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Review Article | Open Access

Conventional and micro scale finite element modeling for metal cutting process: A review

Le WANGaCaixu YUEa( )Xianli LIUaMing LIaYongshi XUaSteven Y. LIANGb
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA
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Abstract

The metal cutting process is accompanied by complex stress field, strain field, temperature field. The comprehensive effects of process parameters on chip morphology, cutting force, tool wear and residual stress are complex and inter-connected. Finite element method (FEM) is considered as an effective method to predict process variables and reveal microscopic physical phenomena in the cutting process. Therefore, the finite element (FE) simulation is used to research the conventional and micro scale cutting process, and the differences in the establishment of process variable FE simulation models are distinguished, thereby improving the accuracy of FE simulation. The reliability and effectiveness of FE simulation model largely depend on the accuracy of the simulation method, constitutive model, friction model, damage model in describing mesh element, the dynamic mechanical behavior of materials, the tool-chip-workpiece contact process and the chip formation mechanism. In this paper, the FE models of conventional and micro process variables are comprehensively and up-to-date reviewed for different materials and machining methods. The purpose is to establish a FE model that is more in line with the real cutting conditions, and to provide the possibility for optimizing the cutting process variables. The development direction of FE simulation of metal cutting process is discussed, which provides guidance for future cutting process modeling.

References

1

Liang L, Lin G, Tan H, et al. Prediction of critical cutting condition for onset of serrated chip in ductile metallic material using dynamic yield stress. J Manuf Process 2021;64:927-36.

2

Ullah I, Zhang S, Zhang Q, et al. Numerical investigation on serrated chip formation during high-speed milling of Ti-6Al-4V alloy. J Manuf Process 2021;71:589-603.

3

Li B, Zhang S, Zhang Q, et al. Simulated and experimental analysis on serrated chip formation for hard milling process. J Manuf Process 2019;44:337-48.

4

Aydın M, Köklü U. Analysis of flat-end milling forces considering chip formation process in high-speed cutting of Ti6Al4V titanium alloy. Simul Model Pract Theory 2020;100:102039.

5

Schraknepper D, Peng B, Bergs T. Advanced calculation of the stress distribution in milling tools during cutting under consideration of residual stresses and tool wear. Procedia CIRP 2021;102:19-24.

6

Rahul Y, Vipindas K, Mathew J. Methodology for prediction of sub-surface residual stress in micro end milling of Ti-6Al-4V alloy. J Manuf Process 2021;62:600-12.

7

Ullah I, Zhang S, Waqar S. Numerical and experimental investigation on thermo-mechanically induced residual stress in high-speed milling of Ti-6Al-4V alloy. J Manuf Process 2022;76:575-87.

8

Ping Z, Yue X, Shuangfeng H, et al. Experiment and simulation on the high-speed milling mechanism of aluminum alloy 7050–T7451. Vacuum 2020;182:109778.

9

Li S, Sui J, Ding F, et al. Optimization of milling aluminum alloy 6061–T6 using modified Johnson-Cook model. Simul Model Pract Theory 2021;111:102330.

10

O’Toole L, Kang CW, Fang FZ. Precision micro-milling process: state of the art. Adv Manuf 2021;9(2):173-205.

11

Thepsonthi T, Özel T. 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 2015;221:128-45.

12

Soliman HA, Shash AY, El Hossainy TM, et al. Investigation of process parameters in orthogonal cutting using finite element approaches. Heliyon 2020;6(11):e05498.

13

Zhang G, Ran J, To S, et al. Size effect on surface generation of multiphase alloys in ultra-precision fly cutting. J Manuf Process 2020;60:23-36.

14

Xu X, Outeiro J, Zhang J, et al. Machining simulation of Ti6Al4V using coupled Eulerian-Lagrangian approach and a constitutive model considering the state of stress. Simul Model Pract Theory 2021;110:102312.

15

Meyghani B. A modified friction model and its application in finite-element analysis of friction stir welding process. J Manuf Process 2021;72:29-47.

16

Kumar CS, Zeman P, Polcar T. A 2D finite element approach for predicting the machining performance of nanolayered TiAlCrN coating on WC-Co cutting tool during dry turning of AISI 1045 steel. Ceram Int 2020;46(16):25073-88.

17

Jing X, Lv R, Chen Y, et al. Modelling and experimental analysis of the effects of run out, minimum chip thickness and elastic recovery on the cutting force in micro-end-milling. Int J Mech Sci 2020;176:105540.

18

Nemetz AW, Daves W, Klünsner T, et al. FE temperature- and residual stress prediction in milling inserts and correlation with experimentally observed damage mechanisms. J Mater Process Technol 2018;256:98-108.

19

Jiang X, Kong X, He S, et al. Modeling the superposition of residual stresses induced by cutting force and heat during the milling of thin-walled parts. J Manuf Process 2021;68:356-70.

20

Gao X, Cheng X, Ling S, et al. Research on optimization of micro-milling process for curved thin wall structure. Precis Eng 2022;73:296-312.

21

Bergs T, Gierlings S, Auerbach T, et al. The concept of digital twin and digital shadow in manufacturing. Procedia CIRP 2020;101:81-4.

22

Chen N, Li HN, Wu J, et al. Advances in micro milling: From tool fabrication to process outcomes. Int J Mach Tools Manuf 2021;160:103670.

23

Soliman HA, Shash AY, El-Hossainy TM, et al. Cutting forces and crater wear prediction in orthogonal cutting using two approaches of finite element modeling. Eng Reports 2020;2(10):1-18.

24

Perl J. Modelling and simulation. Comput Sci Sport Res Pract 2014;38:110-53.

25

Jin X, Altintas Y. Prediction of micro-milling forces with finite element method. J Mater Process Technol 2012;212(3):542-52.

26

Davoudinejad A, Li D, Zhang Y, et al. Optimization of corner micro end milling by finite element modelling for machining thin features. Procedia CIRP 2019;82:362-7.

27

Sabkhi N, Moufki A, Nouari M, et al. Prediction of the hobbing cutting forces from a thermomechanical modeling of orthogonal cutting operation. J Manuf Process 2016;23:1-12.

28

Grissa R, Zemzemi F, Fathallah R. Three approaches for modeling residual stresses induced by orthogonal cutting of AISI316L. Int J Mech Sci 2018;135:253-60.

29

Zhang C, Choi H. Study of segmented chip formation in cutting of high-strength lightweight alloys. Int J Adv Manuf Technol 2021;112(9–10):2683-703.

30

Sridhar P, Prieto JMR, de Payrebrune KM. Discretization approaches to model orthogonal cutting with lagrangian, arbitrary lagrangian eulerian, particle finite element method and smooth particle hydrodynamics formulations. Procedia CIRP 2020;93:1496-501.

31

Ducobu F, Arrazola PJ, Rivière-Lorphèvre E, et al. The CEL method as an alternative to the current modelling approaches for Ti6Al4V orthogonal cutting simulation. Procedia CIRP 2017;58:245-50.

32

Gao Y, Ko JH, Lee HP. 3D eulerian finite element modelling of end milling. Procedia CIRP 2018;77:159-62.

33

Agmell M, Bushlya V, M’Saoubi R, et al. Investigation of mechanical and thermal loads in pcBN tooling during machining of Inconel 718. Int J Adv Manuf Technol 2020;107(3–4):1451-62.

34

Liu Y, Agmell M, Xu D, et al. Numerical contribution to segmented chip effect on residual stress distribution in orthogonal cutting of Inconel718. Int J Adv Manuf Technol 2020;109(3–4):993-1005.

35

Zhang Y, Outeiro JC, Mabrouki T. On the selection of Johnson-Cook constitutive model parameters for Ti-6Al-4V using three types of numerical models of orthogonal cutting. Procedia CIRP 2015;31:112-7.

36

Ducobu F, Rivière-Lorphèvre E, Filippi E. Finite element modelling of 3D orthogonal cutting experimental tests with the Coupled Eulerian-Lagrangian (CEL) formulation. Finite Elem Anal Des 2017;134:27-40.

37

Vovk A, Sölter J, Karpuschewski B. Finite element simulations of the material loads and residual stresses in milling utilizing the CEL method. Procedia CIRP 2020;87:539-44.

38

Liu Y, Xu D, Agmell M, et al. Investigation on residual stress evolution in nickel-based alloy affected by multiple cutting operations. J Manuf Process 2021;68:818-33.

39

Vovk A, Sölter J, Karpuschewski B. Numerical investigation of the influence of multiple loads on material modifications during hard milling. Procedia CIRP 2021;102:500-5.

40

Wan M, Wen DY, Ma YC, et al. On material separation and cutting force prediction in micro milling through involving the effect of dead metal zone. Int J Mach Tools Manuf 2019;146:103452.

41

Rodríguez JM, Carbonell JM, Cante JC, et al. Continuous chip formation in metal cutting processes using the Particle Finite Element Method (PFEM). Int J Solids Struct 2017;120:81-102.

42

Yue CX, Cai CB, Huang C, et al. The latest progress of finite element simulation of cutting process. J Syst Simul 2016;28(4):815-26, Chinese.

43

Johnson GR, Cook WH. A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures. Proc 7th Int Symp Ballist Netherlands 1983:541-7.

44

Chen G, Lu L, Ke Z, et al. Influence of constitutive models on finite element simulation of chip formation in orthogonal cutting of Ti-6Al-4V alloy. Procedia Manuf 2019;33:530-7.

45

Zerilli FJ, Armstrong RW. Dislocation-mechanics-based constitutive relations for material dynamics calculations. J Appl Phys 1987;61(5):1816-25.

46

Oxley PLB. Mechanics of machining: an analytical approach to assessing machinability. Ellis Horwood Limited, Chichester 1989;150(1–2):380-1.

47

Nemat-Nasser S, Guo WG, Nesterenko VF, et al. Dynamic response of conventional and hot isostatically pressed Ti-6Al-4V alloys: Experiments and modeling. Mech Mater 2001;33(8):425-39.

48

Jiang F, Li J, Sun J, et al. Al7050-T7451 turning simulation based on the modified power-law material model. Int J Adv Manuf Technol 2010;48(9–12):871-80.

49

Afazov S, Ratchev S, Segal J. FE modelling of size-effect in micro-machining using the strain gradient plasticity theory. 7th Int Conf Multi-Material Micro-Manufacture, Bg En Bresse Oyonnax Fr 2010:178-83.

50

No T, Gomez M, Karandikar J, et al. Propagation of Johnson-Cook flow stress model uncertainty to milling force uncertainty using finite element analysis and time domain simulation. Procedia Manuf 2021;53:223-35.

51

Mehta S, Singh G, Saini A, et al. Finite element analysis of face milling of Ti-6Al-4 V alloy considering cutting forces and cutting temperatures. Mater Today Proc 2022;50(5):2315-20.

52

Mebrahitom A, Choon W, Azhari A. Side milling machining simulation using finite element analysis: Prediction of cutting forces. Mater Today Proc 2017;4(4):5215-21.

53

Huang X, Xu J, Chen M, et al. Finite element modeling of high-speed milling 7050–T7451 alloys. Procedia Manuf 2020;43:471-8.

54

Reddy LVK, Prasad BS, Rajasekhar M. Analysis of vibration assisted dry end milling using 3D FE simulation-An investigational approach. Mater Today Proc 2021;45:3075-84.

55

Schumski L, Paulsen T, Sölter J, et al. Finite element simulation of low frequency vibration-assisted drilling with modification of oscillation modes. Procedia CIRP 2021;102:168-73.

56

Yıldız A, Kurt A, Yağmur S. Finite element simulation of drilling operation and theoretical analysis of drill stresses with the deform-3D. Simul Model Pract Theory 2020;104.

57

Yang D, Liu Z, Ren X, et al. Hybrid modeling with finite element and statistical methods for residual stress prediction in peripheral milling of titanium alloy Ti-6Al-4V. Int J Mech Sci 2016;108–109:29-38.

58

Khajehzadeh M, Boostanipour O, Reza RM. Finite element simulation and experimental investigation of residual stresses in ultrasonic assisted turning. Ultrasonics 2020;108:106208.

59

Wu Q, Xie DJ, Si Y, et al. Simulation analysis and experimental study of milling surface residual stress of Ti-10V-2Fe-3Al. J Manuf Process 2018;32:530-7.

60

Jia Z, Lu X, Gu H, et al. Deflection prediction of micro-milling Inconel 718 thin-walled parts. J Mater Process Technol 2021;291:117003.

61

Hanson C, Hiwase P, Chen X, et al. Experimental investigation and numerical simulation of burr formation in micro-milling of polycarbonates. Procedia Manuf 2019;34:293-304.

62

Maurel-Pantel A, Fontaine M, Thibaud S, et al. 3D FEM simulations of shoulder milling operations on a 304L stainless steel. Simul Model Pract Theory 2012;22:13-27.

63

Yuan Y, Jing X, Ehmann KF, et al. Modeling of cutting forces in micro end-milling. J Manuf Process 2018;31:844-58.

64

Zhou T, He L, Zou Z, et al. Three-dimensional turning force prediction based on hybrid finite element and predictive machining theory considering edge radius and nose radius. J Manuf Process 2020;58:1304-17.

65

Teng X, Huo D, Chen W, et al. Finite element modelling on cutting mechanism of nano Mg/SiC metal matrix composites considering cutting edge radius. J Manuf Process 2018;32:116-26.

66

Fan YH, Wang T, Hao ZP, et al. Surface residual stress in high speed cutting of superalloy Inconel718 based on multiscale simulation. J Manuf Process 2018;31:480-93.

67

Korkmaz ME. Verification of Johnson-Cook parameters of ferritic stainless steel by drilling process: Experimental and finite element simulations. J Mater Res Technol 2020;9(3):6322-30.

68

Wu Y, Chen N, Bian R, et al. Investigations on burr formation mechanisms in micro milling of high-aspect-ratio titanium alloy ti-6al-4 v structures. Int J Mech Sci 2020;185:105884.

69

Afazov S, Uzunov K. Comparative study of stability predictions in micro-milling by using cutting force models and direct cutting force measurements. Procedia CIRP 2020;101:118-21.

70

Li YF, Zeng XG. Dynamic constitutive model and numerical simulation of TC4 titanium alloy based on dislocation theory. Chinese J Nonferrous Met 2019;29(5):972-82, Chinese.

71

Adibi-Sedeh AH, Madhavan V, Bahr B. Extension of Oxley’s analysis of machining to use different materials models. J Manuf Sci Eng 2003;125(4):656-66.

72

Lalwani DI, Mehta NK, Jain PK. Extension of Oxley’s predictive machining theory for Johnson and Cook flow stress model. J Mater Process Technol 2009;209(12–13):5305-12.

73

Sima M, Özel T. Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti-6Al-4V. Int J Mach Tools Manuf 2010;50(11):943-60.

74

Afazov S, Ratchev S, Segal J. Determination of cutting forces and process stability in micro-milling of Ti6Al4V alloy by considering the size-effect phenomenon. Micro Nanosyst 2011;3(3):199-209.

75

Cui X, Zhao B, Jiao F, et al. Chip formation and its effects on cutting force, tool temperature, tool stress, and cutting edge wear in high- and ultra-high-speed milling. Int J Adv Manuf Technol 2016;83(1–4):55-65.

76
Kiliçaslan C. Modelling and simulation of metal cutting by finite element method. M.Sc. thesis. Izmir Institute of Technology, Izmir, Turkey 2009.
77

Kara F, Aslantaş K, Çiçek A. Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network. Appl Soft Comput 2016;38:64-74.

78

Malakizadi A, Gruber H, Sadik I, et al. An FEM-based approach for tool wear estimation in machining. Wear 2016;368–369:10-24.

79

Calamaz M, Coupard D, Girot F. A new material model for 2D numerical simulation of serrated chip formation when machining titanium alloy Ti-6Al-4V. Int J Mach Tools Manuf 2008;48(3–4):275-88.

80

Andrade U, Meyers MA, Vecchio KS, et al. Dynamic recrystallization in high-strain, high-strain-rate plastic deformation of copper. Acta Metall Mater 1994;42(9):3183-95.

81

Rhim SH, Oh SI. Prediction of serrated chip formation in metal cutting process with new flow stress model for AISI 1045 steel. J Mater Process Technol 2006;171(3):417-22.

82

Anurag S, Guo YB. A modified micromechanical approach to determine flow stress of work materials experiencing complex deformation histories in manufacturing processes. Int J Mech Sci 2007;49(7):909-18.

83

Samantaray D, Mandal S, Borah U, et al. A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel. Mater Sci Eng A 2009;526(1–2):1-6.

84

Gurusamy MM, Rao BC. On the performance of modified Zerilli-Armstrong constitutive model in simulating the metal-cutting process. J Manuf Process 2017;28:253-65.

85

Imbrogno S, Rinaldi S, Umbrello D, et al. A physically based constitutive model for predicting the surface integrity in machining of Waspaloy. Mater Des 2018;152:140-55.

86

Bissacco G, Hansen HN, Slunsky J. Modelling the cutting edge radius size effect for force prediction in micro milling. CIRP Ann - Manuf Technol 2008;57(1):113-6.

87

Yue CX, Wang YW, Gao HN, et al. Research on three-dimensional finite element simulation of convex surface splicing die milling process. Aeronaut Manuf Technol 2018;61:34-42, Chinese.

88

Ducobu F, Arrazola PJ, Rivière-Lorphèvre E, et al. On the selection of an empirical material constitutive model for the finite element modeling of Ti6Al4V orthogonal cutting, including the segmented chip formation. Int J Mater Form 2021;14(3):361-74.

89

Denguir LA, Outeiro JC, Fromentin G, et al. A physical-based constitutive model for surface integrity prediction in machining of OFHC copper. J Mater Process Technol 2017;248:143-60.

90

Duan Z, Li C, Ding W, et al. Milling force model for aviation aluminum alloy: Academic insight and perspective analysis. Chinese J Mech Eng (English Ed) 2021;34(1):18.

91

Zorev NN. Inter-relationship between shear processes occurring along tool face and shear plane in metal cutting. Int Res Prod Eng ASME, New York 1963;42-9.

92

Parida AK, Maity K. FEM analysis and experimental investigation of force and chip formation on hot turning of Inconel 625. Def Technol 2019;15(6):853-60.

93

Zemzemi F, Rech J, Ben Salem W, et al. Identification of a friction model at tool/chip/workpiece interfaces in dry machining of AISI4142 treated steels. J Mater Process Technol 2009;209(8):3978-90.

94

Fezai N, Chaabani L, Niang NF, et al. Characterization of friction for the simulation of multi-pass orthogonal micro-cutting of 316L stainless steel. Procedia CIRP 2022;108:845-50.

95

Özel T. The influence of friction models on finite element simulations of machining. Int J Mach Tools Manuf 2006;46(5):518-30.

96

Childs THC. Friction modelling in metal cutting. Wear 2006;260(3):310-8.

97

Priest J, Ghadbeigi H, Ayvar-Soberanis S, et al. Effects of coefficient of friction coupled with a deformation dependent friction model in cutting simulations. Procedia CIRP 2021;102:429-34.

98

Liu S, Zhang H, Zhao L, et al. Coupled thermo-mechanical sticking-sliding friction model along tool-chip interface in diamond cutting of copper. J Manuf Process 2021;70:578-92.

99

Peng B, Bergs T, Schraknepper D, et al. Development and validation of a new friction model for cutting processes. Int J Adv Manuf Technol 2020;107:4357-69.

100

Duan C, Sun W, Fu C, et al. Modeling and simulation of tool-chip interface friction in cutting Al/SiCp composites based on a three-phase friction model. Int J Mech Sci 2018;142–143:384-96.

101

Storchak M, Möhring HC, Stehle T. Improving the friction model for the simulation of cutting processes. Tribol Int 2022;167:107376.

102

Yameogo D, Haddag B, Makich H, et al. A physical behavior model including dynamic recrystallization and damage mechanisms for cutting process simulation of the titanium alloy Ti-6Al-4V. Int J Adv Manuf Technol 2019;100(1–4):333-47.

103

Johnson GR, Cook WH. Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures. Eng Fract Mech 1985;21(1):31-48.

104

Zhang Z, Xu XL, Sun PC, et al. Prediction, compensation and verification of deformation in NC side milling of slender beams. Mach Tool & Hydraul 2021;49:31-5.

105

Cheng W, Outeiro J, Costes JP, et al. A constitutive model for Ti6Al4V considering the state of stress and strain rate effects. Mech Mater 2019;137:103103.

106

He Y, Li L, Wan M, et al. Three-dimensional finite element simulations of milling carbon/epoxy composites. Compos Struct 2022;282:115037.

107

Bai Y, Wierzbicki T. A new model of metal plasticity and fracture with pressure and Lode dependence. Int J Plast 2008;24(6):1071-96.

108

Hillerborg A, Modéer M, Petersson PE. Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements. Cem Concr Res 1976;6:773-81.

109
Abushawashi YM. Modeling of metal cutting as purposeful fracture of work material[dissertation]. Ann Arbor: Michigan State University; 2013. p. 264.
110

Mabrouki T, Girardin F, Asad M, et al. Numerical and experimental study of dry cutting for an aeronautic aluminium alloy (A2024–T351). Int J Mach Tools Manuf 2008;48(11):1187-97.

111

Carroll JT, Strenkowski JS. Finite element models of orthogonal cutting with application to single point diamond turning. Int J Mech Sci 1988;30(12):899-920.

112

Unger R, Arash B, Exner W, et al. Effect of temperature on the viscoelastic damage behaviour of nanoparticle/epoxy nanocomposites: Constitutive modelling and experimental validation. Polymer (Guildf) 2020;191:122265.

113

Harzallah M, Pottier T, Senatore J, et al. Numerical and experimental investigations of Ti-6Al-4V chip generation and thermo-mechanical couplings in orthogonal cutting. Int J Mech Sci 2017;134:189-202.

114

Aresh B, Khan FN, Haider J. Experimental investigation and numerical simulation of chip formation mechanisms in cutting rock-like materials. J Pet Sci Eng 2022;209:109869.

115

Chenegrin K, Roux JC, Helfenstein-Didier C, et al. 3D numerical simulation of heat transfer during dry drilling of Inconel 718. J Manuf Process 2021;64(January):1143-52.

116

Davies MA, Burns TJ, Evans CJ. On the dynamics of chip formation in machining hard metals. CIRP Ann - Manuf Technol 1997;46(1):25-30.

117

Semiatin SL, Rao SB. Shear localization during metal cutting. Mater Sci Eng 1983;61(2):185-92.

118

Nakayama K, Arai M, Kanda T. Machining Characteristics of Hard Materials. CIRP Ann - Manuf Technol 1988;37(1):89-92.

119

Vyas A, Shaw MC. Mechanics of saw-tooth chip formation in metal cutting. J Manuf Sci Eng Trans ASME 1999;121(2):163-72.

120

Shah SR, Liu G, Özel T. Finite element simulations of chip serration in titanium alloy cutting by considering material failure. Procedia CIRP 2019;82:320-5.

121

Ducobu F, Rivière-Lorphèvre E, Filippi E. Numerical contribution to the comprehension of saw-toothed Ti6Al4V chip formation in orthogonal cutting. Int J Mech Sci 2014;81:77-87.

122

Shuang F, Chen X, Ma W. Numerical analysis of chip formation mechanisms in orthogonal cutting of Ti6Al4V alloy based on a CEL model. Int J Mater Form 2018;11(2):185-98.

123

Childs THC, Arrazola PJ, Aristimuno P, et al. Ti6Al4V metal cutting chip formation experiments and modelling over a wide range of cutting speeds. J Mater Process Technol 2018;255:898-913.

124

Wang B, Xiao X, Astakhov VP, et al. The effects of stress triaxiality and strain rate on the fracture strain of Ti6Al4V. Eng Fract Mech 2019;219(August):106627.

125

Wang B, Liu Z. Investigations on the chip formation mechanism and shear localization sensitivity of high-speed machining Ti6Al4V. Int J Adv Manuf Technol 2014;75(5–8):1065-76.

126

Otalora-Ortega H, Aristimuño Osoro P, Arrazola AP. Uncut chip geometry determination for cutting forces prediction in orthogonal turn-milling operations considering the tool profile and eccentricity. Int J Mech Sci 2021;198:106351.

127

Uçak N, Çiçek A, Oezkaya E, et al. Finite element simulations of cutting force, torque, and temperature in drilling of Inconel 718. Procedia CIRP 2019;82:47-52.

128

Thi-Hoa P, Thi-Bich M, Van-Canh T, et al. A study on the cutting force and chip shrinkage coefficient in high-speed milling of A6061 aluminum alloyPham. Int J Adv Manuf Technol 2018;98(1–4):177-88.

129

Özel T, Altan T. Process simulation using finite element method-prediction of cutting forces, tool stresses and temperatures in high-speed flat end milling. Int J Mach Tools Manuf 2000;40(5):713-38.

130

Caudill J, Schoop J, Jawahir IS. Numerical modeling of cutting forces and temperature distribution in high speed cryogenic and flood-cooled milling of Ti-6Al-4V. Procedia CIRP 2019;82:83-8.

131

Venkatarao K. Power consumption optimization strategy in micro ball-end milling of D2 steel via TLBO coupled with 3D FEM simulation. Meas J Int Meas Confed 2019;132:68-78.

132

Karpat Y. A modified material model for the finite element simulation of machining titanium alloy Ti-6Al-4V. Mach Sci Technol 2010;14(3):390-410.

133

Kaltenbrunner T, Krückl HP, Schnalzger G, et al. Differences in evolution of temperature, plastic deformation and wear in milling tools when up-milling and down-milling Ti6Al4V. J Manuf Process 2022;77:75-86.

134

Attanasio A, Ceretti E, Outeiro J, et al. Numerical simulation of tool wear in drilling Inconel 718 under flood and cryogenic cooling conditions. Wear 2020;458–459:203403.

135

Wang L, Liu H, Huang C, et al. Three-dimensional transient cutting tool temperature field model based on periodic heat transfer for high-speed milling of compacted graphite iron. J Clean Prod 2021;322:129106.

136

Naskar S, Banerjee B. A mixed finite element based inverse approach for residual stress reconstruction. Int J Mech Sci 2021;196:106295.

137

Shi B, Abboud E, Attia MH, et al. Effect of chip segmentation on machining-induced residual stresses during turning of Ti6Al4V. Procedia CIRP 2022;108:424-9.

138

Zhao B, Guo X, Bie W, et al. Thermo-mechanical coupling effect on surface residual stress during ultrasonic vibration-assisted forming grinding gear. J Manuf Process 2020;59:19-32.

139

Li J, Wang S. Distortion caused by residual stresses in machining aeronautical aluminum alloy parts: recent advances. Int J Adv Manuf Technol 2017;89(1–4):997-1012.

140

Weber D, Kirsch B, Chighizola CR, et al. Investigation on the scale effects of initial bulk and machining induced residual stresses of thin walled milled monolithic aluminum workpieces on part distortions: experiments and finite element prediction model. Procedia CIRP 2021;102:337-42.

141

Liu X, DeVor RE, Kapoor SG, et al. The mechanics of machining at the microscale: Assessment of the current state of the science. J Manuf Sci Eng 2004;126(4):666-78.

142

Zeng HH, Yan R, Peng FY, et al. An investigation of residual stresses in micro-end-milling considering sequential cuts effect. Int J Adv Manuf Technol 2017;91(9–12):3619-34.

143

Peng Y, Zhao H, Ye J, et al. Multiscale 3D finite element analysis of aluminum matrix composites with nanoµ hybrid inclusions. Compos Struct 2022;288:115425.

144

Choi JH, Kim H, Kim JY, et al. Micro-cantilever bending tests for understanding size effect in gradient elasticity. Mater Des 2022;214:110398.

145

Lazoglu I, Mamedov A. Deformation of thin parts in micromilling. CIRP Ann - Manuf Technol 2016;65(1):117-20.

146

Jin Y, Wang Y, Zhang X, et al. Micro-milling of fused silica based on instantaneous chip thickness. J Mater Process Technol 2020;285:116786.

147

Niu Z, Jiao F, Cheng K. An innovative investigation on chip formation mechanisms in micro-milling using natural diamond and tungsten carbide tools. J Manuf Process 2018;31:382-94.

148

Dinesh D, Swaminathan S, Chandrasekar S, et al. An intrinsic size-effect in machining due to the strain gradient. ASME Int Mech Eng Congr Expo 2001;12:197-204.

149

Lai X, Peng L, Hu P, et al. Material behavior modelling in micro/meso-scale forming process with considering size/scale effects. Comput Mater Sci 2008;43(4):1003-9.

150

Boswell B, Islam MN, Davies IJ. A review of micro-mechanical cutting. Int J Adv Manuf Technol 2018;94(1–4):789-806.

151

Sun Z, Zhang T, Li P, et al. Analytical modelling of the trans-scale cutting forces in diamond cutting of polycrystalline metals considering material microstructure and size effect. Int J Mech Sci 2021;204:106575.

152

Feng G, Sagapuram D. Size effect and friction in cutting of metals on the small scale. CIRP Ann - Manuf Technol 2020;69(1):77-80.

153

Wang G, Yu T, Zhou X, et al. Investigation on minimum uncut chip thickness and size effect in micro milling of glow discharge polymer (GDP). J Manuf Process 2022;84:786-97.

154

Chen N, Chen M, Wu C, et al. Cutting surface quality analysis in micro ball end-milling of KDP crystal considering size effect and minimum undeformed chip thickness. Precis Eng 2017;50:410-20.

155

Kieren-Ehses S, Böhme L, Morales-Rivas L, et al. The influence of the crystallographic orientation when micro machining commercially pure titanium: A size effect. Precis Eng 2021;72:158-71.

156

Bach DP, Brancherie D, Cauvin L. Size effect in nanocomposites: XFEM/level set approach and interface element approach. Finite Elem Anal Des 2019;165:41-51.

157

Srinivasa YV, Shunmugam MS. Mechanistic model for prediction of cutting forces in micro end-milling and experimental comparison. Int J Mach Tools Manuf 2013;67:18-27.

158

Davoudinejad A, Tosello G, Parenti P, et al. 3D finite element simulation of micro end-milling by considering the effect of tool run-out. Micromachines 2017;8(6):1-20.

159

Cai S, Cai Z, Yao B, et al. Identifying the transient milling force coefficient of a slender end-milling cutter with vibrations. J Manuf Process 2021;67:262-74.

160

Wojciechowski S, Matuszak M, Powałka B, et al. Prediction of cutting forces during micro end milling considering chip thickness accumulation. Int J Mach Tools Manuf 2019;147:103466.

161

Balázs BZ, Geier N, Pereszlai C, et al. Analysis of cutting force and vibration at micro-milling of a hardened steel. Procedia CIRP 2021;99:177-82.

162

Thepsonthi T, Özel T. Experimental and finite element simulation based investigations on micro-milling Ti-6Al-4V titanium alloy: Effects of cBN coating on tool wear. J Mater Process Technol 2013;213(4):532-42.

163

Oliaei SNB, Karpat Y, Davim JP, et al. Micro tool design and fabrication: A review. J Manuf Process 2018;36:496-519.

164

Mamedov A, Lazoglu I. Thermal analysis of micro milling titanium alloy Ti-6Al-4V. J Mater Process Technol 2016;229:659-67.

165

Schewe M, Wilbuer H, Menzel A. Simulation of wear and effective friction properties of microstructured surfaces. Wear 2021;464–465:203491.

166

Pan Z, Liang SY, Garmestani H. Finite element simulation of residual stress in machining of Ti-6Al-4V with a microstructural consideration. Proc Inst Mech Eng Part B J Eng Manuf 2019;233(4):1103-11.

167

Styger G, Laubscher RF, Oosthuizen GA. Effect of constitutive modeling during finite element analysis of machining-induced residual stresses in Ti6Al4V. Procedia CIRP 2014;13:294-301.

168

Salahshoor M, Guo YB. Finite element simulation and experimental validation of residual stresses in high speed dry milling of biodegradable Mg-Ca alloys. Procedia CIRP 2014;14:281-6.

169

Xu X, Zhang J, Liu H, et al. Grain refinement mechanism under high strain-rate deformation in machined surface during high speed machining Ti6Al4V. Mater Sci Eng A 2019;752(February):167-79.

170

Liu H, Xu X, Zhang J, et al. The state of the art for numerical simulations of the effect of the microstructure and its evolution in the metal-cutting processes. Int J Mach Tools Manuf 2022;177:103890.

171

Wang Q, Liu Z, Wang B, et al. Evolutions of grain size and micro-hardness during chip formation and machined surface generation for Ti-6Al-4V in high-speed machining. Int J Adv Manuf Technol 2016;82(9–12):1725-36.

172

Liu H, Zhang J, Xu B, et al. Whole process analysis of microstructure evolution during chip formation of high-speed machining OFHC copper. J Manuf Process 2021;66:470-82.

173

Heininen A, Prod’Hon R, Mokhtarian H, et al. Finite element modelling of temperature in cylindrical grinding for future integration in a digital twin. Procedia CIRP 2021;104:875-80.

174

Finkeldey F, Saadallah A, Wiederkehr P, et al. Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. Eng Appl Artif Intell 2020;94:103753.

175

Landwehr M, Schmid S, Holla V, et al. The finite cell method for the prediction of machining distortion caused by initial residual stresses in milling. Procedia CIRP 2021;102:144-9.

176

Hinchy EP, Carcagno C, O’Dowd NP, et al. Using finite element analysis to develop a digital twin of a manufacturing bending operation. Procedia CIRP 2020;93:568-74.

177

Saadallah A, Finkeldey F, Morik K, et al. Stability prediction in milling processes using a simulation-based Machine Learning approach. Procedia CIRP 2018;72:1493-8.

178

Zhai S, Zhang P, Xian Y, et al. Effective thermal conductivity of polymer composites: Theoretical models and simulation models. Int J Heat Mass Transf 2018;117:358-74.

179

Schmidt J, Marques MRG, Botti S, et al. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater 2019;5(1).

180

Yan B, Gao R, Liu P, et al. Optimization of thermal conductivity of UO2–Mo composite with continuous Mo channel based on finite element method and machine learning. Int J Heat Mass Transf 2020;159:120067.

Chinese Journal of Aeronautics
Pages 199-232
Cite this article:
WANG L, YUE C, LIU X, et al. Conventional and micro scale finite element modeling for metal cutting process: A review. Chinese Journal of Aeronautics, 2024, 37(2): 199-232. https://doi.org/10.1016/j.cja.2023.03.004

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Received: 16 November 2022
Revised: 08 December 2022
Accepted: 12 February 2023
Published: 08 March 2023
© 2023 Chinese Society of Aeronautics and Astronautics.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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