Machining simulations are challenging with respect to both numerical issues and physical phenomena occurring during machining. The latter are mainly related to the description of the bulk material behaviour (plasticity) and surface properties (friction and wear). The aim of this paper is to present what is required for predictive models, depending on their scopes, as well as the needed developments for the future. The paper includes a short review of selected works that are relevant for this purpose as well as conclusions based on our own experience.
Accepté le :
Publié le :
Lars-Erik Lindgren 1 ; Ales Svoboda 1 ; Dan Wedberg 2 ; Mikael Lundblad 2
@article{CRMECA_2016__344_4-5_284_0, author = {Lars-Erik Lindgren and Ales Svoboda and Dan Wedberg and Mikael Lundblad}, title = {Towards predictive simulations of machining}, journal = {Comptes Rendus. M\'ecanique}, pages = {284--295}, publisher = {Elsevier}, volume = {344}, number = {4-5}, year = {2016}, doi = {10.1016/j.crme.2015.06.010}, language = {en}, }
TY - JOUR AU - Lars-Erik Lindgren AU - Ales Svoboda AU - Dan Wedberg AU - Mikael Lundblad TI - Towards predictive simulations of machining JO - Comptes Rendus. Mécanique PY - 2016 SP - 284 EP - 295 VL - 344 IS - 4-5 PB - Elsevier DO - 10.1016/j.crme.2015.06.010 LA - en ID - CRMECA_2016__344_4-5_284_0 ER -
Lars-Erik Lindgren; Ales Svoboda; Dan Wedberg; Mikael Lundblad. Towards predictive simulations of machining. Comptes Rendus. Mécanique, Volume 344 (2016) no. 4-5, pp. 284-295. doi : 10.1016/j.crme.2015.06.010. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2015.06.010/
[1] Recent advances in modelling of metal machining processes, CIRP Ann., Volume 62 (2013), pp. 695-718
[2] Modelling and simulation of high-speed machining, Int. J. Numer. Methods Eng., Volume 38 (1995), pp. 3675-3694
[3] Simulation of high-speed machining (T.J.R. Hughes; E. Onate; O.C. Zienkiewicz, eds.), Recent Developments in Finite Element Analysis, CIMNE Publications, Barcelona, 1994, pp. 62-77
[4] Toward a better understanding of tool wear effect through a comparison between experiments and SPH numerical modelling of machining hard materials, Int. J. Refract. Met. Hard Mater., Volume 27 (2009), pp. 595-604
[5] SPH method applied to high speed cutting modelling, Int. J. Mech. Sci., Volume 49 (2007), pp. 898-908
[6] SPH/FE modeling of cutting force and chip formation during thermally assisted machining of Ti6Al4V alloy, Comput. Mater. Sci., Volume 84 (2014), pp. 188-197
[7] Modelling and simulation of machining processes, Arch. Comput. Methods Eng., Volume 14 (2007), pp. 173-204
[8] Recent developments in chip control research and applications, CIRP Ann., Volume 42 (1993), p. 659
[9]
, Wiley–ISTE (2014), p. 480[10] Surface integrity in material removal processes: recent advances, CIRP Ann., Volume 60 (2011), pp. 603-626
[11] Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, Cambridge Press, 2000
[12] Metal Cutting Mechanics, CRC Press LLC, 2000
[13] Performance-based predictive models and optimization methods for turning operations and applications: part 2, assessment of chip forms/chip breakability, J. Manuf. Process., Volume 8 (2006), pp. 144-158
[14] Computational Welding Mechanics. Thermomechanical and Microstructural Simulations, Woodhead Publishing, 2007
[15] Calibration, validation, and sensitivity analysis: what's what, Reliab. Eng. Syst. Saf., Volume 91 (2006), pp. 1331-1357
[16] Verification, validation, and predictive capability in computational engineering and physics, Appl. Mech. Rev., Volume 57 (2004), pp. 345-384
[17] Nonlinear Finite Elements for Continua and Structures, John Wiley & Sons, Chichester, England, 2000
[18] Verification and validation of machining simulations for sufficient accuracy, COMPLAS X, CIMNE, Barcelona, Spain (E. Onate; D.R.J. Owen, eds.) (2009), p. 4
[19] Simulation of manufacturing chain of a titanium aerospace component with experimental validation, Finite Elem. Anal. Des., Volume 51 (2012), pp. 10-21
[20] Efficient 3D data transfer operators based on numerical integration, Int. J. Numer. Methods Eng., Volume 102 (2015), pp. 892-929
[21] Ductile shear failure damage modelling and predicting built-up edge in steel machining, J. Mater. Process. Technol., Volume 213 (2013), pp. 1954-1969
[22] Developments in simulating built up edge formation in steel machining, Proc. CIRP, Volume 1 (2012), pp. 78-83
[23] Temperature dependent flow softening of titanium alloy Ti6Al4V: an investigation using finite element simulation of machining, J. Mater. Process. Technol., Volume 211 (2011), pp. 737-749
[24] Serrated chip prediction in finite element modeling of the chip formation process, Mach. Sci. Technol., Volume 11 (2007), pp. 367-390
[25] A new material model for 2D numerical simulation of serrated chip formation when machining titanium alloy Ti-6Al-4V, Int. J. Mach. Tools Manuf., Volume 48 (2008), pp. 275-288
[26] 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., Volume 50 (2010), pp. 943-960
[27] A FEM study on mechanisms of discontinuous chip formation in hard machining, J. Mater. Process. Technol., Volume 155–156 (2004), pp. 1350-1356
[28] Non-local damage models in manufacturing simulations, Eur. J. Mech. A, Solids, Volume 49 (2015), pp. 548-560
[29] On the physics of machining titanium alloys: interactions between cutting parameters, microstructure and tool wear, Metals, Volume 4 (2014), pp. 335-358
[30] Modeling and simulation of burr formation: state-of-the-art and future trends (J. Aurich; D. Dornfield, eds.), Burr — Analysis, Control and Removal, Springer-Verlag, Heidelberg, 2009
[31] Simulation of metal cutting using a physically based plasticity model, Model. Simul. Mater. Sci. Eng., Volume 18 (2010)
[32] 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., Volume 213 (2013), pp. 2217-2237
[33] Simulation of cutting tool wear by a modified pin-on-disc test, Int. J. Mach. Tools Manuf., Volume 29 (1989), pp. 377-390
[34] Sliding wear of hard materials — the importance of a fresh countermaterial surface, Wear, Volume 124 (1988), pp. 195-216
[35] Sliding wear testing of coated cutting tool materials, Tribol. Int., Volume 24 (1991), pp. 143-150
[36] Identification of a friction model at tool/chip/workpiece interfaces in dry machining of AISI4142 treated steels, J. Mater. Process. Technol., Volume 209 (2009), pp. 3978-3990
[37] Identification of a friction model — application to the context of dry cutting of an AISI 316L austenitic stainless steel with a TiN coated carbide tool, Int. J. Mach. Tools Manuf., Volume 48 (2008), pp. 1211-1223
[38] 3D finite element analysis of tool wear in machining, CIRP Ann., Volume 57 (2008), pp. 61-64
[39] Solving an inverse heat conduction problem using a non-integer identified model, Int. J. Heat Mass Transf., Volume 44 (2001), pp. 2671-2680
[40] Tool wear effects on white and dark layer formation in hard turning of AISI 52100 steel, Wear, Volume 286–287 (2012), pp. 98-107
[41] Investigations on mechanisms of tool wear in machining of Ti–6Al–4V using FEM simulation, Proc. CIRP, Volume 8 (2013), pp. 158-163
[42] Modelling of tool wear in cemented-carbide machining alloy 718, Int. J. Mach. Tools Manuf., Volume 48 (2008), pp. 1072-1080
[43] Cutting tool wear in the machining of hardened steels, part I: alumina/TiC cutting tool wear, Wear, Volume 247 (2001), pp. 139-151
[44] Cutting forces and TEM analysis of the generated surface during machining metal matrix composites, J. Mater. Process. Technol., Volume 209 (2009), pp. 2260-2269
[45] A FE based machining simulation methodology accounting for cast iron microstructure, Finite Elem. Anal. Des., Volume 80 (2014), pp. 1-10
[46] Influence of Material Variations on Machinability: Machining Difficult-to-Machine Alloys, Department of Materials and Manufacturing Technology, Chalmers University of Technology, Gothenburg, 2015
[47] A numerical model incorporating the microstructure alteration for predicting residual stresses in hard machining of AISI 52100 steel, CIRP Ann., Volume 59 (2010), pp. 113-116
[48] Further insight into the chip formation of ferritic–pearlitic steels: microstructural evolutions and associated thermo-mechanical loadings, Int. J. Mach. Tools Manuf., Volume 77 (2014), pp. 34-46
[49] Towards a physical FE modelling of a dry cutting operation: influence of dynamic recrystallization when machining AISI 1045, Proc. CIRP, Volume 8 (2013), pp. 516-521
[50] Modeling of surface dynamic recrystallisation during the finish turning of the 15-5PH steel, Proc. CIRP, Volume 8 (2013), pp. 311-315
[51] Role of phase transformation in chip segmentation during high speed machining of dual phase titanium alloys, J. Mater. Process. Technol., Volume 214 (2014), pp. 3048-3066
[52] Constitutive modeling of two phase materials using the mean field method for homogenization, Int. J. Mater. Forming, Volume 4 (2011), pp. 93-102
[53] Effect of constitutive modeling during finite element analysis of machining-induced residual stresses in Ti6Al4V, Proc. CIRP, Volume 13 (2014), pp. 294-301
[54] Finite element simulation of machining Inconel 718 alloy including microstructure changes, Int. J. Mech. Sci., Volume 88 (2014), pp. 110-121
[55] Finite element simulation of the orthogonal machining process with Al 2024 T351 aerospace alloy, Proc. Eng., Volume 64 (2013), pp. 1454-1463
[56] Determination of Johnson–Cook parameters from machining simulations, Comput. Mater. Sci., Volume 52 (2012), pp. 298-304
[57] The influence of Johnson–Cook material constants on finite element simulation of machining of AISI 316L steel, Int. J. Mach. Tools Manuf., Volume 47 (2007), pp. 462-470
[58] A new calibration method for ductile fracture models as chip separation criteria in machining, Simul. Model. Pract. Theory, Volume 18 (2010), pp. 1286-1296
[59] Quantification of the chip segmentation in metal machining: application to machining the aeronautical aluminium alloy AA2024-T351 with cemented carbide tools WC-Co, Int. J. Mach. Tools Manuf., Volume 64 (2013), pp. 102-113
[60] An enhanced constitutive material model for machining of Ti–6Al–4V alloy, J. Mater. Process. Technol., Volume 213 (2013), pp. 2238-2246
[61]
, Luleå University of Technology, Luleå (2010), p. 68[62] Modelling high strain rate phenomena in metal cutting simulation, Model. Simul. Mater. Sci. Eng., Volume 20 (2012)
[63] H. Frost, M. Ashby, Deformation-Mechanism Maps: The Plasticity and Creep of Metals and Ceramics, Web version of corresponding book.
[64] Deformation-mechanism maps for pure iron, two austenitic stainless steels and a low-alloy ferritic steel (R.I. Jaffee; B.A. Wilcox, eds.), Fundamental Aspects of Structural Alloy Design, Plenum Press, 1977, pp. 26-65
[65] A dislocation model for the stress–strain behaviour of polycrystalline [alpha]-Fe with special emphasis on the variation of the densities of mobile and immobile dislocations, Materials Science and Engineering, Volume 5 ( 1969/1970 ), pp. 193-200
[66] The plastic deformation of metals — a dislocation model and its applicability, Rev. Powder Metal. Phys. Ceram., Volume 2/3 (1983), pp. 79-265
[67] Dislocations, vacancies and solute diffusion in physical based plasticity model for AISI 316L, Mech. Mater., Volume 40 (2008), pp. 907-919
[68] The prediction of machined surface hardness using a new physics-based material model, Proc. CIRP, Volume 13 (2014), pp. 249-256
[69] A unified material model including dislocation drag and its application to simulation of orthogonal cutting of OFHC Copper, J. Mater. Process. Technol., Volume 216 (2015), pp. 328-338
[70] A unified internal state variable material model for inelastic deformation and microstructure evolution in SS304, Mater. Sci. Eng. A, Volume 594 (2014), pp. 352-363
[71] Simulation of mechanical cutting using a physical based material model, Int. J. Mater. Forming, Volume 3 (2010), pp. 511-514
[72] Flow stress model for IN718 accounting for evolution of strengthening precipitates during thermal treatment, Comput. Mater. Sci., Volume 82 (2014), pp. 531-539
[73] Dislocation density based model for plastic deformation and globularisation of Ti–6Al–4V, Int. J. Plast., Volume 50 (2013), pp. 94-108
[74] Predictive capability of constitutive model outside the range of calibration, COMPLAS XI, Barcelona, Spain (E. Onate; D.R.J. Owen, eds.) (2011), p. 4
[75] Plastic deformation of high-purity α-titanium: model development and validation using the Taylor cylinder impact test, Mech. Mater. B, Volume 80 (2015), pp. 264-275
[76] Plastic flow behavior of 7017 and 7055 aluminum alloys under different high strain rate test methods, Mater. Sci. Eng. A, Volume 612 (2014), pp. 343-353
[77] Finite element modeling and cutting simulation of Inconel 718, CIRP Ann., Volume 56 (2007), pp. 61-64
[78] Numerical and experimental study of dry cutting for an aeronautic aluminium alloy (A2024-T351), Int. J. Mach. Tools Manuf., Volume 48 (2008), pp. 1187-1197
[79] Adiabatic shear banding in high speed machining of Ti–6Al–4V: experiments and modeling, Int. J. Plast., Volume 18 (2002), pp. 443-459
[80] Combination of two simulation methods for prediction of residual stresses induced in turning operation, Saint-Étienne, France (2014)
[81] On the evaluation of the global heat transfer coefficient in cutting, Int. J. Mach. Tools Manuf., Volume 47 (2007), pp. 1738-1743
Cité par Sources :
Commentaires - Politique