Experimental and Numerical Investigation of Ti6Al4V ...

Proceedings of NAMRI/SME, Vol. 42, 2014

Experimental and Numerical Investigation of Ti6Al4V Alloy machinability using TiAlN Coated Tools Salman Pervaiz Department of Production Engineering KTH Royal Institute of Technology Stockholm, Sweden Ibrahim Deiab School of Engineering University of Guelph Ontario, Canada Amir Rashid and Cornel Mihai Nicolescu Department of Production Engineering KTH Royal Institute of Technology Stockholm, Sweden ABSTRACT Titanium alloys exhibit extraordinary characteristics such as excellent strength-to-weight ratio, superior corrosive and erosive resistance and capability to operate at high operating temperatures. These alloys show poor machinability rating due their low thermal conductivity and high chemical reactivity. This study investigates the machinability of Ti6Al4V using TiAlN coated tools by analyzing cutting forces and cutting temperatures. The simulated cutting force data was used to predict the total energy utilized by the process. Cutting tool temperatures during the machining operation were measured by an Infrared (IR) camera with cutting forces experiments. Finite element simulations can offer a cost effective solution when evaluating the machining performance of difficult to cut materials such as Titanium alloys. The study incorporated modified Johnson-Cook constitutive equation and friction model to develop the finite element simulations of the machining process. The finite element simulated results of forces and tool temperature presented good agreement with the experimental results. KEYWORDS Titanium alloy, Cutting temperature, Finite element simulation, Cutting force

INTRODUCTION Titanium alloys such as Ti6Al4V are being utilized extensively in the manufacturing of airframe structures, aerospace engines, power generation, construction, biomedical instruments and chemical processing equipment. Titanium alloys offer excellent strength-toweight ratio, high toughness, superior corrosion and erosion resistance and high strength at high temperature and low density [1]. Titanium alloys are known as difficult-to-cut material as they show poor machinability rating during cutting process. Literature has reported low thermal conductivity, high chemical reactivity, low modulus of elasticity and ability to maintain high strength at elevated temperature as main causes of poor machinability rating of Titanium alloys [2].

Coatings on the cutting tools are applied to increase the machining performance of the tool. Increase in hardness of the tool results in improved resistance against abrasion, adhesion oxidation and diffusion [3, 4]. Other important aspects of coatings are that they increase lubricity and decrease friction at tool chip interface result in better machining performance by lowering the cutting temperature at cutting zone [5]. Wang and Ezugwu [6] performed machining experiments to investigate the machinability of Ti6Al4V using TiN and TiN/ TiCN/ TiN - PVD coated tools. The study showed that TiN single coated tools performed better than other tools at higher feed levels. Choudhury and El-Baradie [7] executed machining experiments on titanium alloys using coated and uncoated tools. The study pointed that uncoated tools performed better at cutting speeds between 26 – 48 m/ min. However coated tools performed better at higher values of depth of


cut. The study suggested cutting speed of 20-25 m/ min, feed level of 0.15 - 0.2 mm/ rev and depth of cut of 1 mm as recommended setting for uncoated cutting tools. Bouzakis et al. [8] explored the machining performance of round sharp corner based TiAlN coated tools. The study used three different types of cutting tools named as intensively grounded, slightly grounded and as deposited. The experiments optimized the performance of rounded cutting tool and further more varied using finite element simulations. Experiment based modeling approaches involve expensive experimental setups and time consumption. Researchers in the metal cutting sector are keen to use finite element simulations to efficiently predict the machining performance. Finite element simulations have been employed in literature to optimize the cutting tool geometry and predict the optimum machining parameters, tool wear, tool life, cutting tool temperatures, residual stresses and surface finish [9]. Arrazola and Özel [10] pointed out that accuracy of the predicted results highly depends upon the modeling method, constitutive model for material flow stress, boundary conditions (heat transfer) and frictional law at tool-chip interface. In another study Özel et al. [11] investigated the machining performance of Ti6Al4V using uncoated and coated tools. The study developed a modified constitute model to simulate an accurate chip formation. Finite element simulations based predicted cutting forces and tool wear results were found in good agreement with experiments. In another study Özel and Sima [12] incorporated different versions of Johnson-Cook constitutive model in their finite element simulations to predict the cutting performance of Ti6Al4V. The modified material models coupled the effects of flow softening, strain hardening and thermal softening effects. The study revealed that flow stress behavior of material greatly influences the temperature generation and cutting forces. Umbrello [13] has also conducted a finite element simulation based study on high speed machining of Ti6Al4V alloy. The study was focused on predicting cutting forces and chip segmentation and morphology. The experimental results found in good compromise with the simulated results. Split Hopkinson’s pressure bar method (SHPB) is used to find the values of Johnson-Cook parameters. Literature [14] recommends that Split Hopkinson’s pressure bar method (SHPB) should be referred as a starting point for the identification of JohnsonCook parameters. For accurate machining prediction, Split Hopkinson’s pressure bar method (SHPB) should be used in combination with machining tests and analytical chip segmentation models. In this current study machinability of Ti6Al4V has been explored experimentally using TiAlN coated tools and the experimental results are compared with 3D finite element simulated predictions as well. 3D finite element simulations have been modeled to predict the cutting forces and tool temperatures during the machining process. These finite

element simulations have incorporated the properties of TiAlN coating and modified Johnson-Cook constitutive model used by Özel and Sima [12] to attain the flow stress behavior of Ti6Al4V. EXPERIMENTS Workpiece material For this study, alpha – beta titanium alloy (Ti6Al4V) was utilized in the machining experiments as a workpiece material. Raw material of Ti6Al4V (ASTM B381) was available in the form of cylindrical rod. The chemical composition (wt. %) and mechanical properties of Ti6Al4V are mentioned in Table 1 and Table 2 respectively. Table 1 Nominal chemical composition of Ti 6Al 4V (wt. %) Element C Al Fe V Ti

% < 0.08 5.5 – 6.75 < 0.4 3.5 – 4.5 Balance

Element

H N O

%

< 0.05 < 0.01 < 0.2

Table 2. Thermal and mechanical properties of Ti 6Al 4V Thermal properties Thermal conductivity (W/m.K)

7.3 580 at 25 °C

Specific heat capacity (j/kg °C) Mechanical properties Density (kg/ mm3) Hardness (HRC) Modulus of elasticity(GPa) Yield strength (MPa)

4428 36 110 825 1933

Melting Temperature (K)

Cutting tool material Physical vapour deposition (PVD) TiAlN coated turning inserts were used in the experimentation of the presented work. The specified cutting insert came with two cutting edges. Each cutting edge was used for single experimental run.


Table 3. Cutting tool specification [31] Cutting Inserts PVD - Coated Carbide 

CCMT 12 04 04 MM 1105

GC1105 grade is a PVD coating of titanium aluminum nitride (TiAlN) over carbide that provides high wear resistance

L (Cutting edge length) S (Insert thickness) IC (Inscribed circle dia ) Corner radius

presented in Eq. (1). The additional term in model with tanh function is responsible of changing flow stress at higher strains to introduce the effect of flow softening. Parameters p, r, S and D are responsible of controlling the flow behaviour and depends upon the material itself. The exponent S specifically controls the tanh function for thermal softening [16].

12.8959 mm 4.7625 mm 12.7 mm 0.4 mm

Machining Tests and Cutting Conditions All of the machining experiments were conducted on a CNC tuning center. Kistler Multi-component dynamometer was utilized for measuring the cutting forces generated during the machining operations. Infrared camera, UFPA – T170, was employed to measure the cutting temperature in the cutting zone during machining operations. The study was performed using three different levels of cutting speed and feed rate as shown in Table 4. Fig. 1 shows schematic illustration of experimental set up. Figure1. Schematic representation of experimental setup Table 4. Cutting parameters Machining Parameters Depth of cut (mm) Cutting Speed (m/min) Feed (mm/ rev) Machining Environment

σ = [A+Bεn ( 0.8 mm Constant Three levels (90 - 120 - 150) Two levels (0.1 – 0.3) Dry

FINITE ELEMENT SIMULATION Material Constitutive Model As recommended by literature [11-12], a modified version of Johnson-Cook constitutive model was incorporated in the Deform 3D FE simulations. The modified Johnson – Cook model contains the effect of temperature based flow softening, strain, strain rate hardening and thermal softening [15]. The model is

[1 – (

] [1+C ln ] ][D + (1-D) [tanh (

]] (1)

Where D= , p= , σ represents flow stress, ε is true plastic strain, ἑ is strain rate, ἑ° is reference strain rate, T is workpiece temperature, Tm is melting point and T° is ambient temperature. Johnson-Cook parameters have been selected from the literature [11]. These parameters are mentioned in Table 5. Flow stress curves have been plotted against the true strain


using different strain rate levels. These flow stress values have been incorporated in to the Deform 3D to simulate cutting behaviour of Ti6Al4V.

The actual cutting involves the presence of sticking and sliding regions on tool chip cutting interface as shown in Figure 3. In DEFORM - 3D finite element simulations, friction in sticking region was defined by using shear friction law that is defined as in Eq. (2). Shear friction law is used to define the severe contact conditions in metal cutting [18]. m = τ/k

(2)

Where m is frictional factor, τ is frictional shear stress and k is work material shear flow stress. Literature [11] used friction factor (m) value of 0.85 for TiAlN coated tools. At the remaining length of the cutting tool sliding region is formed. Sliding region means mild contact condition and can be modelled by using Coulomb friction law. Coulomb friction law is represented in Eq. (3). Figure 2. Flow stress curves using modified Johnson-Cook constitutive model Table 5. Johnson-Cook model parameters [11] A B C n m

782.7 MPa 498.4 MPa 0.028 0.28 1.0

S r d b a

τ = µσ

(3)

Where µ is coefficient of friction, σ is normal stress and τ is shear stress. Literature [11] used coefficient of friction (µ) value of 0.5 for TiAlN coated tools.

0.05 2 1 5 2

Table 6. Temperature dependent mechanical and thermo physical properties for 3D FE simulations

E(T) [MPa] α(T) [1/ ◦C] λ(T) [W/m/◦C] Cp(T) [N/mm2/◦C]

Ti6Al4V [17]

TiAlN coated tool [11]

-57.7T+111.672 3.10-9T+7.10-6 0.015T+7.7 2.7e0.0002T

6.0 x 105 9.4 x 10-6 0.0081T + 11.95 0.0003T + 0.57 Figure 3. Frictional shear and normal stress distribution over the rake face of tool [10]

For accurate and precise simulations, temperature dependant mechanical and thermo-physical properties of Ti6Al4V were also incorporated in the 3D FE simulations. Tool-chip contact friction model To simulate the metal cutting operation, it is necessary to define friction model at the tool chip interface.

Damage Model The Cockroft and Latham damage criterion [19] was utilized in 3D finite element simulations to facilitate the fracture. The fracture criterion generally controls the segmented chip formation when machining titanium alloys [20]. As per this damage model, fracture starts when


integral of highest principal stress component over a strain path becomes equal to the certain critical damage value. Eq. 4 represents the Cockroft and Latham damage model. The critical damage value was used as 0.6, which is a general value for machining simulations.

(4) Where εf is effective strain, σ1is maximum principal stress and D is critical damage value based on material.

(a)

(b)

Finite Element Model in Deform 3D The CAD model of CCMT insert was created in Autodesk Inventor as shown in Figure 4a and then imported in the DEFORM-3D for machining simulations. In finite element model the cutting tool was represented by using 21955 tetrahedral mesh elements as shown in Figure 4b. Similarly workpiece has been represented using 96719 tetrahedral mesh elements as shown in Figure 4c. The workpiece material was modelled as plastic as per modified Johnson-Cook constitutive model as described in Eq. 1. The cutting tool material was modelled as rigid material with Tungsten carbide (WC) as base material and TiAlN as a coating material. Figure 4d shows the finite element based machining simulated executed to obtain cutting temperature and cutting force predictions.

(c)

RESULTS AND DISCUSSION Flank Wear Measurement In material removal operation, cutting edge of the tool experiences different wear mechanisms and permanent deformation. Wear mechanisms results in the loss of tool material from the cutting edge. Under general machining scenario flank wear appears on the flank face of the cutting tool. Flank wear has a direct influence on the dimensional accuracy of the workpiece material. Flank wear measurement provides the most common and practical measure of tool life. Due to the simplicity involved in measurement it is widely used all over the industrial sector. In agreement with the standard ISO 3685: 1993 (E) [21], average flank wear of 0.3 mm was employed in all machining tests as tool life criteria. Fig. 5 shows the tool life observed at cutting speeds of 90, 120 and 150 m/ min for feed of 0.1 mm/ rev. In agreement with classical literature, it was observed that tool life drastically reduced with increasing cutting speed. Highest tool life of approximately 140 sec was observed at 90 m/ min and lowest tool life of 32 sec was observed at 150 m/ min.

(d) Figure 4. Finite element model, (a) CAD model of CCMT type insert, (b) Mesh generated on cutting tool, (c) Mesh generated on workpiece material, and (d) Finite element based machining simulation

Figure 4. Flank wear measurement for cutting speeds of 90, 120 and 150 m/ min at feed of 0.1 mm/ rev


However, at 0.3 mm/ rev feed level cutting force predictions showed an error of 1- 15%. Experimental work has revealed that tool wear plays very critical role towards the behaviour of the cutting forces and cutting temperature. However, in the DEFORM Usui’s wear model is generally employed to simulate wear. Possible deviations in the wear can cause errors in the cutting force and temperature predictions.

Figure 5. Flank wear measurement for cutting speeds of 90, 120 and 150 m/ min at feed of 0.3 mm/ rev Similar trends of tool life were observed for cutting speeds of 90, 120 and 150 m/ min at higher feed level of 0.3 mm/ rev as shown in Figure 5. Extremely short tool life of approximately 4 sec was observed for 150 m/ min cutting speed and 0.3 mm/ rev feed level. As a general trend, it was observed that increase in cutting speed reduces tool life. Highest tool life was observed at lowest cutting speed of 90 at low feed level of 0.1 mm/ rev. However, extremely short tool life was observed at higher cutting speed of 150 m/ min and higher feed level of 0.3 mm/ rev.

Figure 6. Comparison of experimental and simulated cutting forces at feed of 0.1 mm/ rev

Cutting Force Measurements The cutting forces were measured using multichannel Kistler force dynamometer. The generated force signals were amplified using charge amplifier and data acquisition was performed using data Dynoware software package. The experimental obtained results are plotted and compared with FE simulated results as shown in Figures 6 and 7. In Figure 6 no significant increase in the magnitude of cutting force was observed with increasing cutting speed. Although a slight decrease of cutting force was observed. This behavior of cutting force is linked with the built-upedge (BUE) formation and thermal softening. Literature [22-23] reports that built-up-edge (BUE) forms at higher cutting speeds due to the presence of high cutting temperature. This built-up-edge (BUE) formation increases effective rake angle that results in lower cutting forces. Fang and Wu [24] also mentioned similar behaviour of Inconel 718 during their high speed machining experiments. To manage the simulation time efficiently, the finite element model was simulated till the state variable achieved steady state condition. The simulated results of cutting forces at feed level of 0.1 mm/ rev are reported in Figure 6. The simulated cutting forces were found in good agreement with experimental results with about 6% prediction error.

Figure 7. Comparison of experimental and simulated cutting forces at feed of 0.3 mm/ rev Prediction of Energy Consumption As mentioned in the literature [25] that the cutting force component (F cutting) in the basic machining model id in the same direction as of the cutting speed (Vc) of the machining process and thus is used to calculate power utilized in the machining process as shown in Eq. 5. By utilizing the product of time interval of the cutting process and cutting power, energy involved in the cutting process can be computed as shown in Eq. 6.


P cutting = FSimulated Vc

(5)

of 0.3 mm/ rev

E cutting = Pcutting Δt

(6)

The simulated cutting energy of the process can be used directly to estimate the environmental burden involved. It also provides a way to estimate energy even without performing the actual cutting operation. Cutting Temperature Measurement

As available in literature [25-26] that total energy utilization of a cutting process can be divided into the energy utilized in the cutting phase and the idle energy consumed in running the machine modules at zero load. Eq. (7) shows the total energy consumption of a machining operation. Energy Total = Energy cutting + Energy idle

(7)

To compute the idle energy (Energy idle) in a cutting process at certain cutting parameters, energy has been monitored for the air cutting (dry run) using a power logger PS 3500. The total energy utilized in the cutting was predicted by adding the cutting energy by using the simulated cutting forces and energy consumed by the machine tool during air cutting (dry run). The time interval (Δt) selection needs very careful attention as it directly affects the energy consumption.

Heat generation during cutting operation can significantly influence the tool wear and tool life. Presence of higher cutting temperature results in low tool life. Literature [27] supports that it is extremely hard to predict the intensity of heat generation during the cutting process. One of the main difficulties is the variation of mechanical properties of material with temperature deviations. Infrared thermography was utilized by Boothroyd [28] to analyse temperature distribution over chip and workpiece during cutting process. This present study utilized infrared camera to investigate the temperature field experimentally as shown in Figure 8.

(a)

Figure 8. Simulated total energy utilized in cutting at feed of 0.1 mm/ rev

Figure 9. Simulated total energy utilized in cutting at feed

(b)

(c) Figure 8. Sample measurement of cutting temperature under dry condition, (a) Cutting speed = 90 m/ min and feed = 0.1 mm/rev, (b) Cutting speed = 90 m/ min and feed = 0.3 mm/rev, and (c) Cutting speed = 150 m/ min and feed = 0.3 mm/rev The simulated results for the cutting temperature have been plotted against the experimental results as shown in Figures 9 and 10. It can be observed that simulated results displayed much higher temperatures than the values recorded experimentally using infrared camera. The reliability of experimental and simulated data was judged by consulting literature. Literature [12, 29] revealed that simulated results obtained for cutting temperature were found more reliable than our experimental readings. The main cause of low temperature values in the experimental


data was that machining experiments were stopped at regular intervals to measure the flank tool wear. Due to the time involved in tool wear measurement, cutting tool cooled on regular basis. A continuous cutting will provide much higher temperature values. Another important reason of low experimental temperatures can be the use of infrared cameras. Conradie et al. [30] also indicates the inaccuracies associated with the use of infrared camera for temperature measurement in machining experiments. To capture reliable measurements line of sight must be clear which is difficult in machining due to the flying chips coming out of the cutting zone. Similarly accurate calibration is required for emissivity keeping in view all of the variations in surrounding environment.

They have performed cutting experiments using three levels (185, 220 and 280m/ min) of cutting speed. Feed of 0.05 mm/ rev and depth of cut of 0.5 mm was used in experiments under dry conditions. To validate the reliability of finite element based model utilized in the current study simulations were executed on similar cutting conditions used in work by Zoya and Krishnamurthy [29]. It was observed that finite element based simulated model predicted values of cutting temperature were in good agreement with the work conducted by Zoya and Krishnamurthy [29] as shown in Figure 11. The simulated

Figure 9. Comparison of experimental and simulated cutting temperatures at feed of 0.1 mm/ rev

Figure 11. Comparison of experimental data from literature [6, 21] and simulated cutting temperatures at feed of 0.05 mm/ rev and depth of cut = 0.5 mm

cutting temperatures were found with prediction error of 1 – 10% with experimental [29] results.

CONCLUSION The conclusions drawn from the machining of Titanium alloy Ti6Al4V using TiAlN coated carbide inserts are as follows; 1.

It was observed that increase in the cutting speed reduces tool life significantly. Feed level has also direct influence on the tool life, as there was rapid reduction of tool life observed when feed level was changed from 0.1 mm/ rev to 0.3 mm/ rev. Highest tool life was observed at lowest cutting speed of 90 m/ min and low feed level of 0.1 mm/ rev. However, extremely short tool life was observed at higher cutting speed of 150 m/ min and feed level of 0.3 mm/ rev.

2.

It has been observed that at a specific feed level cutting forces remain at almost same level with increasing cutting speed. Increasing cutting speed forms high cutting temperature in the cutting zone which results in built-up-edge (BUE) formation and thermal softening phenomenon. Mainly due to

Figure 10. Comparison of experimental and simulated cutting temperatures at feed of 0.3 mm/ rev Simulated Cutting Temperature Comparison with Experimental Results from Literature To investigate the reliability of our FE model based cutting temperature predictions, experimental data from the work by Zoya and Krishnamurthy [29] has been verified.


these phenomena cutting forces tend to remain stable or lower at higher cutting speeds. 3.

The study reveals the trend of increasing cutting temperature with increasing cutting speed. Similar trend was observed with increase in feed level. Almost all of machining tests pointed out cutting

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5.

Infrared measurements were found highly inaccurate when compared with our FEM simulations and available literature. This is mainly due to problems in line of sight during machining operation and complex calibration required for emissivity for surrounding environment. The study used simulated cutting forces to predict energy involved in the cutting phase and power captured during the dry run provided idle energy component. By adding both components together total energy involved in the cutting process can be predicted without actually performing the cutting process. The energy predictions can further be incorporated to compute the environmental burden involved in the process.

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