with strain hardening

B-D Point B presents the damage initiation criterion, and D corresponding to the maximum stress where the hardening modulus reaches zero value.

D-F After reaching maximum stress, material tends to have observed fracture at point E and continuing to point F, where theoretical final fracture happens.

Dotted line curve ABC Presents the undamaged stress-strain

response.

Figure 1 typical uniaxial stress-strain response of a metal specimen.

The chip formation, by ductile failure phenomenon, occurs in two steps. The first step concerns the damage initiation whereas the second one concerns damage evolution based on the fracture energy approach(Zhang, Mabrouki et al. 2011).

The friction between workpiece and cutting tool is the most important property in machining process. Taylor-Quinney coefficient and Coulomb-Orowan friction law and are used in the definition of tool-workpiece interface and tool-chip interface, respectively. The coefficient of Taylor-Quinnney is generally between 0.8 and 1. It indicates the heat dissipation in shear zones by conduction at plastic deformation process. Heat transfer coefficient and thermal contact resistance are two determines that govern the heat transformation process in the machining process(Atlati, Haddag et al. 2014). Heat partition also exists in the machining process by mechanical work of frication. Coulomb-Orowan law is chosen to present the generated heat by friction at tool-workpiece interface. Frictional stress and sliding velocity are two determines in this problem, since the fraction of friction work is usually taken as 1(Atlati, Haddag et al. 2014).

Contact properties in FEM

There are two kinematic contacts in mechanical calculation: normal gap and tangential gap, which refers to the intersection behavior in ABAQUS. The purpose of these two contacts is to set a proper interface contact between two geometries.

Penalty method is used in the numerical resolution of several mechanical contact problems, such as contact between the tool and the workpiece in machining. It provides an approximate value and the accuracy of results is depended on the value of penalty coefficient (ε n¿. In order to have most accurate results, normal gap or called penetration value has to be minimized. A penalization coefficient is induced to counter the penetration value and it is generally very high. So, a high contact pressure is produced which prevents the penetration of the master surface in the slave surface, which is in the interaction properties(Atlati, Haddag et al. 2014).

Geometric model and its modeling data

A lot of researchers have selected ABAQUS to simulate the machining process of Ti-6Al-4V titanium alloy turning. 2D orthogonal cutting is considered for better computational time and data size. Linear quadrilateral continuum elements CPE4RT were utilized under plain-strain condition for a coupled temperature-displacement calculation(Zanger and Schulze 2013). The used elements are in the

mean of reduced integration and enhanced hourglass control. The contact surface pair between tool and workpiece is designated as master-slave ones. Three parts assemble the workpiece in order to optimize the contact management: part one the uncut chip thickness, part two the tool-tip passage zone and part three the workpiece support. Tie constrains between parts one to three are used to manage the assembly of workpiece. And normally, a chamfer is designed to avoid large elements distortion problem at the beginning of simulation. Workpiece is usually fixed at bottom, and tool is allowed to move horizontally with restrained vertical movement. Figure 2 presents the model mesh and boundary conditions of one of many simulations.

Figure 2 Meshed model and its boundary conditions.

Properties of titanium alloy Ti-6Al-4V and its current of art

In this research, α/β titanium alloy Ti-6AL-4V (grade 5) is used (the alloy has a β transition temperature of 995oC.) The physical properties and chemical composition of the titanium alloy are presented in table 4 and 5, respectively.

Table 4: Physical properties of Ti-6AL-4V alloy.

Tensile strength (MPa)

0.2% proof stress (Mpa)

Elongation (%)

Density (kg/m-3)

Melting point (oC)

Measured hardness (CI-99%)*(HV100)

Thermal conductivity at 20oC (Wm-1K-1)

900-1600 830 8 4430 1650 Min. =341 6.6

Max. =363

Table 5: Nominal chemical composition of Ti-6AL-4V alloy (wt. %).

Al V Fe O C H N Y Ti

Min. 5.50 3.50 0.30 0.14 0.08 0.01 0.03 50 ppm Balance

Max. 6.75 4.50 0.23

Polycrystalline diamond (PCD) is used in the research of machining of Ti-6AL-4V alloy due to its sufficient toughness with wear resistance. PCD is produced by growing the neighbouring grain together, which means the cobalt from the cemented carbide substrate infiltrates through the layer of micron synthetic diamond power under extreme pressures and temperatures. Nowadays, PCD is appreciated by majority tooling companies around the world. Chemical and mechanical properties of the PCD tools employed are shown in table 6(da Silva, Machado et al. 2013).

Table 6: Specification, chemical and mechanical properties of the PCD tool.

ISO designation

Chemical composition (wt.%)

Hardness (Knoops) Gpa

Density (gcm-

3)Thermal conductivity at 20oC (Wm-1k-1)

Grain size (µm)

CCMW 120408F-L1

Diamond+ Co residue

50.0 4,12 540 10

Coolant supplies regarding to surface integrity, tool life and wear mechanisms

It is noticed that a large proportion (about 80%) of the heat generated when machining Ti-6AL-4V is conducted into the tool because it cannot be removed with the fast growing chip due to the low thermal conductivity of titanium alloys(W Konig. 1979).

Ezugwu and Bonney reported that Surface finish generated when machining Ti-6AL-4V with PCD tools are generally acceptable and free of physical damages such as, tears, laps or cracks in all the cutting conditions. Machining at high-speed conditions with PCD tools tend to soften the machined surface under coolant pressures of 11 and 20.3 Mpa due to efficient cooling of the cutting interface by high-pressure coolant. Hardening of machined surface was observed after machining with conventional coolant flow due to irregular cooling effect that tend to promote rapid quenching effect(Ezugwu, Bonney et al. 2007).

Da Silva and Machado concluded that increase in coolant pressure tends to improve tool life and reduce the adhesion tendency, accelerated by the susceptibility of titanium alloy to gall during machining. They reported a desired segmented chips were generated when machining with high pressure coolant supply, while long continuous ribbon like chips produced when machining Ti-6AL-4V with conventional overhead coolant supply could result in swarf entanglement with cutting operation and consequent fracture of the cutting tool and the best results were encountered with the highest (20.3 Mpa) coolant pressure at lower speed conditions(da Silva, Machado et al. 2013).

In order to improve tool life, cryogenic compressed air is used as one of many sub-zero coolants to lower cutting temperature and reduce tool-chip contact length. A low cutting temperature results in reducing wear rate, size of chip built-up edge and maintaining the strength of the cutting edge in order to resist plastic deformation(Sun, Brandt et al. 2015). Generated cutting forces with the application of cryogenic compressed air are higher at beginning of operation, but decrease in accumulative cutting time, especially, at higher cutting speed, due to reduction in size of chip build-up edge(Sun, Brandt et al. 2015).

Thermally enhanced machining (Laser Assisted Milling process)

Thermal assisted machining sounds contradictory with the general idea of lowering cutting temperature, instead relies on introducing heat from an external source to reduce the workpiece material’s strength and hardness, thereby reducing cutting forces and making the material easer to machine(Bermingham, Palanisamy et al. 2012). The methodology they adopted is to preheat the

workpiece to allowed thermal equilibrium by furnace and an additional oxyacetylene gas torch. It reported that the TAM did not significantly improve tool life due to exacerbated rate of diffusion(Bermingham, Palanisamy et al. 2012).

Bermingham and Sim compared the tool life during laser assisted milling, dry milling, milling with flood emulsion, milling with minimum quantity lubrication (MQL) and a hybrid laser + MQL process. In general, longer tool life was achieved by delaying the onset of notching. Notching formed from localised damage to tool at the depth of cut. In this region adhesion and attrition process were found to accelerate notch wear. Diffusion related wear process and abrasion are also likely to contribute tool failure. All wear process are exacerbated at high temperature(Bermingham, Sim et al. 2015). They also concluded that notch formation was suppressed and these tools did not fail during testing under certain MQL conditions, but, in some circumstances the use of flood coolant caused thermal shock and catastrophic failure; this tendency increased with cutting speed. The hybrid process that combined MQL with laser assisted milling increased tool life by over 5 times compared to conventional laser assisted milling(Bermingham, Sim et al. 2015).

Liu and Shi built an analytical model to simulate the LAM process by using Finite Element Method. Eq. (4) shows the laser beam intensity which strikes the surface of a workpiece(Liu and Shi 2014);

I=IOe−(X2+Y2 )/RO

2

4)

Eq. (5) shows the intensity if the heat flux applied on the top surface(Liu and Shi 2014);

Pw=1A∬A

IO e−( X2+Y2 )dxdy 5)

And Eq. (6) and (7) presents the cutting force elements on the bottom cutting edge(Liu and Shi 2014);

d Fr=K rc f z sinksin∅ dz+Kℜdz

coskcosβ

6)

d Ft=K tc f z sinksin∅ dz+K tedz

coskcosβ

7)

They reported drops in laser beam centre and the point in front of cutting zone about 260 oC and 94oC, respectively. The results showed that laser assisted heating can reduce the stress impact to cutting zone caused by cutting edge penetration from more than 800 Mpa to less than 400 Mpa. Consequentially; the maximum of cutting forces will be depressed effectively(Liu and Shi 2014).

LAM also produces the smoother machined surfaces with less grain pull-out and smaller depth of the deformation zone (Sun, Harris et al. 2008). But, LAM reduces the compressive residual stresses at the surface or transforms these stresses into tension with increasing laser power (Germain, Morel et al. 2007), (Germain, Morel et al. 2006). The effect is more significant at low cutting speed and becomes negligible when the cutting speed is higher than 54 m/min(Sun, Brandt et al. 2010). One advantage of laser beam as external heat source is its smaller and accurately controlled spot size(Lacalle, Sάnchez et al. 2004), there is no Widmanstatten microstructure observed in the machined subsurface after laser assisted machining(Sun, Harris et al. 2008), (Germain, Morel et al. 2007), (Dandekar, Shin et al. 2010).

Experimental analysis of PCD and WC-Co inserts in Ti-6AL-4V alloy

Nurul Amin et. al reported the effectiveness of PCD inserts have been found far outperform uncoated WC-Co inserts in machining Ti-6AL-4V. They found that PCD inserts resulted in three (3) - fold increase in cutting speed with approximately two (2) - times higher metal removal capacity within the life span of the insert compared to WC-Co inserts. The applicable cutting speed range for PCD inserts is close to 120 m/min and the surface roughness is less than 0.3 µm, whereas the WC-Co is more suitable for speed range from 40-80 m/min with surface roughness ranged from 0.25 – 0.3 µm(Amin, Ismail et al. 2007). Figure 3(a) and 3(b) shows the tool wear mechanisms of WC-CO and PCD insert under SEM for cutting speed from 120 – 250 m/min, and 40 – 160 m/min, respectively. The average surface roughness produced during machining using PCD inserts is lower compared to that using uncoated carbide inserts, is due to the effect of lower wear rate and less pronounced chatter in the case of PCD inserts(Amin, Ismail et al. 2007).

Figure 3(a), 3(b) SEM views of the WC-Co and PCD inserts at the end of machining experiment.

Methodology

1. Literature review of current published journal paper, researches and articles regarding to machining of Ti-6AL-4V alloy, PCD tool and FEM analysis. Focus will be on the current methodology of increasing PCD tool life and production rate, reducing tool wear and manufacturing cost, improving finishing quality of machined workpiece.

2. Analytical wear models, experimental procedures and FEM analysis setups will be produced as combinations from published literature to conduct, proceed and compare this project regarding to tool performances.

3. After validations and comparison of experimental and simulation results, an optimal solution could possibly be predicted under the conclusions of current literatures.

Experimental setup

The diameter of workpiece is 37.95 mm. the thickness of PCD insert is 1 mm comprised by 0.1 mm PCD layer and 0.9 mm WC-Co support. It has 5 mm width and length, respectively. Workpiece is fixed at one end with restrained vertically movement. The rake angle of insert in determined by the geometry of tool holder, it has edge direction angle κ γ=900, flank angle (or clearance angle) α o=00, and rake angle γo=60. The feed rate, cutting speed and cutting depth are not able to acquire due to computer’s defect. Figure 4(a-b) presents the experimental setup of titanium alloy turning process under conventional coolant supply. Figure 5(c-d) shows the manufacturer of tool holder and chip after machining.

Figure 4(a-b) presents the experimental setup of titanium alloy turning process under conventional coolant supply.

Figure 5(c-d) tool holder and chip formation.

Simulation built-up

3D stress model

A multi-parts model was developed in order to simulate the theoretical cutting process. The tool is designed as 3D deformable solid elements C3D8R, which have reduced integration and hourglass control. The rake angle of tool insert is 6 degree and flank angle is set as zero degree. The workpiece was meshed by the same element type as tool did, but, there was a partition cell existed in the workpiece to separate the workpiece support and chip-tip passage zone. Figure 6 presents the partition cell and mesh density used in the simulation. Johnson-Cook damage law and its damage evolution along with other material properties were used to assign the sections. The simulation was under dynamic explicit with 0.001 (s) time period. Penalty method is used in the intersection property with a 0.7 friction coefficient. The boundary conditions are presented in figure 7, where amplitudes were used to make 0.02 (m) displacement. Element deletion was utilized in order to remove those completely damaged elements. Figure 8 and 9 show Von-Mises equivalent stress and reaction forces of the simulated model. The Von-Mises equivalent stress is shown in figure 10, where the interface at tool nose area has largest stress. The uncut chip region which is about to become the chip once it climb up the tool rake face has largest stress as well. At the end of simulation, chips became segmented and can be observed through ABAQUS visualization. Figure 11 presents chip formation of simulation titanium cutting process.

Figure 6 mesh density and partition cell.

Figure 7 boundary conditions.

Figure 8 Von-Mises equivalent stress of simulated model.

Figure 9 reaction forces of simulated model.

Figure 10 Von-Mises equivalent stress at tool-chip interface.

Figure 11 chip formations at end of simulation, segmented chips are noticeable.

2D orthogonal cutting model

Since no cutting parameters were available to be ready as the inputs of ABAQUS, the2D model was developed by combining parameters of several reference journal papers. Table 7 presents properties of titanium alloy and tool insert. As mentioned above, 2D cutting model was developed by multi-parts model. It composed of four parts: part one is tool geometry; part two is uncut chip thickness;

part three is tool-tip passage zone and part four is work support. The length of workpiece is 12mm with a 1.2mm in height. The height of tool-tip passage zone is about 20um, and uncut chip thickness is 0.28mm. A chamfer was designed to avoid element distortion at beginning of calculation. The rake angle is taken as 6 degree along with 0 degree flank angle. Figure 12 presents assembly step of multi-parts model. Element type CPE4RT was selected in ABAQUS library because displacement and temperature are both node variable. Interaction surfaces are crucial in assembly; otherwise, model cannot be successfully simulated. Table 8 presents the first and second surfaces which are used in the model.

Table 7 parameters of workpiece and tool insert.

Parameters of properties Workpiece Tool (PCD inserts)Density (kg/m3) 4430 3520

Elastic modulus (Gpa) 110 850Poisson’s ratio 0.33 0.1

Specific heat (J/kgoC) 670 400Thermal conductivity (w/moC) 6.6 1200

Expansion coef (um/m/oC) 9 XMelting temperature (oC) 1630 XRoom temperature (oC) 25 25Heat transfer coefficient 40000Heat partition coefficient 0.5

Friction coefficient 0.25Friction energy transferred into

heat 100%

Inelastic heat fraction 0.9

Table 8 surface definitions.

Interactions First surface Second surface1 Chip-bottom Join-top2 Work-top Join-bottom3 Tool-face Chip-bottom4 Work-top Chip-bottom5 Self-contact (chip along)

Figure 12 Instances of multi-parts model, join section is shown in red.

Results and discussions

In 2D orthogonal cutting model, the occurrence of adiabatic shear band formation is observed through three steps by using the variation of temperature and von Mises stress(Ayed, Germain et al. 2014). Figure 13(a-f) show three temperature variations with respects to von Mises stress at same time spot.

At beginning, the plastic deformation is homogenous and the phenomenon of hardening outweighs the softening. Maximum stress is reached at the end of the plastic deformation step. Then, adiabatic shear band (ASB) is taken into account and hardening and softening present comparable influences. ASB is established at the last step when the equivalent stress drops suddenly under the influence of thermal softening that has dominated effects on it. So, at this stage, the temperature reaches maximum and the stress reaches its minimum. By observations from figure 13(a-f) the temperatures at tool chip interface falls in the shear band formation category. Colour contractions in both progressions are clearly presented in the red ovals. An increase in equivalent von Mises plastic stress is to be noted in the primary shear zone with a decrease in stresses near the tooltip due to a loss in material stiffness. As a result, the plastic strain and temperature increase near the tool tip and evolve towards the chip-free side. At the end, the damage is extended along the primary shear zone due to the excessive compression state(Mabrouki, Girardin et al. 2008).

Figure 13a temperature contour at time 2e-06.

Figure 13b von Mises contour at time 2e-06.

Figure 13c temperature contour at time 2.7504e-06.

Figure 13d von Mises contour at time 2.7504e-06.

Figure 13e temperature contour at time 1.5004e-06.

Figure 13f von Mises contour at time 1.5004e-06.

In order to investigate the relationship between cutting speed and temperature, 40m/s and 70m/s cutting speed are used separately. Figure 14a and 14b present the difference in temperature when cutting speed increased. Temperature of 70 m/s cutting speed has 100-Celsius degree higher than 40 m/s cutting speed at tool-chip contact zone. Figure 15 (a-d) present the PEEQ and von Mises stress of corresponding cutting speed. Chip morphology is another consideration during simulation process. There are 8 segmentation wavelengths generated through 40m/s cutting speed, and 16 segmentation wavelengths in 70m/s cutting speed. There is no significant change regarding to wavelength about these two cutting speed, but PEEQ at 70m/s cutting speed has smaller values compared with 40m/s due to temperature increased.

Figure 14a NT11 at 40m/s cutting speed.

Figure 14b NT11 at 70m/s cutting speed.

When comes to the titanium manufacturing, conventional milling, laser-assisted machining or usage of sub-zero temperature coolant can result in different contact temperatures. As shown in figure 16, laser-assisted machining has less change in temperature compared to conventional milling, and both heated has the worst temperature performance in all four category. Laser assisted heating can reduce the stress impact to the cutting zone caused by cutting edge penetration, as a result, the maximum of cutting forces will be depressed effectively. Heat flux generated by laser-assisted machining is lesser than conventional milling due to the decrease of the flow stress caused by laser heating effect, which is simulated through predefined field(Ayed, Germain et al. 2014). Noticing the sub-zero temperature coolant has a relative large temperature difference, but, the maximum temperature is the lowest in all four test versions. As a result, there is less thermal softening as well as shorter tool-chip contact length(Sun, Brandt et al. 2015).

Figure 15a PEEQ at 40m/s cutting speed.

Figure 15B PEEQ at 70m/s cutting speed.

Figure 15c von Mises at 40m/s cutting speed.

Figure 15d von Mises at 70m/s cutting speed.

The friction coefficients were taken as 0, 0.1 and 0.25 at tool-chip interface. From figure 17, it can be observed that temperatures were increased as the CoF increased. However, chip morphology of three different versions remains almost the same whatever COF is. The main reason is that more energy was converted into heat when increasing the friction coefficient, and it will induced a thermal softening in both sides of the machined material and also the cutting force is tends to decrease when increasing the friction coefficient(Zhang, Mabrouki et al. 2011). Orange line in figure 17 is the model without friction coefficient and the nodal temperature drops quickly after it passed tool-tip contact zone. Same situation happened for other two graphs, but with postpone decrease.

Table 9 predefined temperature data.

Test namePredefined temperature (oC) (tool/workpiece)

Highest temperature (oC)

Lowest temperature (oC)

Temperature differences (oC)

Room temperature 20/20 809.294 20 789.294

Both heated 600/300 1198.03 300 898.03

LAMs 300/20 972.503 286.297 686.206

Cryogenic compressed air

-196/20 646.596 -196 842.596

During titanium machining process, coolants are crucial in terms of injection angle, injection pressure and its characteristics under high temperature. The purpose of simulation is to identify the regions with highest temperature. In this case, temperatures of tool-chip interface shown in figure 18a were plotted against time. Temperatures rose quickly from predefined temperature filed to 900 Celsius-degree and drop to almost 100 Celsius-degree above defined temperature and remained there when time ran out. Comparing with figure 18b the temperatures of workpiece-tool ranged from 300 oC to 370 oC. So, the position of coolant injection can be predicted through simulation. Another advantages of high pressure coolant is that it can produce small and segmented chips rather than long continuous ribbon

like chips that mainly generated by shearing of work material, which will often require to clean it away from machining area to prevent swarf entanglement.

Figure 17 CoF effect on temperature.

Figure 16 CoF effect on temperature.

Figure 18a temperature contours at 70m/s cutting speed plotted at join mesh region.

Figure 18b temperature contours at 70m/s cutting speed plotted at work-piece mesh region.

3D stress model was created and simulated successfully. By observing the simulated results about Von-Mises equivalent stress shown in figure 10, it can predict that the heat produced by friction work exists in the tool-chip interface and tool nose. As temperature rises, chemical affinity is likely to happen between tool and workpiece, especially in titanium cutting process. Inter-diffusion of titanium and carbon has been reported, the diffusion process results in the formation of titanium carbide layer on the tool(da Silva, Machado et al. 2013). Once the cutting edge has been damaged, localised notch formation quickly occurs and any exposed tool asperities are rapidly removed to grow the notch(Bermingham, Sim et al. 2015). As a result, the entire section of material are ripped out by flowing chip material.

Segmental chips were formed at the end of simulation due to calculation of numerical values of damage D and it is related to the SDEG. Figure 19 shows the SDEG values versus time frame. The thresholds at the top of graph are presented in the deformed shape as segmental chips. Figure 20(a-b) marks out the segmental chips within red oval.

Figure 19 SDEG versus time frame, especially 0.6e-03, 0.8e-03 and 1e-03.

Figure 20a damage model at time=0.6e-03.

Figure 20b damage model at time=0.8e-03.

Figure 20c damage model at time=1e-03.

Due to the incapability to obtain experimental findings, the simulated data were been reviewed by cross-checking with several journal papers. The temperatures under different cutting speed and predefined field condition are fall in the range of available results. 10% over temperature is observed mainly due to difference material properties setting, alternative mesh density. Interaction properties contribute to the behaviour of model, and have possible link to the accuracy of results.

Conclusion

Literatures are properly reviewed by sifting through topic related journal papers and ABAQUS user’s manual. The results showed that the simulation of titanium turning process was completed. The theories behind temperature with different parameters are investigated and noted by changing the inputs via FEM analysis. Reasons of element distortion have been identified. Varied mechanical properties of titanium alloy under different test procedures have been used against the output of simulation. The results showed that material properties changes only contribute small percentage on the chip formation and temperature variables.

Acknowledgement

The author appreciates the support of Dr. Songlin Ding, and Adam Lee for their kindly helps during simulating and material testing.

Reference

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