Studied validation of finite element modeling for titanium alloy (Ti–6Al–4V) at face milling

Abstract

Background

Due to its qualities, There is a lot of use of the titanium alloy (Ti–6Al–4V) in gas turbines and other aero engines. It is difficult to determine machining parameters such cutting force, temperature, and surface roughness, and it is difficult to calculate these values using analytical methods. The finite element method (FEM) is a particularly useful platform for studies since it predicts the machining parameters.

Methods

The advantage of this method is taken for the purpose of linking the mechanical and thermal equations based on a step of the dynamic, temperature-displacement, explicit of the Lagrangian formulation in a new model that is fully thermomechanically connected. Three mesh areas were created for optimizing the cutting zone during the cutting simulation process. The machining process by using a face milling tests was carried out. There are two variable factors of cut such as feed rate, cutting speed are created randomly when depth is a constant parameter. High-speed camera used to capture the machining process which determines the important details of spark generated.

Findings and interpretation

There is a very excellent agreement between the experimental data and the simulation results from the finite element modeling (FEM). By raising the feed rate in the cutting zone, the cutting temperature can be raised and a spark can be generated. This led to the conclusion that surface roughness trends can be predicted using feed cutting force measurements. Surface roughness rose in direct proportion to the magnitude of the feed cutting force, and vice versa.

1. Introduction

Due to its exceptional combination of high specific strength that is maintained at high temperatures, amazing resistance to corrosion, and fracture resistant qualities, Gas turbines and other aero-engines typically employ the titanium alloy (Ti–6Al–4V). Titanium alloy (Ti–6Al–4V) milling is frequently used in the aerospace industry, and it is also more popular in other business and industrial settings, including as the military, medical, and racing sectors [1].

High speed machining techniques are appealing to the expanding industry because they provide greater rates of material removal and may also have a favorable impact on the material's characteristics in the finished work-piece. The fact that the specific cutting pressures for the majority of materials significantly drop with rising cutting speeds before plateauing is a particularly alluring aspect of the high speed cutting process [2].

Cutting force is considered as a key standard to evaluate the machining performance process, since it is directly related to machined surface quality, tool wear, cutting temperature, etc. [18], [19], [20]. Due to its benefits of reduced cutting forces in machining aforementioned brittle materials. A series of experiments are conducted to verify the developed model through the comparisons between predicted and experimental values of cutting forces [17]. Therefore, cutting at fast rates is permitted by current cutting tools, increasing the amount of chips removed in a given amount of time. This leads to a quick metal removal rate and necessitates improved management of the system of the machining process system composed of the combination of the cutting tool and machine tool and the workpiece. In the investigation of greater cutting speeds, the materials of the cutting tool are more heavily weighed [3]. Additionally, Increased cutting speed, feed rate, and depth of cut are frequently necessary throughout the high-speed machining operation in order to maintain heat, pressure, chip flow, and finally surface polish [4], [5], [6].

One of the major risks when temperature rise and thermal shock, which result in poor surface quality, heat generation in the cutting zone appearance of the spark and chip burr formation because of higher thermal stress on the cutting zone due to lower heat dissipation from the chips and workpiece. Adhesion and diffusion processes are enhanced, high temperature gradients happen whereby thermal stress emerges. Many researchers have work on the thermal shock such as Abukhshim et al. [21] present a thermal imaging camera considering the measurement of temperature during high-speed machining of super-alloys. Richardson et al. [22] estimated the temperature by utilizing an analytical process in dry milling operation through the analytical movable heat exporter method. On the other hand, Abukhshim et al. [23] explained that the prediction of temperature stays challenging. However, the reduction in cutting temperature distributions leads to an increase in tool life and a decrease in wear rate but a reduction in the cutting temperature of the workpiece can be increased shear stress. Therefore, the cutting force may be increased, and then may cause a decrease in tool life. Sharma [24] focused on the tool life how can be enhanced by means avoided tool failure during the classical turning operation.

Hence, prediction the machining response parameters such as cutting temperature during different machining process of titanium alloys are led to improve the surface finish quality and considered a very important in the aircraft industry because titanium alloys are widely used in the aerospace industry. The values of machining parameters including cutting force, temperature, and surface roughness are challenging to quantify and to compute using analytical techniques. On the other hand, statistical method for design of experiments such as Box–Behnken designs [25], [26] is considered a successful technique to perform different combinations of design variables contain the for instance, feed rate, cutting speed, and depth of cut [27].

In fact, conventional methods can be used to extract the necessary machining parameter values, but doing so requires a lot of time and money due to the multiple empirical procedures that lead to trial and error Instead of examining the overall effectiveness of the machinability and testing facilities, the study has concentrated on specific issues related to the titanium alloy's poor machinability (Ti–6Al–4V).

Numerical simulation methods are extremely important for the understanding of the machining process and for the elimination of needless experimental testing due to the optimization of cutting conditions, tool geometries, and other cutting parameters that is employed in numerical approaches [7]. Hence, the aircraft industry uses the finite element method to determine the static and dynamic analysis of machining parameters by the great variety of environments and conditions that can be found during their cutting operation. In order to produce predictions and/or optimize specific machining parameters such cutting force components, temperature, surface roughness, stress-strain components, and others analysis, researchers are concentrating on simulation methodologies through modeling analysis. Additionally, these procedures don't necessitate a lot of expensive and time-consuming experimental studies. As a result, the finite element method (FEM) is a very useful tool for researchers since it can forecast the machining parameter values, which results in an estimate of the usual titanium alloy machining performance (Ti–6Al–4V).

The primary goal of this study is to design a model utilizing the finite element technique (FET) and apply it to the ABAQUS/EXPLICIT software to be able to forecast cutting parameters such cutting force, temperature, and surface roughness when machining titanium alloy (Ti–6Al–4V) at face milling.

2. Finite element method (FEM)

The cutting simulation approach used in this work has yielded a wealth of knowledge on the machining behavior of titanium alloy, allowing for a better understanding of the machining of titanium alloy during face milling operations (Ti–6Al–4V). Analytical evaluations of the cutting parameters of the orthogonal cutting process are performed using a new model. The advantage of this technique is used to couple the mechanical and thermal equations based on a step in the dynamic, temperature-displacement, explicit Lagrangian formulation. Then, the model employs a completely thermo-mechanically coupled approach. Based on Pittalà and Monno's [10] use of the orthogonal machining technique in the face milling operation of aluminum, this method was employed. The uncoated cemented carbide milling insert tool geometry and titanium alloy workpiece (Ti–6Al–4V) have been developed and deployed in the ABAQUS/EXPLICIT program. Using a square tool made of uncoated cemented carbide and a workpiece measuring 60 mm by 100 mm, the titanium alloy (Ti–6Al–4V) is machined. When running the simulation module, the cut length is 70 mm. The outcomes of using the Johnson-Cook plasticity model to analyze the properties of the titanium alloy are shown in Table 2. (Ti–6Al–4V). The tool geometry, which is employed in the research under the presumption that it is a discrete rigid, is developed in compliance with the shape inserts made by KENNAMETAL manufacturers. The physical requirements for the cutting tool, however, are shown in Table 3. The flow stress () was calculated using the Johnson-Cook model's constant parameters, as indicated in Eq (1). Additionally, Lesuer is used to determine the failure parameters, which are d₁ = −0.09, d₂ = 0.25, d₃ = −0.5, d₄ = 0.014, and d₅ = 3.87. Hence, the failure strain (ε_f) is itemized in Eq. (2) as given below:

$$\sigma =\left(A+B{\epsilon }_p^n\right)\left(1+C\ln \left(\dot{\epsilon }/{\dot{\epsilon }}_0\right)\right){(1-\left(\frac{T-T_r}{T_m-T}\right)}^m)$$ (1)

$${\epsilon }_f=\left(d_1+d_2{e}^{d_2\left({\sigma }_{\rho }/{\sigma }_e\right)}\right)\left(1+d_4\ln \left({\dot{\epsilon }}^p/{\dot{\epsilon }}_0\right)\right)\left(1+d_5\left(\frac{T-T_r}{T_m-T}\right)\right)$$ (2)

Table 2.

Chemical composition of titanium alloy (Ti–6Al–4V).

Material	Ti %	Al %	V %	Fe %	O %	C %	N %
Ti–6Al–4V	Balance	6.01	3.87	0.18	0.14	0.009	0.006

Table 3.

Physical parameters of the work-piece for titanium alloy (Ti–6Al–4V) [8], [9].

Physical parameters	Values
Density, ρ (kg/m3)	4428
Young’s modulus (GPa)	113.8
Poisson’s ratio	0.342
Specific heat, Cp (J/kg°C)	670
Conductivity of heat, λ (W/m°C)	6.6
Expansion Coeff., (μm/m/°C)	9
T room (°C)	25
T melting (°C)	1605
Heat fraction inelastic (β)	0.9

where σ is the reference strain rate (1/s), is flow stress, and are strain and strain rate, and n, m, A, B, and C are constant parameters for the Johnson-Cook material models depicted in Table 1. (ε^ρ/ε_o) depends on non-dimensional plastic strain, a dimensionless pressure stress ratio (σ_p/σ_e) (where σ_p is the pressure stress and e is the stress (Von-Mises)), the temperature of the workpiece (T), the room temperature (T_r), and the melting temperature (T_m) [11].

Table 1.

Constant parameters for Johnson-Cook plasticity model of titanium alloy (Ti–6Al–4V) [12].

Constant parameters	Values
A (MPa)	987.8
B (MPa)	761.5
n	0.41433
m	1.516
C	0.01516
Reference strain rate (1/s)	2000

2.1. Assembly part

The engagement will occur in accordance with the marks made on the geometry of the tool and the workpiece. During the simulation phase, these marks will direct the cutting tool's course on the work-piece surface. In order to provide enough momentum when the tool edge interacts with the work-piece surface during the simulation process, a reference point must be defined in one of the side portions of the tool geometry, as illustrated in Fig. 1.

Fig. 1.

Work-piece and tool geometry layout.

2.2. Step module

A vital method section that outlines the simulation process is taken into account in the phase module. To compute the machining response parameters required for this investigation, including the cutting force, temperature, and surface roughness, a finite element modeling approach called dynamic, temperature displacement, explicit was utilized. In actuality, these answers are connected to and reliant on the user's selections of titanium alloy machining parameters (Ti–6Al–4V). The second sort of action is the module interaction, which takes place in the cutting zone between the titanium alloy workpiece (Ti–6Al–4V) and the cutting tools. In this regard, the penalty contact approach was utilized to combine the mechanical contact property of the friction formulation, with the type of contact being "surface-to-surface" (Explicit). The penalty contact approach was used to ascertain the tangential behavior of the friction coefficient value and is regarded as one of a particular class of algorithms for resolving constrained optimization problems. In addition, the friction model is employed while taking the shear constant model into account. On the basis of Pittalà's studies with face milling, the friction coefficient is considered to be 0.7.

2.3. Boundary conditions

In relation to boundary conditions, it was seen from Fig. 2 that a framework's properties are externally superimposed. The titanium alloy workpiece (Ti–6Al–4V) was fixed to the cutting tool's left side and bottom while moving horizontally from right to left. In addition, the discrete rigid body design of the tool geometry enables high-speed machining due to the decreased contact surface area between the tool edge and the work-piece surface. These hypotheses are therefore used to define the problem to be solved as well as the boundary and cutting conditions for cutting simulations, such as the cutting width being greater than the feed, which is referred to as a "plane strain" condition, and the cutting velocity vector being normal to the cutting edge. Additionally, the workpiece is made of the homogeneous polycrystalline, isotropic, and incompressible solid titanium alloy (Ti–6Al–4V). The simulation was started at a reference temperature of 25 °C, and the cutting zone friction coefficient between the workpiece and tool geometry was kept constant throughout the cutting process. Additionally, the model was developed by selecting the possibilities that can be illustrated as follows:

• 1."JOHNSON-COOK model" of plastic material hardening is employed.
• 2.The "JOHNSON-COOK model" was employed as the damage initiation criterion.
• 3.CPE4R is the chosen type of workpiece material element (4-node).
• 4.R2D2 for discrete stiff for a chosen type of tool element (2-node).
• 5.Elements has been selected as the tool geometry type.
• 6.Surface of workpiece chosen: NODE.
• 7.Select the special point at the reference point to find the mass per inertia for the tool geometry.
• 8.To determine the velocity and temperature during dry cutting, the step of dynamic, Temp-disp., explicit is used (fully thermo-mechanically coupled).

Fig. 2.

Boundary conditions for machining titanium alloy (Ti–6Al–4V).

2.4. Mesh elements

The grid of a finite element method was viewed as the module of the meshing elements (FEM). A multi-area on this model was created to maximize the contact surface area (cutting zone) between the workpiece and tool edge during the cutting simulation process, as illustrated in Fig. 8. Three mesh sections were built and generated for the titanium alloy workpiece (Ti–6Al–4V) during the simulation phase to avoid mesh distortion problems and speed up program execution. When the tool-tip passage zone was produced, area 1 was created by the element size of 0.2 μm, area 2 by the element size of 0.1 μm, and region 3 by the element size of 0.05 μm. In order to achieve exact data regarding the contact surface area between the cutting tool edge and work-piece of titanium alloy, the cutting zone in area 3 was mesh with the element size of 0.05 μm. (Ti–6Al–4V). Additionally, the element that was entirely damaged was deleted from the damage zone and will eventually be included in the chip formation throughout the cutting simulation process.

Fig. 8.

Feed cutting force measured during the cutting process.

The titanium alloy (Ti–6Al–4V) and tool geometry components are first added to the workpiece's edge by the mesh generator. As a result, a finer mesh was used to cover the cutting zone (contact surface area) between the material of the workpiece and the cutting tool edge. This is due to the fact that, as illustrated in Fig. 3, the chip formation would result in the creation of the primary and secondary shear stress deformation zone. For the titanium alloy workpiece, a total of 6645 different elements were used, 6840 nodes were included, and 13682 different variables were present in the model (Ti–6Al–4V). A rough element size of 0.2 μm was mesh for the tool geometry. By splitting the body into a limited number of pieces, the mesh settings assist in gridding the workpiece material and the cutting tool for smaller parts. As is common, there are three different sorts of element shapes included in the section on assigning mesh controls. For this investigation, a titanium alloy workpiece was machined using the element form "Quad" and a free dynamic explicit mesh control approach (Ti–6Al–4V). However, "CPE4R," which stands for a 4-node bilinear plane strain quadrilateral continuum element with reduced integration and enhanced hourglass control, is the element type control for a dynamic, temperature-displacement, explicit analysis.

Fig. 3.

Titanium alloy work piece with mesh components (Ti–6Al–4V).

3. Experimental work

3.1. Materials

Titanium alloy was used as the work piece material for the machining process (Ti–6Al–4V). Alloy of titanium (Ti–6Al–4V) was a square bar with dimensions of 100 mm × 100 mm x 500 mm. Table 2 lists the chemical makeup of titanium alloy. After the titanium alloy (Ti–6Al–4V) original workpiece was chopped into square blocks measuring 100 mm by 100 mm by 30 mm, It was employed to carry out these experimental studies during the machining procedure. Table 3 also displays the physical properties of the titanium alloy workpiece (Ti–6Al–4V).

3.2. Tools

Two inserts are held in place in this project by a typical face milling cutter made by Kennametal manufacturers. The face milling tests were conducted using uncoated cemented carbide tool inserts with Rz = 0.8 mm nose radius and 12.7 mm cutting width under the Kennametal manufacturer's code (K313). The cutter's diameter is 37 mm. Rake is 0o, whereas the clearance angle is 11°. The physical features of the cutting tool inserts are listed in Table 4. Additionally, experiments on milling were performed using a dry cutting OKUMA MX-45VA CNC milling machine.

Table 4.

The cutting tool's physical characteristics [8].

Tool Material	Values
Density, ρ (kg/m3)	15,700
Modulus of Young (GPa)	705
Poisson’s ratio	0.23

3.3. Measurements

Kistler, Type-5070A is available as 4-channels or 8-channels version with data acquisition software DynoWare “Type 2825A-02”. As an option, the multi-channel charge (4-channels) version for cutting force measurements and recording by a PC-based data acquisition system is used in this study. In addition, a non-contact thermometer like the Fluke Model 66 Infrared thermometer is employed. By measuring the amount of infrared energy emitted by an object's surface, this thermometer can determine the temperature of its surface. During the experiment, the machined surface roughness at each cutting pass was measured using a portable roughness tester, model TR200.

3.4. Experimental setup

The OKUMA MX-45VA CNC milling machine was used for the face milling tasks, which required dry cutting. When depth is a constant quantity, Table 5 two variable components of cut, such as feed rate and cutting speed, are generated at random when depth is constant by using Box–Behnken designs which is a statistical method for design of experiments. Each experiment's such as the blade, Dynamometer, and machining length was 70 mm, as illustrated in Fig. 4. The presentation of a crucial methodology for machining parameter prediction during a face milling operation. As an illustration, consider the high-speed camera method and specification of the infrared thermometers (IR). Sportscam-250 used to capture the milling process and establish the essential data on spark creation. Therefore, the camera was positioned with an optical zoom around 60 cm distant from the titanium alloy (Ti–6Al–4V) workpiece. Also, Fluke model 66 - (IR) can be determined an object’s surface temperature through measuring the amount of infrared energy irradiated by the object’s surface. The thermometer’s reflected, optics sense emitted, and transmitted energy that was focused and collected upon a detector. By precisely forecasting the machining parameters, this method can improve the efficiency of the machining process and, as a result, the quality of the goods.

Table 5.

Five cutting conditions selected randomly through Box–Behnken designs.

Five Conditions for Cutting	Feed Rate, f (mm/min)	Speed of Cutting, vc (rpm)	Cut Depth, d (mm)
1	160	1400	0.6
2	80	600	0.6
3	120	1000	0.6
4	160	600	0.6
5	80	1400	0.6

Fig. 4.

Face milling experimental setup.

4. Results and discussion

In this study, three case studies are taken into account based on the cutting responses during a face milling operation, including cutting force, temperature, and surface roughness. Five test results were generated at random. The experimental data and numerical simulation data from these five test circumstances were compared, and Table 5 shows the results. With a set depth of cut and two different levels of feed rates and cutting speeds, the machining parameter was changed. The results of measurements of cutting force, temperature, and surface roughness were shown in Table 6 using a multi-channel charge amplifier with dynamometer (Kistler, Type-5070A), infrared radiation (IR), and a portable roughness tester model TR200, respectively.

Table 6.

The machining parameter results during the face milling operation.

Number of Experiments	Case Study 1		Case Study 2	Case Study 3
	Principal Cutting Force (N)	Force for Cutting Feed (N)	Temperature (°C)	Surface Roughness, μm
1	322.27	69.82	81.9	0.141
2	389.28	25.21	75.2	0.062
3	565.92	64.45	79.4	0.094
4	478.45	69.09	127.2	0.129
5	283.45	116.58	81.6	0.209

4.1. Cutting force

It is challenging to formulate an analytical dynamic cutting force model since the cutting force depends on so many different cutting system factors. Therefore, by breaking down the metal cutting into smaller pieces as shown in Fig. 5, finite element modeling is thought to be a crucial tool to resolve this problem. As demonstrated in Fig. 6, when the simulation results from the finite element modeling (FEM) are compared with the experimental data, there is a very excellent agreement. In this regard, the main cutting force reached its maximum value, which was measured at 565.92 N, and was predicted by finite element modeling (FEM) at 553.35 N at feed rates of 120 mm/min, 1000 rpm, and 0.6 mm of cut depth. Additionally, with a feed rate of 80 mm/min, a cutting speed of 1400 RPM, and a depth of cut of 0.6 mm, the greatest value of feed cutting force was measured at 116.58 N and predicted by finite element modeling (FEM) to be 116.34 N showen Fig, 5. As a result, the feed cutting force was regarded as the cutting force with the lowest value. Hence, the main cutting force was measured and estimated as the higher force and thesefindingsare in agreement with earlier findings from Ezugwu and Ulvi [13], [14]. In addition, increasing the cutting speed during machining of titanium alloy (Ti–6Al–4V) led to decrease the main cutting force. However, the main cutting force increase as little bit while increasing of feed rate during cutting process on titanium alloy (Ti–6Al–4V).

Fig. 5.

Cutting force estimated using FEM.

Fig. 6.

Shows a comparison of cutting force results from simulations and experimental work on titanium alloy (Ti–6Al–4V) during face milling operations.

This increasing of the main cutting force scored its highest values at the intermediate ranges of both feed rate and cutting speed, but it decreases with increasing of the cutting speed associated with lower value of the feed rate shown in Fig. 7. On the other hand, increasing the cutting speed has a significant effect on feed cutting force especially when feed rates at low or medium values as shown in Fig. 8. Therefore, it can be predicted machining parameters by using finite element modeling (FEM) and also to optimize the influence during cutting process of titanium alloy (Ti–6Al–4V) from knowing the relationships between machining parameters. As a result, increasing the feed rate will sign as a better selection for maintaining of the main and feed cutting force at its lowest values. However, cutting speed play different way that increases will lead to decrease of the main cutting force.

Fig. 7.

Main cutting force measured during the cutting process.

4.2. Temperature

Temperature is a very significant component that affects the machining of titanium alloy throughout the machining process (Ti–6Al–4V). The poor surface finish and thus low productivity when cutting titanium alloy will be accelerated by temperature increase (Ti–6Al–4V). In order to record this phenomena, the spark produced during the machining process is seen using the portable high speed (Sportscam-250) camera and (Fluke Model-66) infrared thermometer (IR), as illustrated in Fig. 9. Therefore, experimentation demonstrates spark generation and chip formation in the area of contact between the cutting tool edge and the titanium alloy workpiece (Ti–6Al–4V) as illustrated in Fig. 10. This is as a result of increased thermal stress in the contact zone brought on by decreased heat dissipation by the workpiece and chips. As a result, even though cutting temperatures while working with titanium alloy are roughly twice as high, the high temperature was localized at the cutting edge and the tool's nose (Ti–6Al–4V).

Fig. 9.

Spark at the cutting zone during machining titanium alloy (Ti–6Al–4V).

Fig. 10.

Chip formation and spark generation at the cutting edge.

The face milling of titanium alloy (Ti–6Al–4V) has been fully predicted thermomechanically. Using FEM, a temperature distribution estimate was made, with the assumption that it would remain constant along the cut depth. Using this approach, which is based on finite element modeling, it is possible to predict machining parameters, such as temperature, when processing titanium alloy (Ti–6Al–4V). According to Fig. 2, the work-thermal piece's boundary condition for titanium alloy (Ti–6Al–4V) was carried out under dry cutting conditions. In addition, supposed that the thermal contact between the work-piece of titanium alloy (Ti–6Al–4V) and the cutting tool is continuous. Consequently, enough time to release the heat produced during the machining process. The greatest temperature observed throughout the machining process, as shown in Fig. 11, is localized in the contact area, with the temperature distribution findings ranging from 75.2 °C to 127.2 °C. As a result, during the modeling of the machining of titanium alloy (Ti–6Al–4V), as shown in Fig. 12, the results of the experimental cutting and the estimated values of temperature distribution match.

Fig. 11.

The maximum value of temperature measured at cutting speed 600 rpm, feed rate 160 mm/min and depth of cut 0.6 mm.

Fig. 12.

Estimated temperature distribution at 600 rpm cutting speed, 160 mm/min feed rate, and 0.6 mm of cut depth.

Bill [15] foundany increase in the feed rate, cutting speed, and depth of cut caused increases in the cutting temperature and this earlier finding is in agreement with our experimental results. Additionally, raising the feed rate increases the area where the cutting tool edge comes into contact with the workpiece, which raises the cutting temperature and makes it feasible to produce a spark, as illustrated in Fig. 13. As a result, the spark and heat produced during the machining of the titanium alloy (Ti–6Al–4V) might also result in a subpar surface polish and a short tool life.

Fig. 13.

Temperature distribution measured during the cutting process.

In this study, it obtained a surface roughness and feed cutting force follow the same curve trend in terms of similarity. When seen in Fig. 14, the depth of cut is maintained throughout the cutting process, however this comparable pattern changes as the temperature distribution rises. Therefore, it is possible to infer that the temperature distribution and machining responses, such as feed cutting force, influence the trend of surface roughness during the cutting of titanium alloy.

Fig. 14.

When the temperature distribution during the cutting process increases, there is a similar pattern between the feed cutting force and surface roughness.

4.3. Surface roughness

Surface roughness is a crucial outcome of the machining process for determining whether a material is machinable. On the other hand, tool wear is thought to be the cause of surface roughness. This is due to the fact that increased tool wear causes increased surface roughness, which has a direct impact on tool life [16]. The experimental design's outcomes, which are shown in Table 6, Clearly demonstrate the strong correlation between the feed cutting force and surface roughness. Raising the value of the feed cutting force has the opposite effect on the value of surface roughness. Fig. 15 displays the feed cutting force values that were computed based on the experimental results. The feed cutting force initially ranged from 69.82 N to 25.21 N before increasing once again to 116.58 N. The surface roughness measured during the cutting process is shown in Fig. 16 to follow a similar trend, beginning at 0.141 m, falling to 0.062 m, and then rising to 0.209 m. As a result, it was shown that feed cutting force measurements may be used to forecast the trajectory of surface roughness. Therefore, surface roughness can be predicted using finite element modeling (FEM), which also predicts the feed cutting force throughout the cutting simulation process. This is due to the fact that it was thought that the prior feed cutting force and surface roughness parameters were too similar. Given that the cutting process for titanium alloy utilized finite element modeling, it was feasible to estimate surface roughness from feed cutting force measurements (FEM). The surface roughness trend will be predicted using this module as a predictive model. Additionally, as demonstrated in Fig. 17, the impact of cutting speed on surface roughness during the cutting process was more pronounced than the impact of feed rate.

Fig. 15.

Feed cutting force measured by the experimental results.

Fig. 16.

Surface roughness measured by the experimental results.

Fig. 17.

Surface roughness measured during cutting process.

5. Conclusion

Due to their poor machinability, titanium alloy (Ti-6AL-4V) is one of the most demanding materials for industrialists to manufacture. It is exceedingly difficult to simultaneously regulate all cutting parameters during the machining process. As a result, the following conclusions can be derived from the work's results:

• 1.Due to its ability to produce extremely accurate findings, reduce manufacturing costs, and shorten machining times, finiteelement modeling (FEM) is regarded as a very significant technology for evaluating the cutting force, temperature, and surface roughness. The accuracy of the model values predicted by the finite element simulation was 97% when compared to the experimental data. As a result, examination of finite element models provides researchers with a very effective platform to enhance the cutting process.
• 2.This research effort has taken into account a thorough thermo-mechanical prediction of the titanium alloy (Ti–6Al–4V). Dry cutting conditions were used to conduct thermal boundary conditions on the workpiece. Three mesh sections were built and generated for the titanium alloy workpiece (Ti–6Al–4V) during the simulation phase to avoid mesh distortion problems and speed up program execution.
• 3.Surface roughness trends can be predicted using feed cutting force measurements. Because of raising the feed rate in the cutting zone, the thermal shock and a spark can be generated during the cutting process of (Ti–6Al–4V). This led to the conclusion that Surface roughness rose in direct proportion to the magnitude of the feed cutting force, and vice versa.
• 4.While holding the depth of cut constant, The acquisition of a similar trend between feed cutting force and surface roughness is made possible by the FEM. Surface roughness can be forecasted as a function of the feed cutting force during the simulated cutting process of titanium alloy (Ti–6Al–4V). This model can be useful in forecasting the machining parameters to improve the performance of the cutting process and subsequently the quality of the goods.
• 5.Based on the results from experimental and simulated by utilizing finite element modeling (FEM) during the simulation process of cutting titanium alloy (Ti–6Al–4V), a satisfactory surface finish may be attained when the cutting parameters are set as high-speed machining and by choosing a medium feed rate through fixed depth of cut. Therefore, this scientific research investigation is led to validation of finite element modeling for titanium alloy (Ti–6Al–4V) at face milling.
• 6.Finite element method can be used to predict the optimal machining process for titanium alloy. Becusea of reduce manufacturing costs, shorten machining times, and produce extremely accurate findings which are considered the future scope of titanium alloy machining.

Author contribution statement

Moaz H. Ali: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Data availability statement

Data included in article/supp. material/referenced in article.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.