The effects of coolant on the cutting temperature, surface roughness and tool wear in turning operations of Ti6Al4V alloy

Abstract The titanium alloys are widely used in aeronautical engineering and medical device materials due to exceptional mechanical properties such as tensile resistance and toughness of fractures. High thermo-mechanical loads occur in metal cutting of Titanium alloy Ti6Al4V, which can decrease life of cutting tool and increase cost of part production. In this paper, the coolant effects on the cutting temperature, surface roughness and tool wear are investigated by using the developed virtual machining system. The cutting forces during turning operations of Ti6Al4V alloy are accurately calculated in order to be used in calculation of cutting temperature and tool wear. The modified Johnson–Cook methodology is utilized to obtain the cutting temperatures along machining paths. Then, the Coupled Eulerian-Lagrangian (CEL) approach is investigated to predict and evaluate the effects of coolants on the cutting temperature in turning operations of Ti6Al4V alloy. The finite element approach is employed to predict tool wear by using the Takeyama–Murata analytical model and modifying the cutting tool geometry during the chip production process. To verify the developed methodology in the study, the results of experiments for the measured cutting temperatures, surface quality and wear rate are compared to the results of virtual machining system obtained by the finite element simulation. Thus, utilizing the proposed virtual machining system in the study, cutting temperatures, surface quality and tool wear during the turning operations of Ti6Al4V alloys with and without coolant can be accurately predicted to enhance the accuracy as well as productivity in the CNC machining operations.


Introduction
The Ti6Al4V alloy is the most widely used metallic alloy in the aerospace and medical industries due to properties of high strength-to-weight ratio and corrosion resistance.High mechanical and thermal loads are generated during machining operations of difficult-to-cut materials such as Ti-6Al-4V alloy, which can cause negative impacts on performances and life of cutting tool, tool wear rate and surface quality of the machined component (Singh et al. 2020).Excessive wear of cutting tool and hence short life of cutter are created due to generated heat in the cutting zone and dispersion of material expansion which can decrease the surface quality of machined components (Uzun et al. 2022;Shalaby and Veldhuis 2019).Tool wear has an impact on not only tool life, but also the quality of the finished product in terms of dimensional accuracy as well as surface integrity of machined components (Magalhães et al. 2022).Moreover, the ultimate surface roughness of machined components is one of the most essential factor in machining operations which should be investigated in terms of quality enhancement of machined parts (Hatefi and Abou-El-Hossein 2020).So, cutting temperatures, tool wear and surface roughness should be examined and reduced in order to improve performance of machining operations in processes of component production using machining operations (Gupta et al. 2021).
The Finite Element Method (FEM) is commonly used methodology in order to examine the generation and transfer of heat during machining operations.However, it is challenging to simulate the chip generation process with regard to the deformation in cutting procedures by using the typical Lagrangian formulation.Element distortion which are produced by severe strain during the chip formation process frequently cause negative element volume and node penetrations (Francis Ducobu, Rivi ere-Lorph evre, and Filippi 2016).As a result, due to material separation simulation by using the element degradation method, the usual Lagrangian formulation approach for chip fabrication requires the employment of a sacrificial layer as well as related material failure criterion (Francis Ducobu, Rivi ere-Lorph evre, and Filippi 2016).The approach decreases the mass and stiffness of workpiece, which can create significant simulation errors in terms of chip shape prediction during machining operations.Reasonable predictions of chip morphology can be made by using 3D Lagrangian FEM models according to the method of continual remeshing (Gao, Hoon Ko, and Lee 2018a).However, cutting forces during machining processes are not precisely modeled which cause the remeshing methods are mainly depend on the developed methodology in each study.During the FEM analysis process, the produced mesh in the Arbitrary Lagrangian-Eulerian (ALE) approach is not attached to the material (Gao, Hoon Ko, and Lee 2018a).Because, the ALE technique does not allow for element separation and blanks are also not permitted during the process simulation (Gao, Hoon Ko, and Lee 2018a).The cutting procedures should begin with an initial chip in the ALE approach which makes it difficult to be used in 3D simulation and analysis (Gao, Hoon Ko, and Lee 2018a).Furthermore, the ALE approach contains brick components with decreasing integration, which cause challenges through mesh generation process of intricate structure of chips with three dimensions using tetrahedral pieces (Gao, Hoon Ko, and Lee 2018a).
In Coupled Eulerian-Lagrangian (CEL) formulation and simulation, the workpiece is modeled as assigned material in an Eulerian domain, and the cutting tool can be modeled as a Lagrangian component (Francis Ducobu, Rivi ere-Lorph evre, and Filippi 2016;Ducobu et al. 2017).The Lagrangian and Eulerian analysis techniques can be combined in the same model which is frequently referred to as CEL analysis (Khochtali et al. 2021).It is generally utilized in order to expand the simulation capabilities of ABAQUS software in order to concurrently address fluid structure interaction and represent a class of problems where the interactions between a solid body and fluids are important to be predicted and analyzed (Khochtali et al. 2021).It allows the user to selectively represent stiffer bodies with more effective Lagrangian elements and high/extreme deformation bodies with Eulerian components (Khochtali et al. 2021).
The effects of machining parameters such as cutting speed and feed rate are investigated by Usca et al. (2021) to increase the surface quality and cutting tool life during turning operations of Cu-B-CrC composites materials.The experimental works are implemented in the study in order to obtain effective parameters and conditions on the tool wear rate and cutting temperature in terms of surface quality and cutting tool life enhancement.Experimental works at three cutting speeds (50, 75 and 100 m/min), fixed cutting depth (0.5 mm) and feed rate (0.12 mm/rev) are implemented by Yıldırım et al. (2020) in order to obtain the wear, surface roughness/topography and chip morphology during machining operations of Ni-based alloy 625 under MQL, cryogenic cooling and CryoMQL.Cutting tool wear, energy consumption, and surface roughness during turning of inconel 718 is experimentally investigated by Khanna et al. (2020) in order to enhance surface quality and material removal rate during turning operations.The obtained results of study proved that cryogenic turning uses up to 8-17% less energy during machining operations when the results are compared to dry and wet turning operations.Also, The Ra values of surface roughness for cryogenic turning operations are decreased by up to 20-37% in contrast to dry and wet turning at different material removal rates.Comparison between cryogenic coolants effect on tool wear and surface integrity in finishing turning of Inconel 718 is presented by Chaabani et al. (2020) to increase cutting tool life during machining operations of difficult-to-cut materials.
Vapor compression assisted cooling system is utilized by Ara ujo et al. ( 2019) in order to investigate the effects of cutting fluid usage on the surface roughness and cutting tool wear during turning operations.Sustainable cooling strategies is experimentally investigated by Airao et al. (2022) in order to reduce tool wear, power consumption and surface roughness during ultrasonic assisted turning of Ti-6Al-4V.The experiments of the study are performed at different process parameters as cutting speeds, feed, depth of cut, frequency and vibration amplitude to analyze the effects of cooling methodologies on the tool wear, power consumption and surface roughness during turning operations.
Influences of coating thickness on cutting temperature for dry hard turning Inconel 718 with PVD TiAlN coated carbide tools in initial tool wear stage is experimentally investigated by Zhao and Liu (2020) to decrease the cutting temperature and tool wear during turning operations.Xu et al. (2021) investigated the milling simulation of Ti6Al4V using a coupled Eulerian-Lagrangian technique and a compositional model considering the state of stress to enhance efficiency in milling operations of hard-to-cut metals.To simulate and improve friction stir processing, Ansari et al. (2019) have developed a combined Eulerian-Lagrangian finite element model.Also, Gao, Hoon Ko, and Lee (2018b) developed 3D Eulerian finite element modeling of end milling operations in order to simulate and analyze cutting procedures in digital environments.The coupled Eulerian-Lagrangian (CEL) formulation in Abaqus/Explicit is implemented by Agmell et al. (2020) in order to investigate thermo-mechanical stresses in pcBN cutters through milling processes of Inconel 718 components.A comprehensive examination of cutter life, surface quality and rate of tool wear, carbon emissions and production costing during turning operations of Ti-6Al-4V titanium alloy was performed by Agrawal et al. (2021) in order to decrease the effects of part production process on the environmental pollution.
The influence of cryogenic coolants on tool wear and surface quality during the turning operations of Ti6Al4V Alloy was compared by Chaabani et al. (2020) in order to analyze the effects of different coolant materials on the roughness of surface and wear rate of cutter during turning operations.Deep learning methodology was used by Ma et al. (2021) to forecast tool wear in milling TC18 titanium alloy.Tool wear prediction model based on the force signal with a convolutional bi-directional long short-term memory networks and a convolutional bi-directional gated recurrent unit is developed in the study in order to accurately predict the tool wear during milling operations.The impact of incremental tool wear on surface characteristics and chip formation in micro-milling operations of Ti-6Al-4V is investigated by Wang et al. (2020) in order to increase productivity in CNC turning operations.The obtained results of the study showed that the amounts of plastic side flow and metal debris were more severe as the tool wear progressed, and the quality of machined surfaces due to the up-milling side operations was better than the down-milling side operation.
Analysis of tool wear and chip morphology in Titanium Alloy Ti-6Al-4V dry trochoidal cutting operations is investigated by D. Liu et al. (2019) to provide the green production in turning operations.Tool wear and surface morphology in turning of Al 7075-T6 alloy using mixed cooling-lubrication techniques is analyzed by Gupta et al. (2019) to decrease the machining cost and increase productivity in part production process using turning operations.Machining efficiency and tool wear assessment during the end milling of Ti-6Al-4V alloy is investigated by Sivalingam et al. (2018) to increase the machineability of the titanium alloys using milling operations.The experimental results showed that inserts that have undergone cryogenic treatment exhibit higher machinability and enhanced tool life in comparison to untreated inserts under a certain set of working circumstances.Soori, Arezoo, and Habibi (2017, 2014, 2013, 2016) presented virtual machining methodologies in order to assess and improve CNC machining operations in virtual environments.Applications of virtual machining systems in milling operations are developed in the studies in order to simulate and analyze the dimensional, geometrical and tool defection errors in virtual environments in terms of accuracy as well as efficiency enhancement of part manufacturing processes.
A numerical model based on the finite difference approach is proposed by Lazoglu and Altintas (2002) to predict tool and chip temperature fields in continuous machining and timevarying milling operations.To enhance accuracy during five-axis flank milling of thin-walled parts, dimensional surface form errors regarding the combined tool and flexible part deflections is predicted by Li et al. (2018).A generalized modeling of chip shape and cutting forces in multipoint thread turning operations is presented by Khoshdarregi and Altintas (2015) in order to accurately estimate the cutting forces in the insert coordinate system, and integrate them along the engaged teeth during turning operations.Monitoring of in-process force coefficients and tool wear is proposed by Y.-P.Liu, Murat Kilic, and Altintas (2022) in order to accurately predict the cutting forces during turning operations.To enhance accuracy as well as efficiency in process of turn-milling operations, the cutting forces and chip thickness distribution is modeled by Comak and Altintas (2017).Multilayered feed-forward neural network is proposed by Q. Liu and Altintas (1999) in order to provide on-line monitoring of flank wear in turning operations.
The analysis of previous research indicates that utilizing a virtual machining system to simulate and predict the effects of coolants on cutting temperature, tool wear, and surface roughness in turning processes has not been investigated.The applications of the virtual machining systems are not applied to the turning operations in order to analyze the effects machining conditions on the quality of machined components in terms of accuracy as well as efficiency enhancement of part production process.The developed virtual machining system in the study is novel which can predict the cutting tool wear during turning operations based on the cutting forces calculation for each position of cutting tool along machining paths.The geometry of the cutting tool is subsequently modified during finite element modeling of cutting tool in order to obtain amount of tool wear using the calculated cutting forces at each point of the cutting tool throughout turning operations.The developed procedure of cutting tool geometry modification is continued in order to obtain the final shape of cutting tool regarding the tool wear during turning operation.Moreover, the FEM methods are not applied to the virtual machining systems in order to predict and analyze the influences of coolant on the cutting tool wear and temperature during chip formation process in turning operations.Most of the proposed research works in analysis of coolant effects on cutting temperature, tool wear, and surface roughness are investigated based on experimental works which can decrease the efficiency and flexibility of the studies in comparison to the virtual machining systems regarding to the time, cost of experiments and flexible conditions of workpiece materials and machining operations.As a result, in order to increase accuracy as well as efficiency of component manufacturing utilizing the applications of virtual machining systems in turning operations, the research work is original and new methodology.
In order to reduce cost and time of part production, virtual simulation offers a powerful device for creating and assessing components in digital environments (Soori, Arezoo, and Habibi 2014).The aim of virtual machining system is to simulate the process of part production in virtual environments in order to enhance accuracy as well as efficiency in component manufacturing process (Soori, Arezoo, and Habibi 2014).So, It is possible to reduce testing and trials on the shop floor by applying virtual machining systems to the manufacturing operations in terms of productivity enhancement of part production using machining operations (Soori, Arezoo, and Habibi 2014).
An advanced virtual machining methodology is proposed in the research work in order to study the effects of coolant on cutting temperature, surface finish and wear rate of cutting tool in turning operations of Ti6Al4V alloy.A Finite Element Method (FEM) analysis of orthogonal cutting is applied to calculate the cutting temperature of the cutting tool and workpiece with and without coolant.The CEL approach is used in this study to predict and evaluate the effects of coolant on the generated heat in Ti6Al4V alloy turning operations.The finite element technique is then used to estimate tool wear throughout the chip formation process, using the Takeyama-Murata analytical model and modifying the cutting tool geometry.The surface roughness along the turning operations of Ti6Al4V Alloy is predicted using the developed virtual machining system.To examine the validity of the presented methodology in the study, a sample workpiece is produced by utilizing the CNC turning machine tool Okuma LU3000EX.The FLIR T640-15 infrared camera is then used in order to measure the cutting temperature during the experimental works.To determine the surface roughness of machined part, the surface roughness tester kairDa KR210 is used.The SOIF XJP-2B optical microscope is also used in order to measure the tool face and flank wear in the experiments.Finally, the experimental as well as predicted results by the virtual machining are compared using the diagrams and tables.As a result, the proposed virtual machining methodology can increase accuracy as well as efficiency in process of turning operations by predicting and analyzing the influences of the coolant usage on the cutting temperature, tool wear and surface roughness of machined components.
The simulation and calculation of machining forces is presented in Section 2. The Jonson-Cook Model for the Ti6Al4V alloy is described in the Section 3. The surface roughness and wear of cutting tool models are explained in the Sections 4 and 5 respectively.The proposed virtual machining methodology to analyze the influences of coolant on the cutting temperature, roughness of surface and wear rate of cutter is presented in Section 6.The experimental works and simulation of the turning operations are presented in the Section 7. Finally, the results of research work are presented in Section 8.

Cutting forces in turning operations
To provide cutting operations along the process of chip formation, the cutting forces are as feed force, tangential force and radial force in turning operations.The cutting forces during chip formation process of turning operations are feed force, F f , tangential force, F t , and radial force, F r (Atabey, Lazoglu, and Altintas 2003).Figure 1 shows three cutting forces components in orthogonal cutting operations.where, the cutting forces of F f , F t and F r are feed force, tangential force and radial force respectively.Region definition and configuration for the uncut chip area is shown in the Figure 2.where, c is angle of L2 line in the region 2 of uncut chip area and vertical axis.Also, h is the angle of final points of uncut chip area in the region 1 and horizontal axis.Moreover, dh and A 1,i are the differential elements of angle of h and related area in the region 1 respectively.The location of each differential element on the uncut chip area can be presented by (Atabey, Lazoglu, and Altintas 2003), (1) Where, r e is the radius of each differential element on the uncut chip area and h is shown in the Figure 2. So, the ultimate equation for calculating chip thickness is derived as (Atabey, Lazoglu, and Altintas 2003).
where x, y are the location of each differential element on the uncut chip area.The r 1 is radius of the arc1 in the Figure 2 which can be expressed as (Atabey, Lazoglu, and Altintas 2003), The r 2 is radius of the arc2 in the Figure 2 during the chip creation procedure which can be explained as (Atabey, Lazoglu, and Altintas 2003), where, f is feed rate during chip formation process.So, the differences can create the thickness of chip (h) as (Atabey, Lazoglu, and Altintas 2003), where r e is the radius of each differential element on the uncut chip area, f is feed rate during chip formation process and h is shown in the Figure 2. All differential components are discretely added together to obtain the overall chip area in cutting region.The differential elements were modeled as tiny rectangles with 0.001 increments.The following are the area's borders as (Atabey, Lazoglu, and Altintas 2003), A rectangle has been specified as the chip creation in the region2, which can be modeled as (Atabey, Lazoglu, and Altintas 2003), where the jBCj and jBJj are shown in the Figure 2. The region3 in chip formation process is simulated as a simple triangle with the area as (Atabey, Lazoglu, and Altintas 2003), where the jABj and jAJj and c are shown in the Figure 2. So, the total area is the summation of the three areas as (Atabey, Lazoglu, and Altintas 2003), By summing the lengths of the cutting edges in the cutting areas and the final area, the overall length of cutting-edge which is involved in the cutting operations of workpiece can be estimated as (Atabey, Lazoglu, and Altintas 2003).
where L c1 and L c2 are the contact lengths in regions 1 and 2 of Figure 2 respectively.So, cutting forces in differential format can be presented as (Atabey, Lazoglu, and Altintas 2003), where, dA is function of local chip area and dLc is contact length of chip cutting edge.The edge cutting force coefficients (Kte, Kfe and Kre) are dependent on the cutting-edge condition and preparation.The cutting force coefficients (Ktc, Kfc and Krc) are dependent on the local rake, inclination, chip flow angles, cutting conditions and work tool material properties.The coefficients of cutting forces can be determined by using the experimental results.To calculate cutting forces during turning processes, coefficients of cutting forces are obtained from experimental works (Atabey, Lazoglu, and Altintas 2003).

Jonson-Cook model
The Johnson-Cook model is utilized to quantify the flow stress of material as a combination of strain impacts, effects of generated heat and rate of strain due to theoretical simplicity and great accuracy.The three variables explain the impact of hardening via strain, rate of hardening via strain, and heat relaxation on the stress of the flow of the component during deformation.As a result of adaptability of the method in FEM analysis, the method is applied to evaluate the different materials' deformation propensities.The Johnson-Cook model is (Ji et al. 2015).
where e is equal strain of plastic _ e and _ e0 are the equal and basis strain rates of plastic, T, T m , and T 0 are the temperature of cutting area, temperatures of melting and experimental room respectively.The m is index of heat softening, while the strain hardening index is N. A, B, and C are the material's rate of strength of yield, strain, and sensitivity of strain, respectively.
The Johnson-Cook model assumes that the three influencing components of strain, strain intensity, and temperature are entirely independent of one another, eliminating the accumulating effect of any influence component.Using the standard J-C constitutive model, such strain rate dependency is difficult to predict.The modified Johnson-Cook model investigates the linked impacts of temperature of deformation and strain rate on flow stress, considerably enhancing the model's prediction accuracy over the original Johnson-Cook model (He et al. 2013).In order to solve the limitations of Johnson-Cook model, the modified Johnson-Cook model is presented as Equation (( 15)) (Lin and Chen 2010), where parameters D and P are introduced to describe effect of dynamic recrystallization and recovery mechanisms on flow stress respectively.The parameters of D and P can be calculated as the Equation (( 17)) and Equation (( 18)) respectively,

Surface roughness model
To present the model of surface roughness during turning operations of titanium alloy Ti6Al4V (grade-5), the response surface methodology is used by Ramesh, Karunamoorthy, and Palanikumar (2012).So, the Surface roughness model of titanium alloy Ti6Al4V (grade-5) can be presented as (Ramesh, Karunamoorthy, and Palanikumar 2012), where, v c is cutting speed, a p is depth of cut and f is feed rate.

Tool wear model
To simulate the rate of cutting tool wear considering the cutting temperature, the Takeyama-Murata's model is presented as (Takeyama and Murata 1963), where, A is a pre-exponential constant, E a is the activation energy, R is the constant of universal gas, and T is temperature of cutting zone in degrees Kelvin.Feed rate and sliding speed are considered to calculate the abrasive term.According to the model, contact normal stress and sliding speed between cutting tool and chip are related to the tool wear rate as (Takeyama and Murata 1963), where, A is a pre-exponential constant, r n is contact stress which is normal, and t s is speed of sliding between cutting tool and chip during chip formation process.Also, B indicates the cumulative temperature-dependent due to the thermal softening and wear asperities diffusion into the workpiece, as well as the chance of a wear particle forming.As a result, similar to Takeya-ma's and Jiang's model, an Arrhenius-type model is employed and simplified to either of the replaceable wear/time or wear/distance versions as (Takeyama and Murata 1963), and where, A is a pre-exponential constant, w is unit of volume, s is sliding distance, R is the constant of universal gas and E a is activation energy.
6. Virtual machining system to analyze the influences of coolant on the cutting temperature, surface roughness and tool wear A virtual machining system is proposed in the study in order to investigate the effects of coolant during titanium alloy Ti6Al4V turning operations.The virtual machining system is developed in the research work by using the Visual Basic programming language.Original G codes, the cutting tool specifications and geometries, coolant conditions as fluid specifications and pressure and the workpiece CAD model are input data to the system.Then, based on cutting tool data and machining process variables, cutting forces along machining paths can be determined using the developed virtual machining system in the study.By using the calculated cutting forces along machining paths, the amounts of e as equivalent plastic strain, _ e and _ e0 as the equivalent and basis plastic strain rates are then calculated.As a result, the modified model of Johnson-Cook for the titanium alloy Ti6Al4V as Equation (( 16)) is used to obtain the information of cutting temperature (T) during turning operations.To calculate the cutting temperature during the chip formation process, the developed virtual machining system is coupled to the FEM analysis software of Abaqus R2016X.The CAD model of the component is then mesh-generated in order to implement the finite element analysis on the simulated parts in virtual environments.In the next step, the modified Johnson-Cook methodology is utilized to obtain the cutting temperatures along machining paths.Finally, the CEL methodology is implemented to simulate and evaluate the coolant effects on the cutting temperature during chip formation process.The virtual machining system flowchart in analysis of coolant usage impacts on the workpiece and cutting tool temperatures along different machining pathways is shown in Figure 3.
The cutting tool angles and geometry, such as rake, flank, and cutting-edge nose, are determined using the finite element approach.The values of cutting tool wear are then determined using the Takeyama-Murata analytical model.In order to obtain values of tool wear using the calculated cutting forces at each point of the cutting tool throughout turning operations, the geometry of the cutting tool is subsequently modified during finite element modeling of cutting tool.The developed procedure of cutting tool geometry modification is continued in order to obtain the final shape of cutting tool regarding the tool wear during turning operation.As a result, the geometry of cutting tool is modified in order to reflect the calculated values of tool wear at each location of cutting tool during turning operations.The procedure is repeated in order to achieve a new cutting tool geometry which can accurately predict the tool wear during turning operations.Figure 4 shows the flowchart of the virtual machining system for predicting cutting tool wear.
Furthermore, the created virtual machining system can use machining parameters and cutting conditions to determine the surface roughness of machined surfaces.As a result, the developed system can determine the size of surface roughness in machined components, which can then be investigated and minimized.Thus, the developed virtual machining system in the study can predict and analyze the impact of coolant consumption on cutting tool and workpiece temperatures, wear of cutting tool, and surface roughness of machined components during turning operations.

Simulation and validation
To validate the proposed virtual machining system in the research work, the Okuma LU3000EX CNC lathe is used.The used cutting tool holder is ACLNL2525K12-A which is double-clamp toolholder with 95 approach angle for negative 80 rhombic inserts.The used insert in the experiment is carbide CNMG120408-HRM AH8015.The used insert and tool holder dimensions are shown in Figure 5.
The sample workpiece material is titanium alloy Ti6Al4V.The cutting parameters are as speed of cutting 60 m/min, depth of cut ¼ 1.5 mm and feed rate 0.2 mm/rev.The used coolant in the turning operations is composed by 95% of water and 5% of a synthetic cutting fluid at 20 bar pressure.The specifications of used coolant are appearance blue, PH 9.3, specific gravity 1.05, boiling point (122 C, 251.6 F) and pour point (-18 C, 0 F).The experiments are repeated for 10 times in order to reduce the effect of errors in the obtained results.Figure 6 shows the experimental process of turning operation.
The conditions and parameters of research work experiment in the study is shown in the Table 1.
To obtain the cutting force coefficients of titanium alloy Ti6Al4V by using experimental methods advanced force measurement system as Multicomponent dynamometer type 9257B which is attached to the tool holder is used.As a result, by using the Multicomponent dynamometer type 9257B, cutting forces in each of the three Cartesian directions (X, Y, Z) are measured.Then, the coefficients of cutting force K tc , K te , K rc , K re , K ac and K ae for the titanium alloy Ti6Al4V are obtained as 1735.42, 24.9, 380.31, 44.3, 640.61 and 4.5 respectively.
The cutting forces along the turning operations can be obtained by using the E1. ( 11) in virtual environments.To simulate the cutting temperatures using Finite Element Method, the Abaqus R2016X FEM analysis software is used.The specific heat capacity of the titanium alloy Ti6Al4V is 0.5263 J/kg-K, thermal conductivity 6.7 W/m-K, modulus of elasticity 114 GPa and Poisson ratio 0.3 in the FEM simulation.Engineering simulations are built on the basis of generating the best possible mesh since it affects the simulation's accuracy, convergence, and speed (Feather, Lim, and Knezevic 2021).In this study, a configurable mesh density is employed in the simulations by using the smallest possible element size of 1 lm.During finite element simulations of turning operations, the sample part with shape of chips and a cutting tool are mesh generated and 235,000 and 95,000 tetrahedral elements are applied to the sample part and cutting tool respectively.When the model was divided into mesh elements, grid independence test is implemented in order to choose the best partition of the meshed part.So, the generated mesh on the cutting tool and workpiece are analyzed and modified in order to decrease the mesh generation errors during FEM simulation.In order to define contact between the cutting tool and workpiece during finite element simulation, the mechanical contact using penalty method is utilized.In penalty method, the penetration is introduced between the two contact boundaries and the normal  contact forces are connected to this penetration by the penalty parameters (Khoei and Taheri Mousavi 2010).Each slave node is examined by the penalty algorithm for master surface penetration.Typically, the master surface is the contact surface with the coarser mesh or the higher stiffness compared to its counterpart (Yang, Deng, and Li 2019).A finite element analysis of a turning process is shown in Figure 7.
The simulated cutting parameters are as time 0.8 s, cutting speed 60 m/min, feed rate 0.2 mm/rev and depth of cut ¼ 1.5 mm.Thus, the prediction of the cutting temperatures during the turning operation of titanium alloy Ti6Al4V without using the coolant by the Coupled Eulerian-Lagrangian method is obtained as shown in the Figure 8.
Then, effects of coolants on the prediction of cutting temperature during chip formation process of titanium alloy Ti6Al4V is obtained by using the Coupled Eulerian-Lagrangian method.The FEM simulation of temperature distribution in the cutting area during turning operations of titanium alloy Ti6Al4V with coolants is shown in the Figure 9.
To measure the cutting temperature during turning operations, the FLIR T640-15 infrared camera.The measuring range of þ100 C to þ650 C is selected in terms of measuring process of generated heat during machining operations.The accuracy of used infrared camera during measuring process is ±2 C (±3.6 F) or 2%.All trials were carried out with a temperature of reference     Figure 12 shows a comparison of virtual machining results and experimentally determined workpiece temperatures for Titanium alloy Ti6Al4V with coolant.
The measured and predicted cutting temperature without and with using the coolant by considering the machining time during the turning operations of Titanium alloy Ti6Al4V is presented in the Table 2.
In order to measure the surface roughness of machined part, the surface roughness tester kairDa KR210 is used.The measurement process of surface roughness of the machined sample workpiece is shown in the Figure 13.
Measured and predicted surface roughness of 10 selected points of the machined sample workpiece without and with using the coolant during the turning operations of Titanium alloy Ti6Al4V is presented in the Table 3.
The surface roughness of machined workpiece with coolant is decreased in comparison to the dry turning operations of Titanium alloy Ti6Al4V.The built-up edge (BUE) is generated during dry machining which can cause cutting tool inserts to drag over the surface of machined components and can decrease the surface quality of machined parts (Patel and Patil 2022).The coolant reduces cutting temperature in the contact zone of the cutting tool and workpiece and prevents the formation of BUE which can increase the surface quality of machined components.In order to obtain the amount of tool wear using the calculated cutting forces at each point of the cutting tool throughout turning operations, the geometry of the cutting tool is subsequently modified during finite element modeling of cutting tool using the developed virtual machining system in the study.The simulated cutting tool flank wear after 14 min of Titanium alloy Ti6Al4V turning operation is shown in the Figure 14.
The SOIF XJP-2B optical microscope is utilized to obtain the tool face and flank wear in the experiments.The microscope measurement range is 0-50 mm, the digital step of the microscope micrometer and the maximum permissible error during measuring process are 0.001 mm and ±3 mm respectively.The method for measuring the cutting tool wear during turning operations is microscopic visualization based on the ISO standard for tool life testing 3685:1993.The places of flank wear and face wear in the cutting insert (A), the optical microscope image of turning     The measured and FEM-simulated cutting tool flank wear in turning operations of Titanium alloy Ti6Al4V without using the coolant is shown in the Figure 17.
Diffusion caused by high temperature in the interference zone was discovered as the main cause of tool wear mechanism during machining operations of the titanium alloy Ti6Al4V (Ezugwu et al. 2007).Thus, the cutting temperature during turning operations of Titanium alloy Ti6Al4V is decreased by using the coolant which can reduce the tool wear in chip formation process.

Conclusion
A virtual machining system is developed in the study in order to predict the cutting temperatures, roughness of surface and wear of cutter in turning operations of Titanium alloy Ti6Al4V.The effects of coolant consumption on cutting temperature, roughness of surface and wear of cutter are modeled and experimentally validated in order to be enhanced.The modified Johnson-Cook modeling approach is used in metal cutting calculations in order to predict the chip formation process by evaluating the linked impacts of strain rate and temperature of deformation on yield stress without and with coolant.To predict and assess the cutting temperature in turning process of Ti6Al4V alloy, the CEL method is used.To calculate the cutting temperature of the workpiece and cutting tool throughout the chip formation process, FEM simulation of turning operations is  applied.Using the Takeyama-Murata analytical model and altering the cutting tool geometry during the chip creation process, the finite element approach is then utilized to predict tool wear during the chip formation process.The proposed virtual machining system is used to predict surface roughness of sample workpiece from Ti6Al4V Alloy during turning operations.The limitation of the study is fluid flow equations as well as CFD analysis using the numerical methods which can also be applied to virtual machining systems.The CFD equations can be used in order to examine the impacts of internal cooling systems on cutting temperature, surface roughness and tool wear during turning operations.The effects of internal cooling parameters such as coolant channel shapes and size, coolant pressure and velocity and coolant spray locations to the turning cutting tool can be analyzed using the CFD equations in order to enhance efficiency of internal cooling systems of turning cutting tools in advanced machining operations.So, the coolant flows in the machining operations can be analyzed and modified in order to enhance the coolant flow effects in terms of cutting temperature, surface roughness and tool wear reduction during turning operations.To verify the proposed system in the study, cutting temperatures, roughness of surface and wear of cutter of machined parts during turning operations of Titanium alloy Ti6Al4V with and without coolant are compared with the predicted results calculated by the developed virtual machining system.The following summarizes the findings from the study, 1.By comparing the results of virtual machining system and experimental cutting temperatures, 84.49% and 88.17% compatibility are obtained for the without and with cooling conditions respectively.2. 88.5% and 88.7% compatibility are obtained for the measured and predicted surface roughness of the machined sample workpiece without and with using the coolant during the turning operations of Titanium alloy Ti6Al4V.3. The measured and predicted tool wear are also compared by the diagram and a 91.2% compatibility are then obtained in comparison between the experimental and virtual machining system results.4. Cutting temperature is decreased by 19.05%, surface quality is increased by 9.37% and cutting tool wear is decreased by 17.87% due to using the coolant during turning operations of Titanium alloy Ti6Al4V. 5.The proposed virtual machining system in the study has the potential to reduce experimental testing and economic waste by analyzing and minimizing cutting temperatures, tool wear and surface roughness during metal cutting operations of hard-to-cut materials in virtual environments.6. Advanced cutting tool inserts with modified cutting tool angles and materials can be presented due to analysis of cutting temperate and tool wear using virtual machining systems.7. The effects of coolant variants and materials on the cutting temperature, surface roughness and tool wear in turning operations can also be considered in order to increase the cutting performances during turning operations.This is concept of future research work of the authors.

Figure 2 .
Figure 2. Region definition for the uncut chip area.

Figure 3 .
Figure 3. Flowchart of virtual machining in analysis of coolant usage impacts on the workpiece and cutting tool temperatures during turning operations.

Figure 4 .
Figure 4.The flowchart of the virtual machining system to predict cutting tool wear.

Figure 5 .
Figure 5.The used insert and tool holder dimensions.

Figure 6 .
Figure 6.The experimental process of turning operation.

Figure 11 .
Figure 11.Comparison between virtual machining results and experimentally determined workpiece temperatures for Titanium alloy Ti6Al4V without coolant.

Figure 12 .
Figure 12.Comparison of virtual machining results and experimentally determined workpiece temperatures for Titanium alloy Ti6Al4V with coolant.
cutting tool face and flank wear insert after 14 min of turning operation of Titanium alloy Ti6Al4V in flank wear (B) and in face wear (C) are shown in the Figure15.The measured and FEM-simulated cutting tool flank wear in turning operations of Titanium alloy Ti6Al4V without using the coolant is shown in the Figure16.

Figure 13 .
Figure 13.The measurement process of machined sample workpiece surface roughness.

Figure 15 .
Figure 15.The places of flank wear and face wear in the cutting insert (A), the optical microscope image of turning cutting tool face and flank wear insert after 14 min of turning operation of Titanium alloy Ti6Al4V without using the coolant in flank wear (B) and in face wear (C).

Figure 16 .
Figure 16.The measured and FEM-simulated cutting tool flank wear in turning operations of Titanium alloy Ti6Al4V without using the coolant.

Figure 17 .
Figure 17.The measured and FEM-simulated cutting tool flank wear in turning operations of Titanium alloy Ti6Al4V with using the coolant.

Table 1 .
The conditions of research work experiment.

Table 2 .
The measured and predicted cutting temperature without and with using the coolant regarding the machining time during the turning operations of Titanium alloy Ti6Al4V.

Table 3 .
Measured and predicted surface roughness of 10 selected points of the machined sample workpiece without and with using the coolant during the turning operations of Titanium alloy Ti6Al4V.