Over a century, metal cutting has been observed as a vital process in the domain of manufacturing. Among the numerous available metal-cutting processes, milling has been considered as one of the most employable processes to machine a variety of engineering materials productively. In the milling process, material removal occurs when the workpiece is fed against a rotating tool with multiple cutting edges. In order to maximize the profitability of metal cutting operations, it is essential that the various input and output variable relationships are analyzed and optimized. The experimental method of studying milling processes is costly and time demanding, particularly when a large variety of elements such as cutting tool shape, materials, cutting conditions, and so on, are included. Due to these issues, other alternatives emerged in the form of mathematical simulations that employ numerical methods. The finite element approaches have well-proven to be the most practical and commonly utilized numerical methods. The finite element model (FEM) can be used to determine the various physical interactions occurring during the machining process along with the prediction of various milling characteristics, such as cutting forces, cutting temperature, stresses, etc., with the help of milling inputs. In the present article, various research studies in the broad milling process domain practiced with numerous finite element approaches have been critically reviewed and reported. It further highlights the several experimental and finite element approaches-based research studies that attempted to analyze and optimize the overall performance of the different milling processes. In recent years, various investigators have explored numerous ways to enhance milling performance by probing the different factors that influence the quality attributes. Some of the studies have also been found to be focused on the economic impacts of milling and various process inputs that affect milling performance. Furthermore, various milling factors’ impact on the performance characteristics are presented and critically discussed. The issues related to the recent improvements in tool-work interaction modeling, experimental techniques for acquiring various milling performance measures, and the aspects of turn and micro-milling with finite element-based modeling have been further highlighted. Among the various available classifications in the milling process as employed in industries, face milling is more strongly established compared to other versions such as end milling, helical milling, gear milling, etc. The final section of this research article explores the various research aspects and outlines future research directions.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00417-x
1 Aachen R. RWTH edition (1876) The University of Manchester. Nature 14(350):225-226.https://doi.org/10.1038/014225a0
2 McDougall D (1966) Machine tool output, 1861-1910. In Output, employment, and productivity in the United States after 1800 (pp. 497-519). NBER
3 De Nicola L (2013) From mechanical arts to the philosophy of technology. Econ Innov New Technol 22(7):726-750
4 Raicu L, Marin D (2008) Design aspects in machine tools evolution. J Proc Int Conf Manuf Syst 3:59-64
5 Balázs BZ, Geier N, Takács M et al (2021) A review on micro-milling: recent advances and future trends. Int J Adv Manuf Technol 112(3/4):655-684
6 Friedrich CR, Vasile MJ (1996) Development of the micromilling process for high-aspect-ratio microstructures. J Microelectromech Syst 5(1):33-38
7 Rosli AM, Jamaludin AS, Nizar M et al (2020) State of the art on micromilling hard to cut material. Proc Mech Eng Res Day 2020:99-101
8 Mamedov A (2021) Micro milling process modeling: a review. Manuf Rev 8(3):1-23
9 Altan T (2000) Process simulation using finite element method—prediction of cutting forces, tool stresses and temperatures in high-speed flat end milling. Int J Mach Tool Manuf 40(5):713-738
10 Li B, Zhang S, Zhang Q et al (2019) Simulated and experimental analysis on serrated chip formation for hard milling process. J Manuf Process 44:337-348
11 Li BX, Zhang S, Zhang J (2019) Plastic deformation and grain refinement in surface layer induced by thermo-mechanical loads for hard milling process. Proc Inst Mech Eng Part B J Eng Manuf 233(10):2033-2046
12 Marimuthu KP, Prsada HP, Kumar CSC (2019) Finite element modelling to predict machining induced residual stresses in the end milling of hard to machine Ti6Al4V alloy. Period Eng Nat Sci 7(1):1-11
13 Bergmann B, Denkena B, Ko J (2015) Development of cutting edge geometries for hard milling operations. CIRP J Manuf Sci Technol 8:43-52
14 Gopikrishnan P, Akbar A, Asokan A et al (2018) Numerical modelling and optimization of surface finish during peripheral milling of AISI 4340 steel using RSM. Mater Today Proc 5(11):24612-24621
15 Çiçek A, Kara F, Kivak T et al (2013) Evaluation of machinability of hardened and cryo-treated AISI H13 hot work tool steel with ceramic inserts. Int J Refract Met Hard Mater 41:461-469
16 Ali MH, Khidhir BA, Ansari MNM et al (2013) FEM to predict the effect of feed rate on surface roughness with cutting force during face milling of titanium alloy. HBRC J 9(3):263-269
17 Silva RBD, Machado ÁR, Ezugwu EO et al (2007) Increasing productivity in high speed machining of Ti-6Al-4V alloy under high pressure coolant supply. Int J Mach Mach Mater 2(2):222-232
18 Huang X, Xu J, Chen M et al (2020) Finite element modeling of high-speed milling 7050-T7451 alloys. Procedia Manuf 43:471-478
19 Guo C, Zhang C, Bai H et al (2019) Influence of milling parameters on milling performance of 300 M ultra high strength steel. IOP Conf Ser Mater Sci Eng 493(1):012065. https://doi.org/10.1088/1757-899X/493/1/012065
20 Li A, Zhao J, Luo H et al (2012) Progressive tool failure in high-speed dry milling of Ti-6Al-4V alloy with coated carbide tools. Int J Adv Manuf Technol 58(5/8):465-478
21 Daymi A, Boujelbene M, Ben AA et al (2011) Surface integrity in high speed end milling of titanium alloy Ti-6Al-4V. Mater Sci Technol 27(1):387-394
22 Chen CH, Wang YC, Lee BY (2013) The effect of surface roughness of end-mills on optimal cutting performance for high-speed machining. J Mech Eng 59(2):124-134
23 Hall S, Loukaides E, Newman ST et al (2020) Computational and experimental investigation of cutting tool geometry in machining titanium Ti-6Al-4V. Procedia CIRP 86:139-144
24 Pelayo GU (2019) Modelling of static and dynamic milling forces in inclined operations with circle-segment end mills. Precis Eng 56:123-135
25 Meng L, Zhang C, Ren Y et al (2017) Study on the power consumption of different milling modes and number of inserts in face milling processes. In: IEEE international conference on automation science and engineering, pp 1475-1480
26 De Geare J (2015) Milling operations. Guid to Oilw Fish Oper. https://doi.org/10.1016/b978-0-12-420004-3.00012-5
27 Liu Y, Zhou W, Li X et al (2020) Experimental investigations on cutting force and temperature in milling process of copper foam with high porosity. Int J Adv Manuf Technol 108(3):759-767
28 Vivek J, Dr Alok S (2015) Analysis of lubrication in milling machine. Int J Eng Tech Res 4(4):256-259
29 Cai C, Liang X, An Q et al (2020) Cooling/lubrication performance of dry and supercritical CO2-based minimum quantity lubrication in peripheral milling Ti-6Al-4V. Int J Precis Eng Manuf - Green Technol 8:405-421
30 Kim DY, Kim DM, Park HW (2019) Numerical and experimental study of end-milling process of titanium alloy with a cryogenic internal coolant supply. Int J Adv Manuf Technol 105(7/8):2957-2975
31 Ziberov M, Da Silva MB, Jackson M et al (2016) Effect of cutting fluid on micromilling of Ti-6Al-4V titanium alloy. Procedia Manuf 5:332-347
32 Varadarajan AS, Philip PK, Ramamoorthy B (2002) Investigations on hard turning with minimal cutting fluid application (HTMF) and its comparison with dry and wet turning. Int J Mach Tools Manuf 42(2):193-200
33 Bräunig M, Regel J, Glanzel J et al (2019) Effects of cooling lubricant on the thermal regime in the working space of machine tools. Procedia Manuf 33:327-334
34 Tlhabadira I, Daniyan IA, Masu L et al (2019) Process design and optimization of surface roughness during M200 TS milling process using the Taguchi method. Procedia CIRP 84:868-873
35 Imani L, Rahmani HA, Hamzeloo R et al (2020) Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA. Proc Inst Mech Eng Part B J Eng Manuf 234(5):920-932
36 Hrennikoff A (1941) Solution of problems of elasticity by the framework method. J Appl Mech 8(4):169-175
37 Paul R, Paddison SJ (2003) Variational methods for the solution of the Ornstein-Zernicke equation in inhomogeneous systems. Phys Rev E - Stat Phys Plasmas Fluids Relat Interdiscip Top 67(1):11. https://doi.org/10.1103/PhysRevE.67.016108
38 Hinton E, Irons B (1968) Least squares smoothing of experimental data using finite elements. Strain 4(3):24-27
39 Strang G, Fix GJ (1973). An analysis of the finite element method(Book- An analysis of the finite element method.). Englewood Cliffs, N. J., Prentice-Hall, Inc., 1973. p 318
40 Zienkiewicz OC (2013) The finite element method: its basis and fundamentals, Elsevier, Amsterdam. https://doi.org/10.1016/b978-1-85617-633-0.00020-4
41 Tay AO, Stevenson MG, De Vahl DG (1974) Using the finite element method to determine temperature distributions in orthogonal machining. Inst Mech Eng Proc 188(55):627-638
42 Muraka PD, Barrow G, Hinduja S (1979) Influence of the process variables on the temperature distribution in orthogonal machining using the finite element method. Int J Mech Sci 21(8):445-456
43 Holmquist TJ, Johnson GR (1991) Determination of constants and comparison of results for various constitutive models. Le J Phys IV 1(C3):853-860
44 GaoY HJ, Pueh H (2020) Meso-scale tool breakage prediction based on finite element stress analysis for shoulder milling of hardened steel. J Manuf Process 55:31-40
45 Özel T, Zeren E (2004) Determination of work material flow stress and friction for FEA of machining using orthogonal cutting tests. J Mater Process Technol 153-154(1/3):1019-1025
46 Bariani PP, Dal NT, Bruschi S (2004) Testing and modelling of material response to deformation in bulk metal forming. CIRP Ann Manuf Technol 53(2):573-595
47 Chaparro BM, Thuillier S, Menezes LF et al (2008) Material parameters identification: gradient-based, genetic and hybrid optimization algorithms. Comput Mater Sci 44(2):339-346
48 Hokeš F, Kala J, Hušek M et al (2016) Parameter identification for a multivariable nonlinear constitutive model inside ANSYS workbench. Procedia Eng 161:892-897
49 Germain G, Morel A, Braham-Bouchnak T (2013) Identification of material constitutive laws representative of machining conditions for two titanium alloys: Ti6Al4V and Ti555-3. J Eng Mater Technol Trans ASME 135(3):1-12
50 Melkote SN, Grzesik W, Quteiro J et al (2017) Advances in material and friction data for modelling of metal machining. CIRP Ann Manuf Technol 66(2):731-754
51 Özel T, Karpat Y (2007) Identification of constitutive material model parameters for high-strain rate metal cutting conditions using evolutionary computational algorithms. Mater Manuf Process 22(5):659-667
52 Childs THC (1997) Material property requirements for modelling metal machining. J Phys IV JP7(C3):XXI-XXXVI. https://doi.org/10.1051/jp4:1997301
53 Hor A, Morel F, Lou LJ et al (2013) Modelling, identification and application of phenomenological constitutive laws over a large strain rate and temperature range. Mech Mater 64:91-110
54 Denguir LA, Outeiro JC, Fromentin G et al (2016) Orthogonal cutting simulation of OFHC copper using a new constitutive model considering the state of stress and the microstructure effects. Procedia CIRP 46:238-241
55 Liu R, Salahshoor M, Melkote SN et al (2015) A unified material model including dislocation drag and its application to simulation of orthogonal cutting of OFHC copper. J Mater Process Technol 216:328-338
56 Outeiro JC, Umbrello D, M’Saoubi R et al (2015) Evaluation of present numerical models for predicting metal cutting performance and residual stresses. Mach Sci Technol 19(2):183-216
57 Chagnes A, Mialkowski C, Carré B et al (2001) Phase diagram of γ-butyrolactone-dimethyl-carbonate mixtures. Le Journal de Physique IV, 11(PR10), Pr10-27
58 Dang J, Zhang W, Yang Y et al (2010) Cutting force modeling for flat end milling including bottom edge cutting effect. Int J Mach Tools Manuf 50(11):986-997
59 Matsumura T, Usui E (2010) Predictive cutting force model in complex-shaped end milling based on minimum cutting energy. Int J Mach Tools Manuf 50(5):458-466
60 Pabst R, Fleischer J, Michna J (2010) Modelling of the heat input for face-milling processes. CIRP Ann Manuf Technol 59(1):121-124
61 Zhang YC, Mabrouki T, Nelias D et al (2011) Chip formation in orthogonal cutting considering interface limiting shear stress and damage evolution based on fracture energy approach. Finite Elem Anal Des 47(7):850-863
62 Akhtar MM, Xiang H, Chen WL et al (2011) Milling dynamics of flexible part with helix angle. Procedia Eng 23:792-798
63 Jin X, Altintas Y (2012) Prediction of micro-milling forces with finite element method. J Mater Process Techol 212(3):542-552
64 Malekian M, Mostofa MG, Park SS et al (2012) Modeling of minimum uncut chip thickness in micro machining of aluminum. J Mater Process Techol 212(3):553-559
65 Balogun VA, Mativenga PT (2013) Modelling of direct energy requirements in mechanical machining processes. J Clean Prod 41:179-186
66 Joliet R, Byfut A, Kersting P et al (2013) Validation of a heat input model for the prediction of thermomechanical deformations during NC milling. Procedia CIRP 8:403-408
67 Smolenicki D, Boos J, Kuster F et al (2014) In-process measurement of friction coefficient in orthogonal cutting. CIRP Ann Manuf Technol 63(1):97-100
68 Lazoglu I, Bugdayci B (2014) Thermal modelling of end milling. CIRP Ann Manuf Technol 63(1):113-116
69 Jiao L, Wang X, Qian Y et al (2015) Modelling and analysis for the temperature field of the machined surface in the face milling of aluminium alloy. Int J Adv Manuf Technol 81:1797-1808
70 Kara ME, Budak E (2015) Optimization of turn-milling processes. Procedia CIRP 33:476-483
71 Çal H, Küçükköse M (2015) The effect of aCN/TiAlN coating on tool wear, cutting force, surface finish and chip morphology in face milling of Ti6Al4V superalloy. Int J Refract Met Hard Mater 50:304-312
72 Malakizadi A, Gruber H, Sadik I et al (2016) An FEM-based approach for tool wear estimation in machining. Wear 368/369:10-24
73 Zhu L, Li H, Liu C (2016) Analytical modeling on 3D chip formation of rotary surface in orthogonal turn-milling. Arch Civ Mech Eng 16(4):590-604
74 Wu BH, Cui D, He XD et al (2016) Cutting tool temperature prediction method using analytical model for end milling. Chin J Aeronaut 29(6):1788-1794
75 Putz M, Schmidt G, Semmler U et al (2016) Modeling of heat fluxes during machining and their effects on thermal deformation of the cutting tool. Procedia CIRP 46:611-614
76 Mamedov A, Lazoglu I (2016) Thermal analysis of micro milling titanium alloy Ti-6Al-4V. J Mater Process Techol 229:659-667
77 Soori M, Arezoo B, Habibi M (2017) Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system. Int J Comput Appl Technol 55(4):308-321
78 Malghan RL, Rao KMC, ArunKumar S et al (2018) Effect of process parameters in face milling operation and analysis of cutting force using indirect method. Mater Manuf Process 33(13):1406-1414
79 Biermann D, Bücker M, Tiffe M et al (2017) Experimental investigations for a simulative optimization of the cutting edge design of twist drills used in the machining of Inconel 718. Procedia Manuf 14:8-16
80 Ducobu F, Rivière-Lorphèvre E, Filippi E (2017) Finite element modelling of 3D orthogonal cutting experimental tests with the Coupled Eulerian-Lagrangian (CEL) formulation. Finite Elem Anal Des 134:27-40
81 Stief P, Dantan J, Etienne A et al (2018) 3D Eulerian finite modelling of end milling. Procedia CIRP 77:159-162
82 Gulpak M, Wernsing H, Sölter J et al (2018) Compensation strategies for thermal effects in dry milling. In Biermann D, Hollmann F (eds) Thermal effects in complex machining processes, Springer, Berlin, pp 251-288
83 Karaguzel U, Budak E (2018) Investigating effects of milling conditions on cutting temperatures through analytical and experimental methods. J Mater Process Technol 262:532-540
84 Feng Y, Pan Z, Liang SY (2018) Temperature prediction in Inconel 718 milling with microstructure evolution. Int J Adv Manuf Technol 95(9/12):4607-4621
85 Geier N, Szalay T, Biró I (2018) Trochoid milling of carbon fibre-reinforced plastics (CFRP) Procedia CIRP 77:375-378
86 Wang SQ, Li JG, He CL et al (2019) A 3D analytical model for residual stress in flank milling process. Int J Adv Manuf Technol 104(9/12):3545-3565
87 Zhou R, Yang W (2019) Analytical modeling of machining-induced residual stresses in milling of complex surface. Int J Adv Manuf Technol 105:565-577
88 Otalora-Ortega H, Osoro PA, Arrazola APJ (2019) Analytical modeling of the uncut chip geometry to predict cutting forces in orthogonal centric turn-milling operations. Int J Mach Tools Manuf 144:103428. https://doi.org/10.1016/j.ijmachtools.2019.103428
89 Prasad VUSV, Rao KV, Balaji M et al (2020) FEA investigation of process parameters effect on machining characteristics in helical milling. Mater Today Proc 23:590-593
90 Augspurger T, Da Silva G, Schraknepper D et al (2020) Model-based monitoring of temperatures and heat flows in the milling cutter. Int J Adv Manuf Technol 107(9/10):4231-4238
91 Arizmendi M, Jiménez A (2019) Modelling and analysis of surface topography generated in face milling operations. Int J Mech Sci 163:105061. https://doi.org/10.1016/j.ijmecsci.2019.105061
92 Davoudinejad A, Li D, Zhang Y et al (2019) Optimization of corner micro end milling by finite element modelling for machining thin features. Procedia CIRP 82:362-367
93 Roushan A, Rao US, Vijayaraghavan L (2020) Prediction of cutting force in micro-end-milling by a combination of analytical and FEM method. J Micromanuf 3(1):28-38
94 Kryzhanivskyy V, Saoubi RM, Ståhl JE et al (2019) Tool-chip thermal conductance coefficient and heat flux in machining: theory, model and experiment. Int J Mach Tools Manuf 147:103468. https://doi.org/10.1016/j.ijmachtools.2019.103468
95 Sima M (2010) Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti-6Al-4V. Int J Mach Tool Manuf 50:943-960
96 Basturk S, Senbabaoglu F, Islam C et al (2010) Titanium machining with new plasma boronized cutting tools. CIRP Ann Manuf Technol 59(1):101-104
97 Rao B, Dandekar CR, Shin YC (2011) An experimental and numerical study on the face milling of Ti-6Al-4V alloy: tool performance and surface integrity. J Mater Process Technol 211(2):294-304
98 Thepsonthi T, Ulutan D, Kaftanog B (2011) Experiments and finite element simulations on micro-milling of Ti-6Al-4V alloy with uncoated and CBN coated micro-tools. CIRP Ann Manuf Technol 60:85-88
99 Thepsonthi T (2013) 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 213:532-542
100 Rech J, Arrazola PJ, Claudin C et al (2013) Characterisation of friction and heat partition coefficients at the tool-work material interface in cutting. CIRP Ann Manuf Technol 62(1):79-82
