General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

Users may download and print one copy of any publication from the public portal for the purpose of private study or research.

You may not further distribute the material or use it for any profit-making activity or commercial gain

You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from orbit.dtu.dk on: Jul 06, 2020

A Finite Element Modeling Prediction in High Precision Milling Process of Aluminum6082-T6

Davoudinejad, A.; Doagou-Rad, S.; Tosello, G.

Published in:Nanomanufacturing and Metrology

Link to article, DOI:10.1007/s41871-018-0026-7

Publication date:2018

Document VersionPeer reviewed version

Link back to DTU Orbit

Citation (APA):Davoudinejad, A., Doagou-Rad, S., & Tosello, G. (2018). A Finite Element Modeling Prediction in High PrecisionMilling Process of Aluminum 6082-T6. Nanomanufacturing and Metrology. https://doi.org/10.1007/s41871-018-0026-7

A finite element modeling prediction in high precision milling process

of aluminum 6082-T6

A. Davoudinejad1*, S. Doagou-Rad1, G. Tosello1

1Department of Mechanical Engineering, Technical University of Denmark, Building 427A, Produktionstorvet, 2800 Kgs. Lyngby, Denmark

Abstract

This study investigates micro end-milling machining prediction using three-dimensional finite element modeling (3D

FEM) method. The FE model was developed for contouring up-milling operation for prediction of chip flow, burr formation,

cutting temperature and cutting forces in an integrated model. The flow stress behavior of the workpiece was modeled by

the Johnson–Cook material constitutive model. Different cutting conditions were simulated to consider the effect of the

process variables that might be difficult or impossible to follow in the physical experiments at this scale. The tool was pre-

cisely 3D modeled to describe the detailed geometry of the tool which is one of the critical aspects of the model and is

characteristic of the cutter tool micro geometry design. Furthermore, the tool deflection was studied using a FE model under

the experimental cutting forces measured in the different cutting conditions in order to consider the micro end-mill deviation

from the nominal position. 3D simulations of chip flow and temperature distribution are compared in various cutting condi-

tions. The results of the burr formation, temperature distribution and cutting forces in three directions predictions are com-

pared against the experiments. Simulations were able to predict the burr height with an accuracy between 1 to 4 µm depend-

ing on cutting parameters settings. The results of this study are beneficial to comprehend the micro end-milling chip and burr

formation to increase the machinability of the workpiece and to understand the influence of cutting parameter in order to

prevent several experimental tests prior to the final machining.

Keywords: 3D finite element modeling; Burr formation; Chip formation; Cutting force; deflection; Micro end-milling; Temperature distribution

1. Introduction

The micro end-milling has become an established high-performance process within the last three

decades. Various industries such as aerospace, automotive, medical, precision die and mold manufac-

turing use micro milling for manufacturing complex miniaturized structure with high precision [1]. A

number of experimental studies have investigated micro machining by evaluating the cutting parame-

ters to improve the process performance in terms of surface roughness, cutting force, chip and burr

formation, tool wear, tool life [2]–[5] [6]. However, the predictive models are also used and can be

combined as supplementary tool into the process planning system to increase productivity and improve

product quality [7][8].

FE modeling of machining process have been reported by different studies. For example, 2D FEM

was used for high speed cutting (up to 200,000 rpm) to predict burr formation on Ti6Al4V in micro

end-milling process [9].

Another study evaluated the stress distribution in the vicinity of the cutting edge [10]. Microstructural

texture evolution by 2D FEM was investigated in high speed machining of Ti6Al4V alloy and plastic

deformation of the machined surface was analyzed [11]. The same workpiece material was used to

investigate the stress distribution on the tool surface and friction coefficients between the tool and the

formed chip on the tool rake face with FE simulations [12].

In addition to 2D analysis, the 3D FEM method provides additional analysis material to explore the

effect of the cutting tool on chip flow and burr formation [13]. A 3D FEM in dry micro milling process

studied chip formation such as plastic chip flow, plastic strain, principle stresses, and temperatures of

titanium alloy Ti-6Al-4V [14]. Another study [13] with 3D FEM investigated the effect of the cutting

edge radius on temperature distribution, effective stress and simulated cutting forces in Al2024-T6 mi-

cro milling. Abouridouane et al. [15] studied microstructure-based multiphase FEM of micro drilling

ferritic-pearlitic carbon steels. Various size effects and chip formation were predicted with experimental

tests and more than 60% improvement of feed and torque with FEM computation were observed. Chip

formation and cutting forces were predicted with a developed 3D multiphase FEM. A comparable cut-

ting forces and chip formation prediction tool was validated against the experiments [16].

The micro burr formation was investigated in the past decades mainly with experimental, analytical and

numerical methods. Different studies classified burrs formation according to its location in milling pro-

cess namely as; entrance burr, exit burr, side burr, top burr and bottom burr [17] [18]. Micro milling

FEM simulations were conducted for controlling the burr formation by vibration-assisted machining

on the top burr generation results [19]. The 3D FE simulation was used to investigate the prediction

capability of the model on different types of burr formation in micro ball milling of Ti-6Al-4V alloy

[17].

This study presents 3D FE simulations of contouring operation micro up-milling by considering tool

helix angle and corner radius. Different cutting conditions are simulated in order to evaluate the possible

prediction capability. Various aspects of machining process were assessed and the model is used to

study the chip flow, burr formation and temperature distribution. The burr formation, cutting forces and

temperature predictions are validated by comparing the FEM results with experiments. The design of

experiments analysis DOE is carried out on the burr formation to clarify the parameters contribution

and to show the experimental and simulation comparison. Additionally the tool deflection under the

acquired experimental cutting forces was studied using a structural finite element modeling.

2. 3D FE modeling

2.1 FE model

The FE model for micro milling of Aluminum 6082-T6 has been established using a Lagrangian FE

formulation for implementing coupled thermo-mechanical transient analyses by AdvantEdge® FEM

software [20]. The calculations were carried out on with 8 threads parallel simulation mode to speed up

the simulation time. The computing time was 25-35 hours long based on the selected conditions. Fig. 1

presents the set-up and the geometry of the model

Fig. 1 Up-milling set-up and tool cutting edge

2.2 Tool and workpiece

Considering the proper geometry of the tool is a significant factor for modeling accuracy as it was

investigated and demonstrated in the previous study on the influence of the tool cutting edge solid

modeling [21]. In this context the accuracy of the micro end-mill model is critical. A CAD model was

generated based on the points cloud obtained by a 3D optical microscope in order to demonstrate and

reproduce the actual topology of the micro end-mill. The geometry of the mill (Table 1) was scanned

and reconstructed by using an Alicona G5 focus variation 3D microscope. A four nodes tetrahedral

elements type was used for meshing tool and workpiece which is one of the most proper mesh type for

3D modeling [22]. The rigid micro end-mill was meshed using 58205 brick elements and 15042 nodes.

The workpiece was meshed with total number of 27870 elements and 5458 nodes. After earliest tests

[23] [24], the element sizes for the workpiece were set to 2 mm and 1µm. A finer mesh was defined in

the region of the contact on the micro end-mill (Fig. 1(b) and Fig. 1(d)) and the workpiece cutting zone

area.

Table 1 Tool characteristics [23]

Actual dimensions

Tool manufacturer Dormer

Code S150.05

Tool material Carbide

Flute number 2

Diameter 493 µm

Cutting edge radius (re) 3 μm

Helix angle 27.26°

Rake angle 0°

Relief angle 7°

Corner radius (rε) 22 µm

The tool element size was fixed at 1 mm and 1 µm. The quality of the mesh was continuously monitored

in the run and, when the element distortion reached a certain threshold, adaptive remeshing was applied.

A reliable material model is critical in machining simulation performance [25]. Among various material

models the Johnson-Cook (JC) model [26] is broadly applied for machining simulations for equivalent

behavior of material as a function of strain, strain rate and temperature [27]. The JC material model

used in this study is expressed in (Eq. 1):

A B ε 1 C ln∙

∙ 1 (1)

Where is the material flow stress, ε is the plastic strain, ∙ is the strain rate, ε∙ is the reference

strain rate. is the material temperature, is the melting point and is the room temperature.

The JC constants are as follows: A is the yield stress, B is the pre-exponential factor, C is the strain rate

factor, n is the work hardening exponent and m is the thermal softening exponent. Table 2 and Table 3

listed the JC material constants and the physical properties of material respectively.

Table 2 Material constants for the JC model [28][24]

A (MPa) B (MPa) C m n Tm

( ̊C)

Ta

( ̊C)

214.25 327.7 0.00747 1.3 0.504 582 21

Table 3 Material properties for Al6082-T6 [24]

Property Unit Value

Young’s modulus E (GPa) 70

Poisson ratio v ------ 0.33

Density ρ (g/cm2) 2.70

Thermal conductivity K (W/m ̊K) 180

Specific heat Cp (J/Kg°C) 700

Thermal expansion coefficient (°C) 24x10-6

Coulomb law (Eq. 2) was used as friction model. This model consider frictional stresses τ on the tool

rake face relative to the normal stresses with a friction coefficient µ [29]. The constant value of

coefficient of friction was assigned as µ=0.7 based on the experimental study on Al6061-T6 [30]. These

two aluminum Al6082-T6 and Al6061-T6 are popular alloys that sometimes are used to replace each

other in the industrial practice due to the similar characteristics.

(2)

2.3 Deflection model

The tool deformation was also studied using FE simulations. The simulations are conducted using

the commercial FE code ABAQUS Implicit. Quadratic tetrahedral elements (C3D10) were used to

mesh the FE models. Mesh-seed lengths of 0.5 μm were used around the tip of the tool. The conver-

gence studies led to the construction of FE models comprising 700000 elements. Fig. 2 shows the

full length of end-mill with fine mesh on the region of interest. The processing unit similar to the

aforementioned FE simulations was used. Fig 2 shows the constructed FE model and the imple-

mented mesh distribution.

Fig. 2 Tool FE model in ABAQUS.

3. Experimental method

Experiments were carried out on a Kern EVO ultra precision 5-axis machine (Fig. 3) with the position-

ing tolerance on the workpiece equal to ± 2 μm as stated by the manufacturer. The workpiece was

especially designed to carry out the machining near the geometrical center of the dynamometer flange

as shown in Fig. 3. The carbide micro end-mill (Dormer S150.05) details are presented in Table 1. The

infrared ThermaCAM Researcher camera with waveband in the electromagnetic spectrum (in the range

of 8-9 μm) has been used for measuring the temperature at the cutting area. All of the experimental data

have been normalized to a room temperature of 21 °C. The camera was adjusted with standard black-

bodies at different temperatures [31]. Fig. 3(c) show the experimental temperature measurements in the

cutting area at different cutting conditions.

Fig. 3 (a) experiment setup; (b) cutting area (top view) (c) experimental temperature measurements

The cutting edges was measured with a 3D optical microscope (Alicona) before machining with fol-

lowing measurement parameters: 10x magnification, exposure time = 1.206 ms, contrast = 1, coaxial

light, estimated vertical and lateral resolutions = 0.083 μm and 4 μm respectively. A good agreement

between nominal and measured value was found for the characteristics angles (Table 1). Table 4 shows

the experimental conditions of the tests. The cutting parameters were selected with consideration of

previous studies on the process parameters and burr formation [22] [32]. The test plan was a 22 full

factorial DOE. The tests were repeated two times. The cutting conditions (feed per tooth, depth of cut,

width of cut) were selected to stay around the cutting edge radius, this way, to appreciate the minimum

uncut chip thickness effect.

The micro slots were acquired with an Infinite Focus Alicona G5 focus variation 3D microscope

(with the following measurement parameters: 10x magnification, exposure time = 110 ms, contrast = 1,

coaxial light, estimated vertical and lateral resolutions = 3.6 μm and 2.4 μm). Concerning the cutting

force a miniature piezoelectric three-axial dynamometer Kistler 9317B was used and amplified by three

Kistler 5015A charge amplifiers.

The force signal compensation [19] was applied to remove the dynamic contribution of the sensor

resonance. The frequency response functions (FRF) of the dynamometer were recognized from impact

tests applied on the force directions as detailed in [33].

Table 4 Experimental conditions

Cutting parame-

ters

Unit Test

1

Test

2

Test

3

Test

4

Depth of cut µm 50 100 50 100

Width of cut µm 125 250

Feed per tooth µm/

(tooth·rev) 4

Cutting speed m/min 28.27

Approach Up-milling

4. Results and discussion

Different chip shapes were found after 360° tool rotation in different cutting conditions. Fig. 4 presents

the plastic strain distribution in the cutting area. . The plastic strain has a slight reduction in comparison

to the highest cutting condition. The variation of the plastic strain is more visible on the burr formation

and machine wall. It was noticed that the degree of plastic deformation on the machined surface area

increases considerably in the lowest cutting condition represented Fig. 4 (a). Fig. 5 shows different

phases of the tool engagement and the distribution of temperature in the tool and cutting area. The

simulation predicted temperature reaches its maximum of ≈35.5 °C in the chip and workpiece zone at

highest engagement angle. It is visible in the simulations, where the chip thickness starts at zero and

increases towards the end of the cut. Highest temperature distribution was observed in Test 4 (Fig. 5 (d))

with higher axial and radial depth of cut. It can be noticed that the chip shows the highest temperature

mainly due to its intensive plastic deformation. In the higher cutting conditions temperature rise mainly

due to the heat generation of the workpiece (mechanical energy) in the deformation zones and the fric-

tion between the tool–chip and tool–workpiece interface. Therefore, more heat is generated in the chip

area at Test 4 (Fig. 5 (d)) and more in the machined surface wall at Test 1 (Fig. 5 (a)) which is correlated

to increased plastic deformation in the cutting interface.

Fig. 4 Plastic strain distribution in the cutting zone

Simulated temperature on the tool corner is presented (on the right side) in Fig. 5 at the end of cut

for (Test 1,2 θ ≈ 50° and Test 3,4 θ ≈ 125°). The heat is determined in the chip and contact area of

the workpiece and for the tool mainly in cutting edge radius. Lower temperature distribution in the

cutter area vicinity recorded than the chip and workpiece.

4.1 Burr formation

Burrs formation at various cutting conditions in FEM (end of the cut) and in real experiments from

the side and the top respectively, are shown in Fig. 6. Based on the burrs classification defined in [17]

[18], top burrs were formed on the top side of the machined slot. The simulations were carried out for

both teeth engagements and the pictures of FEM results illustrate the end tool engagements arc. How-

ever, the experimental results indicate several teeth engagements. Fig. 7 shows the top burr height com-

parison from experimental and simulation results. The results indicate that machining conditions influ-

ence the burrs.

Based on the selected machining conditions, the depth of cut and width of cut affected the top burr

height. Top burr size is higher for lower ap values (Test 1, Fig. 6(a) and Test 3, Fig. 6(c)). The smallest

top burr is obtained for high depth and width of cut (Test 4, Fig. 6(d)).

The Design of Experiments (DOE) technique was applied to investigate the different parameters’

impact. The interaction and main effect plots are presented in Fig. 8. The main effect plot (see Fig. 8(a))

shows the burr height variation which by increasing depth and width of cut smaller burr were formed

on the machined wall. The interaction plot in Fig. 8(b) shows experimental and simulation in different

depth of cut, width of cut and burr measurement position. The simulation results were underestimated

between 8-17% in evaluation with the experimental results nevertheless, parallel trends were predicted

for all simulations. The deviation of height were in the range of 2-4 μm. It was noticed with respect to

the experimental cutting conditions, the prediction of burr height was rather poor in lower setting (depth

of cut 50 µm and width of cut 125 µm).

Fig. 5 Simulated chip temperature distribution in different angular positions and along the cutting edge at the end of cut.

Fig. 6 Burr formation in different cutting conditions

Fig. 7 Burrs height assessment at different conditions

Fig. 8 (a) Main effects plot of burr height (b) interaction plot of burr height

4.2 Cutting forces

A low-pass filter with a fifth-order Butterworth was used for the simulated cutting force signals.

Similar experimental cut-off frequency to the bandwidth of the dynamometer (about 7000 Hz) was

applied to the signals for the comparable results with experimental tests [24]. In the previous experi-

ments with similar cutting speed similar sampling frequency was applied [33]. The assessments were

performed with the results obtained from the FEM and the experiments for one tooth engagement as

shown in Fig. 9. The shapes of the first tooth forces acquired for two different cutting conditions, Test

1 and Test 2, are presented. Feed force Fx is the main component and presented the highest force value

and shows similar trends for FEM in comparison to experiments. In the up-milling approach the force

directions the initial of the cut start at zero and increases by the engagement of the tooth toward the end

of machining path. Fx starts decreasing faster and with a lower inclination in case of FEM. In the ex-

periments with this approach the cutting edge forced into the workpiece and cutting forces push the

cutter. The difference might be due to the fact that during the experiments the cutting edge slides on the

workpiece. Another issue that generated problems was represented by the influence of the built up edge

(BUE) on the tool which alters the geometry of the cutting edge and also increases the deviation be-

tween real and nominal spindle speed in the machine tool and as a consequence it could affect the tool

engagement. The Fy forces for both results show a minor decrease and a positive increase at the end of

the cut. The force in Z direction (Fz) has very small value in comparison to other force directions so it

is frequently neglected in the models. The cutting forces evaluation presents a similar trend of profile

curve shapes, for three forces components. A slightly relevant over-estimation of the forces in X and Y

directions are recorded in the simulated conditions. The mismatching in the validation of cutting force

can be explained by the tool static deflection during machining since the model does not include the

simulation of cutting deflection. The spindle and the cutter run-outs were not considered in the

FE model.

Fig. 9 Cutting forces comparison for (a) Test 1 and (b) Test 2

4.3 Deflection

Acquiring the cutting forces from experiments and simulation enables us to study the deflection

behavior of the tool under the accompanying loads during the process [34]. In order to achieve high

quality part geometry and desirable tolerances, the sources and levels of errors should be completely

understood. In fact, one of the major sources of the error in the process is originated from the tool

deflection. Therefore, the tool deflection under the actual loads in the process is investigated using

structural finite element. Two loading scenarios with the two described depths of cuts are considered

in the simulations (See Fig. 9). In the second loading case the depth of cut is twice the first scenario.

Fig. 10 shows the maximum deflections of the tip under the achieved experimental forces.

Fig. 10 The maximum total deflection considering the loading and depth of cuts of (a) Test 1, and (b) Test 2. Deflections in the pic-

tures are scaled up 100 times. (The depicted magnitudes are in millimeters)

In addition, considering the forces in one cycle, the deflections of the tool along the length of the tip

under the two applied loads were studied. Fig. 11 presents the average deflections of the tool in one

cycle along the whole length of the tool under individual and total loading directions. As it can be

clearly seen from the results the deflection of tip increases notably with increasing the cutting depths.

In fact, under second loading forces the maximum deflection increases up to 4.5 μm. It is also notewor-

thy to mention that since the tip deforms elastically, the deflection of the tip follows the similar path

observed in Fig. 9.

Fig. 11 Deflection of central axis of the tool in (a) Test 1, and (b) Test 2.

5. Conclusion

A numerical model has been developed to investigate the micro milling process of the aluminum

alloy Al6082-T6. The JC material model and was employed with modified coefficients for the specific

workpiece material. 3D FE simulations were utilized for predicting different machining aspects. The

tool CAD model was generated based on the actual geometry by using the micro end-mill cloud of

points. The model was validated with experimental tests carried out in various conditions. The tool

deflection was also investigated under the acquired experimental cutting forces to find out the defor-

mation of the tool position regarding the nominal geometry. In addition, the influence of the cutting

depth on the deflection of the tool was studied. The evolution of chip generation and the cutting tem-

perature distribution were demonstrated. The predicted temperature along the cutting edge and machin-

ing were determined. The maximum temperature that was observed was approximately of 35.5 °C. The

simulated burr revealed a comparable results which affected by different cutting conditions as DOE

analysis was conducted to clarify the machining setting impact. The simulation results were underesti-

mated between 8-17% in comparison with the experiments. The proposed 3D FE model demonstrated

a good matching with experiments force profiles shape, mostly in the two major directions Fx and Fy

and comparable cutting forces amplitude in all directions.

Acknowledgments:

The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the

European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 609405 (CO-

FUNDPostdocDTU). The authors would also like to acknowledge the Politecnico di Milano university for founding

the PhD project "3D Finite Element Modeling of Micro End-Milling by Considering Tool Run-Out, Temperature Dis-

tribution, Chip and Burr Formation' by Ali Davudinejad.

References

[1] H. Hassanpour, M. H. Sadeghi, H. Rezaei, and A. Rasti, “Experimental Study of Cutting Force, Microhardness, Surface Rough-

ness, and Burr Size on Micromilling of Ti6Al4V in Minimum Quantity Lubrication,” Mater. Manuf. Process., vol. 31, no. 13, pp.

1654–1662, 2016.

[2] U. Koklu and G. Basmaci, “Evaluation of Tool Path Strategy and Cooling Condition Effects on the Cutting Force and Surface

Quality in Micromilling Operations,” Metals (Basel)., vol. 7, 426, 2017.

[3] D. L. Zariatin, G. Kiswanto, and T. J. Ko, “Investigation of the micro-milling process of thin-wall features of aluminum alloy

1100,” Int. J. Adv. Manuf. Technol., vol. 93, no. 5, pp. 2625–2637, Nov. 2017.

[4] İ. Ucun, K. Aslantas, and F. Bedir, “An experimental investigation of the effect of coating material on tool wear in micro milling

of Inconel 718 super alloy,” Wear, vol. 300, no. 1, pp. 8–19, 2013.

[5] S. Nadimi, B. Oliaei, and Y. Karpat, “Built-up edge effects on process outputs of titanium alloy micro milling,” Precis. Eng., vol.

49, pp. 305–315, 2017.

[6] A. Abbas, D. Pimenov, I. Erdakov, M. Taha, M. El Rayes, and M. Soliman, “Artificial Intelligence Monitoring of Hardening

Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recy-

cled Aluminum Alloy 6061 Сhips,” Metals (Basel)., vol. 8, no. 6, p. 394, May 2018.

[7] P. J. Arrazola, T. Özel, D. Umbrello, M. Davies, and I. S. Jawahir, “Recent advances in modelling of metal machining processes,”

CIRP Ann. - Manuf. Technol., vol. 62, no. 2, pp. 695–718, 2013.

[8] Z. Niu, F. Jiao, and K. Cheng, “Investigation on Innovative Dynamic Cutting Force Modelling in Micro-milling and Its Experi-

mental Validation,” Nanomanufacturing Metrol., vol. 1, no. 2, pp. 82–95, Jun. 2018.

[9] A. K. Yadav, V. Bajpai, N. K. Singh, and R. K. Singh, “FE modeling of burr size in high- speed micro-milling of Ti6Al4V,” Pre-

cis. Eng., vol. 49, pp. 287–292, 2017.

[10] X. Wu, L. Li, and N. He, “Investigation on the burr formation mechanism in micro cutting,” Precis. Eng., vol. 47, pp. 191–196,

2017.

[11] A. Li, J. Pang, J. Zhao, J. Zang, and F. Wang, “FEM-simulation of machining induced surface plastic deformation and micro-

structural texture evolution of Ti-6Al-4V alloy,” Int. J. Mech. Sci., vol. 123, pp. 214–223, Apr. 2017.

[12] D. Ulutan and T. Özel, “Determination of tool friction in presence of flank wear and stress distribution based validation using

finite element simulations in machining of titanium and nickel based alloys,” J. Mater. Process. Technol., vol. 213, no. 12, pp. 2217–

2237, Dec. 2013.

[13] K. Yang, Y. Liang, K. Zheng, Q. Bai, and W. Chen, “Tool edge radius effect on cutting temperature in micro-end-milling pro-

cess,” Int. J. Adv. Manuf. Technol., vol. 52, no. 9, pp. 905–912, 2011.

[14] T. Özel, A. Olleak, and T. Thepsonthi, “Micro milling of titanium alloy Ti-6Al-4V: 3-D finite element modeling for prediction of

chip flow and burr formation,” Prod. Eng., vol. 11, no. 4, pp. 435–444, 2017.

[15] M. Abouridouane, F. Klocke, and D. Lung, “Microstructure-based 3D Finite Element Model for Micro Drilling Carbon Steels,”

Procedia CIRP, vol. 8, pp. 94–99, Jan. 2013.

[16] M. Abouridouane, F. Klocke, A. Oktafiani, and B. Döbbeler, “Microstructure-based FEM Simulation of Metal Cutting,” Procedia

CIRP, vol. 58, pp. 85–90, Jan. 2017.

[17] M. J. Chen, H. B. Ni, Z. J. Wang, and Y. Jiang, “Research on the modeling of burr formation process in micro-ball end milling

operation on Ti--6Al--4V,” Int. J. Adv. Manuf. Technol., vol. 62, no. 9, pp. 901–912, 2012.

[18] J. C. Aurich, D. Dornfeld, P. J. Arrazola, V. Franke, L. Leitz, and S. Min, “Burrs—Analysis, control and removal,” CIRP Ann. -

Manuf. Technol., vol. 58, no. 2, pp. 519–542, 2009.

[19] W. Chen, X. Teng, L. Zheng, W. Xie, and D. Huo, “Burr reduction mechanism in vibration-assisted micro milling,” Manuf. Lett.,

vol. 16, pp. 6–9, Apr. 2018.

[20] Advantedge, “Third Wave Advantedge user’s manual,” 2014.

[21] A. Davoudinejad and M. Y. Noordin, “Effect of cutting edge preparation on tool performance in hard-turning of DF-3 tool steel

with ceramic tools,” J. Mech. Sci. Technol., vol. 28, no. 11, pp. 4727–4736, Nov. 2014.

[22] T. Thepsonthi and T. Özel, “3-D finite element process simulation of micro-end milling Ti-6Al-4V titanium alloy: Experimental

validations on chip flow and tool wear,” J. Mater. Process. Technol., vol. 221, pp. 128–145, 2015.

[23] A. Davoudinejad, G. Tosello, and M. Annoni, “Influence of the worn tool affected by built-up edge (BUE) on micro end-milling

process performance: A 3D finite element modeling investigation,” Int. J. Precis. Eng. Manuf., vol. 18, no. 10, pp. 1321–1332, 2017.

[24] A. Davoudinejad, G. Tosello, P. Parenti, and M. Annoni, “3D Finite Element Simulation of Micro End-Milling by Considering

the Effect of Tool Run-Out,” Micromachines, vol. 8, 187., 2017.

[25] S. Pervaiz, S. Anwar, S. Kannan, and A. Almarfadi, “Exploring the influence of constitutive models and associated parameters

for the orthogonal machining of Ti6Al4V,” IOP Conf. Ser. Mater. Sci. Eng., vol. 346, p. 12058, Apr. 2018.

[26] G. R. Johnson and W. H. Cook, “A constitutive model and data for metals subjected to large strains, high strain rates and high

temperatures,” 7th International Symposium on Ballistics. pp. 541–547, 1983.

[27] U. S. Dixit, S. N. Joshi, and J. P. Davim, “Incorporation of material behavior in modeling of metal forming and machining pro-

cesses: A review,” Mater. Des., vol. 32, no. 7, pp. 3655–3670, 2011.

[28] S. P. F. . Jaspers and J. . Dautzenberg, “Material behaviour in conditions similar to metal cutting: flow stress in the primary shear

zone,” J. Mater. Process. Technol., vol. 122, no. 2, pp. 322–330, 2002.

[29] X. Man, D. Ren, S. Usui, C. Johnson, and T. D. Marusich, “Validation of Finite Element Cutting Force Prediction for End Mill-

ing,” Procedia CIRP, vol. 1, pp. 663–668, 2012.

[30] M. K. Medaska, L. Nowag, and S. Y. Liang, “SIMULTANEOUS MEASUREMENT OF THE THERMAL AND

TRIBOLOGICAL EFFECTS OF CUTTING FLUID,” Mach. Sci. Technol., vol. 3, no. 2, pp. 221–237, 1999.

[31] “ThermCAM Researcher Features/Specifications,” 2002.

[32] E. Vázquez, C. A. Rodríguez, and A. Elías-zúñiga, “An experimental analysis of process parameters to manufacture metallic mi-

cro-channels by micro-milling,” pp. 945–955, 2010.

[33] A. Davoudinejad, P. Parenti, and M. Annoni, “3D finite element prediction of chip flow, burr formation, and cutting forces in

micro end-milling of aluminum 6061-T6,” Front. Mech. Eng., vol. 12, no. 2, 2017.

[34] S. N. B. Oliaei and Y. Karpat, “Influence of tool wear on machining forces and tool deflections during micro milling,” Int. J. Adv.

Manuf. Technol., vol. 84, no. 9–12, pp. 1963–1980, 2016.