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摘要: 作为高性能复杂金属构件的新兴制造技术，增材制造已被应用于航空航天、汽车工业、医疗和核电等领域。金属增材制造工艺涉及传热、热力、相变及流动等复杂物理现象，不同尺度及跨尺度数值模拟结合实验验证可实现对增材制造过程中复杂物理现象的理解、调控及优化，为高质量、高精度、高性能金属构件的成形提供有力支撑。本文综述了宏观、介观、微观及多尺度下金属增材制造的数值模拟，阐述了温度场、热应力场、粉末粉床、熔池流动及凝固行为的数值仿真方法，展望了增材制造数值模拟方法的发展趋势。Abstract: As one of the new technologies for the high-performance complex metal components, the additive manufacturing technology has been used in the aerospace, automotive industry, medical, and nuclear powder fields. The metal additive manufacturing processes involve the complex physical phenomena such as heat transfer, thermal, phase transition, and flow. Numerical simulation methods in the different scales and cross-scales combined with the experimental verification can realize the understanding, control, and optimization of the complex physical phenomena in the additive manufacturing process, which can provide the strong support for the forming of high-quality, high-precision, and high-performance metal components. The numerical simulation of metal additive manufacturing processes in the macro, mesoscopic, micro, and multi-scale was reviewed in this article. The numerical simulation methods of temperature field, thermal stress field, powder bed, molten pool flow, and solidification behavior process were described. Finally, the development trend of the numerical simulation methods for the additive manufacturing was prospected.
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图 1 热源模型示意图[26]：（a）圆柱形；（b）半球形；（c）半椭球形；（d）锥形；（e）辐射传递法；（f）射线追踪法；（g）线性衰减法；（h）辐射衰减法
Figure 1. Schematic of the heat source models[26]: (a) cylindrical shape; (b) semi-spherical shape; (c) semi-ellipsoidal shape; (d) conical shape; (e) radiation transfer method; (f) ray-tracing method; (g) linearly decaying method; (h) exponentially decaying method
图 5 随机粉床带旋转的随机包装降雨模型（a），降雨模型产生的粉床（b），去除部分颗粒并调整相对密度（c），真实粉末床（钛合金）截面（d）[49]
Figure 5. Schematic of the rain model for random packing with rotations(a), powder bed produced by the rain model(b), adjusting the relative density by removing some of the particles(c), cross section of a real powder bed (titanium alloy) (d)[49]
图 8 具有多种物理场的模拟熔池[68]：（a）Marangoni效应、电子反压力和蒸汽反冲压力；（b）没有电子反压力；（c）没有蒸汽反冲压力；（d）没有Marangoni对流
Figure 8. Simulated molten pool with the multiple physics[68]: (a) Marangoni effect, electron counter pressure, and vapor recoil pressure; (b) without electron counter pressure; (c) without vapor recoil pressure; (d) without Marangoni convection
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[1] Martin J H, Yahata B D, Hundley J M, et al. 3D printing of high-strength aluminium alloys. Nature, 2017, 549(7672): 365 doi: 10.1038/nature23894 [2] Hou Z, Tian X, Zhang J, et al. Design and 3D printing of continuous fiber reinforced heterogeneous composites. Compos Struct, 2020, 237: 111945 doi: 10.1016/j.compstruct.2020.111945 [3] Lin X, Huang W D. Laser additive manufacturing for high-performance metal components. Sci Sin Inform, 2015, 45(9): 1111 doi: 10.1360/N112014-00245林鑫, 黄卫东. 高性能金属构件的激光增材制造. 中国科学:信息科学, 2015, 45(9): 1111 doi: 10.1360/N112014-00245 [4] Huang W D. Advances in the research of material 3D printing technology. J New Ind, 2016, 6(3): 53黄卫东. 材料3D打印技术的研究进展. 新型工业化, 2016, 6(3): 53 [5] Wiese M, Thiede S, Herrmann C. Rapid manufacturing of automotive polymer series parts: A systematic review of processes, materials and challenges. Addit Manuf, 2020, 36: 101582 [6] Juechter V, Franke M M, Merenda T, et al. Additive manufacturing of Ti‒45Al‒4Nb‒C by selective electron beam melting for automotive applications. Addit Manuf, 2018, 22: 118 [7] Haleem A, Javaid M, Khan R H, et al. 3D printing applications in bone tissue engineering. J Clin Orthop Traum, 2020, 11(suppl 1): S118 [8] Cakmak A M, Unal S, Sahin A, et al. 3D printed polycaprolactone/gelatin/bacterial cellulose/hydroxyapatite composite scaffold for bone tissue engineering. Polymers, 2020, 12(9): 1962 doi: 10.3390/polym12091962 [9] Ma Q Y, Du P M, Peng Y B et al. Application status and development trend of metal additive manufacturing technology in the field of nuclear materials. Powder Metall Technol, 2022, 40(1): 86马青原, 杜沛南, 彭英博, 等. 金属增材制造技术在核材料领域的应用现状与发展趋势. 粉末冶金技术, 2022, 40(1): 86 [10] Charbonnier B, Hadida M, Marchat D. Additive manufacturing pertaining to bone: Hopes, reality and future challenges for clinical applications. Acta Biomater, 2021, 121: 1 doi: 10.1016/j.actbio.2020.11.039 [11] Lowther M, Louth S, Davey A, et al. Clinical, industrial, and research perspectives on powder bed fusion additively manufactured metal implants. Addit Manuf, 2019, 28: 565 [12] Wu H S, Zhang Y, Dai D. 3D the current situation and prospects of nuclear power printing. China Nucl Ind, 2017(7): 45 doi: 10.3969/j.issn.1007-8282.2017.07.014伍浩松, 张焰, 戴定. 3D核电打印现状及前景. 中国核工业, 2017(7): 45 doi: 10.3969/j.issn.1007-8282.2017.07.014 [13] Zinovieva O, Zinoviev A, Romanova V, et al. Three-dimensional analysis of grain structure and texture of additively manufactured 316L austenitic stainless steel. Addit Manuf, 2020, 36: 101521 [14] Almangour B, Grzesiak D, Cheng J, et al. Thermal behavior of the molten pool, microstructural evolution, and tribological performance during selective laser melting of TiC/316L stainless steel nanocomposites: Experimental and simulation methods. J Mater Process Technol, 2018, 257: 288 doi: 10.1016/j.jmatprotec.2018.01.028 [15] Sanaei N, Fatemi A. Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review. Prog Mater Sci, 2021, 117: 100724 doi: 10.1016/j.pmatsci.2020.100724 [16] Gh Ghanbari P, Mazza E, Hosseini E. Adaptive local-global multiscale approach for thermal simulation of the selective laser melting process. Addit Manuf, 2020, 36: 101518 [17] Zhang L, Basantes-Defaz A C, Ozevin D, et al. Real-time monitoring of welding process using air-coupled ultrasonics and acoustic emission. Int J Adv Manuf Technol, 2019, 101(5): 1623 [18] Kledwig C, Perfahl H, Reisacher M, et al. Analysis of melt pool characteristics and process parameters using a coaxial monitoring system during directed energy deposition in additive manufacturing. Materials, 2019, 12(2): 308 doi: 10.3390/ma12020308 [19] Guo Q, Zhao C, Escano L I, et al. Transient dynamics of powder spattering in laser powder bed fusion additive manufacturing process revealed by in-situ high-speed high-energy X-ray imaging. Acta Mater, 2018, 151: 169 doi: 10.1016/j.actamat.2018.03.036 [20] Yan W, Ge W, Smith J, et al. Multi-scale modeling of electron beam melting of functionally graded materials. Acta Mater, 2016, 115: 403 doi: 10.1016/j.actamat.2016.06.022 [21] Yang Y, Ayas C. Point, surface and volumetric heat sources in the thermal modelling of selective laser melting // AIP Conference Proceedings. Dublin, 2017: 04006 [22] Mollamahmutoglu M, Yilmaz O. Volumetric heat source model for laser-based powder bed fusion process in additive manufacturing. Therm Sci Eng Prog, 2021, 25: 101021 doi: 10.1016/j.tsep.2021.101021 [23] Roberts I A, Wang C J, Esterlein R, et al. A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing. Int J Mach Tools Manuf, 2009, 49(12): 916 [24] Gusarov A V, Yadroitsev I, Bertrand P, et al. Model of radiation and heat transfer in laser-powder interaction zone at selective laser melting. J Heat Transfer, 2009, 131(7): 072101 doi: 10.1115/1.3109245 [25] Liu S, Zhu H, Peng G, et al. Microstructure prediction of selective laser melting AlSi10Mg using fi nite element analysis. Mater Des, 2018, 142: 319 doi: 10.1016/j.matdes.2018.01.022 [26] Zhang Z, Huang Y, Rani Kasinathan A, et al. 3-Dimensional heat transfer modeling for laser powder-bed fusion additive manufacturing with volumetric heat sources based on varied thermal conductivity and absorptivity. Opt Laser Technol, 2019, 109: 297 doi: 10.1016/j.optlastec.2018.08.012 [27] Loh L E, Chua C K, Yeong W Y, et al. Numerical investigation and an effective modelling on the selective laser melting (SLM) process with aluminium alloy 6061. Int J Heat Mass Transfer, 2015, 80: 288 [28] Li Y, Gu D. Parametric analysis of thermal behavior during selective laser melting additive manufacturing of aluminum alloy powder. Mater Des, 2014, 63: 856 doi: 10.1016/j.matdes.2014.07.006 [29] Ma L, Bin H. Temperature and stress analysis and simulation in fractal scanning-based laser sintering. Int J Adv Manuf Technol, 2007, 34(9): 898 [30] Tang Q, Chen P, Chen J, et al. Numerical simulation of selective laser melting temperature conduction behavior of H13 steel in different models. Optik, 2020, 201: 163336 doi: 10.1016/j.ijleo.2019.163336 [31] Zhang D Q, Cai Q Z, Liu J H, et al. Select laser melting of W‒Ni‒Fe powders: Simulation and experimental study. Int J Adv Manuf Technol, 2010, 51(5): 649 [32] Mercelis P, Kruth J P. Residual stresses in selective laser sintering and selective laser melting. Rapid Prototyp J, 2006, 12(5): 254 doi: 10.1108/13552540610707013 [33] Cheng B, Shrestha S, Chou K. Stress and deformation evaluations of scanning strategy effect in selective laser melting. Addit Manuf, 2016, 12: 240 [34] Kruth J P, Froyen L, Van Vaerenbergh J, et al. Selective laser melting of iron-based powder. J Mater Process Technol, 2004, 149(1): 616 [35] Parry L, Ashcroft I A, Wildman R D. Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation. Addit Manuf, 2016, 12: 1 [36] Liu Y, Yang Y, Wang D. A study on the residual stress during selective laser melting (SLM) of metallic powder. Int J Adv Manuf Technol, 2016, 87(1): 647 [37] Dunbar A J, Denlinger E R, Heigel J, et al. Development of experimental method for in situ distortion and temperature measurements during the laser powder bed fusion additive manufacturing process. Addit Manuf, 2016, 12: 25 [38] Vastola G, Zhang G, Pei Q X, et al. Controlling of residual stress in additive manufacturing of Ti6Al4V by finite element modeling. Addit Manuf, 2016, 12: 231 [39] Farahmand P, Kovacevic R. An experimental-numerical investigation of heat distribution and stress field in single- and multi-track laser cladding by a high-power direct diode laser. Opt Laser Technol, 2014, 63: 154 doi: 10.1016/j.optlastec.2014.04.016 [40] Li C, Fu C H, Guo Y B, et al. A multiscale modeling approach for fast prediction of part distortion in selective laser melting. J Mater Process Technol, 2015, 229: 703 [41] Peng H, Ghasri-Khouzani M, Gong S, et al. Fast prediction of thermal distortion in metal powder bed fusion additive manufacturing: Part 1, a thermal circuit network model. Addit Manuf, 2018, 22: 852 [42] Li L, Anand S. Fast additive manufacturing distortion prediction using backward interpolation. Additi Manuf, 2021, 41: 101955 doi: 10.1016/j.addma.2021.101955 [43] Gu D, Shi X, Poprawe R, et al. Material-structure-performance integrated laser-metal additive manufacturing. Science, 2021, 372(6545): 1487 doi: 10.1126/science.abg1487 [44] Nan W, Pasha M, Ghadiri M. Numerical simulation of particle flow and segregation during roller spreading process in additive manufacturing. Powder Technol, 2020, 364: 811 doi: 10.1016/j.powtec.2019.12.023 [45] Yao D, An X, Fu H, et al. Dynamic investigation on the powder spreading during selective laser melting additive manufacturing. Addit Manuf, 2021, 37: 101707 [46] Chen H, Wei Q, Zhang Y, et al. Powder-spreading mechanisms in powder-bed-based additive manufacturing: Experiments and computational modeling. Acta Mater, 2019, 179: 158 doi: 10.1016/j.actamat.2019.08.030 [47] Zhang J, Tan Y, Xiao X, et al. Comparison of roller-spreading and blade-spreading processes in powder-bed additive manufacturing by DEM simulations. Particuology, 2022, 66: 48 doi: 10.1016/j.partic.2021.07.005 [48] Markl M, Körner C. Multiscale modeling of powder bed-based additive manufacturing. Annu Rev Mater Res, 2016, 46: 93 doi: 10.1146/annurev-matsci-070115-032158 [49] Körner C, Attar E, Heinl P. Mesoscopic simulation of selective beam melting processes. J Mater Process Technol, 2011, 211(6): 978 doi: 10.1016/j.jmatprotec.2010.12.016 [50] Papadakis L, Loizou A, Risse J, et al. Numerical computation of component shape distortion manufactured by selective laser melting. Procedia CIRP, 2014, 18: 90 doi: 10.1016/j.procir.2014.06.113 [51] Parteli E J R, Schmidt J, Blümel C, et al. Attractive particle interaction forces and packing density of fine glass powders. Sci Rep, 2014, 4: 6227 [52] Ammer R, Markl M, Ljungblad U, et al. Simulating fast electron beam melting with a parallel thermal free surface lattice Boltzmann method. Comput Math Appl, 2014, 67(2): 318 doi: 10.1016/j.camwa.2013.10.001 [53] Wang Z, Yan W, Liu W K, et al. Powder-scale multi-physics modeling of multi-layer multi-track selective laser melting with sharp interface capturing method. Comput Mech, 2019, 63(4): 649 doi: 10.1007/s00466-018-1614-5 [54] Gu D, Xia M, Dai D. On the role of powder flow behavior in fluid thermodynamics and laser processability of Ni-based composites by selective laser melting. Int J Mach Tools Manuf, 2019, 137: 67 doi: 10.1016/j.ijmachtools.2018.10.006 [55] Scharowsky T, Osmanlic F, Singer R F, et al. Melt pool dynamics during selective electron beam melting. Appl Phys A, 2014, 114(4): 1303 doi: 10.1007/s00339-013-7944-4 [56] Tolochko N K, Mozzharov S E, Yadroitsev I A, et al. Balling processes during selective laser treatment of powders. Rapid Prototyp J, 2004, 10(2): 78 doi: 10.1108/13552540410526953 [57] Yadroitsev I, Gusarov A, Yadroitsava I, et al. Single track formation in selective laser melting of metal powders. J Mater Process Technol, 2010, 210(12): 1624 doi: 10.1016/j.jmatprotec.2010.05.010 [58] Dilip J J S, Zhang S, Teng C, et al. Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti‒6Al‒4V alloy parts fabricated by selective laser melting. Prog Addit Manuf, 2017, 2(3): 157 doi: 10.1007/s40964-017-0030-2 [59] Geiger M, Leitz K H, Koch H, et al. A 3D transient model of keyhole and melt pool dynamics in laser beam welding applied to the joining of zinc coated sheets. Prod Eng, 2009, 3(2): 127 doi: 10.1007/s11740-008-0148-7 [60] Das S. Physical aspects of process control in selective laser sintering of metals. Adv Eng Mater, 2003, 5(10): 701 doi: 10.1002/adem.200310099 [61] Shuja S Z, Yilbas B S. Laser produced melt pool: Influence of laser intensity parameter on flow field in melt pool. Opt Laser Technol, 2011, 43(4): 767 doi: 10.1016/j.optlastec.2010.12.003 [62] Liu S, Liu J, Chen J, et al. Influence of surface tension on the molten pool morphology in laser melting. Int J Therm Sci, 2019, 146: 106075 doi: 10.1016/j.ijthermalsci.2019.106075 [63] Tseng C C, Li C J. Numerical investigation of interfacial dynamics for the melt pool of Ti‒6Al‒4V powders under a selective laser. Int J Heat Mass Transfer, 2019, 134: 906 doi: 10.1016/j.ijheatmasstransfer.2019.01.030 [64] Lee K H, Yun G J. A novel heat source model for analysis of melt pool evolution in selective laser melting process. Addit Manuf, 2020, 36: 101497 [65] Li E L, Wang L, Yu A B B, et al. A three-phase model for simulation of heat transfer and melt pool behaviour in laser powder bed fusion process. Powder Technol, 2021, 381: 298 doi: 10.1016/j.powtec.2020.11.061 [66] Shi R, Khairallah S A, Roehling T T, et al. Microstructural control in metal laser powder bed fusion additive manufacturing using laser beam shaping strategy. Acta Mater, 2020, 184: 284 doi: 10.1016/j.actamat.2019.11.053 [67] Zhang D, Zhang P, Liu Z, et al. Thermofluid field of molten pool and its effects during selective laser melting (SLM) of Inconel 718 alloy. Addit Manuf, 2018, 21(100): 567 [68] Zhao Y, Koizumi Y, Aoyagi K, et al. Molten pool behavior and effect of fluid flow on solidification conditions in selective electron beam melting (SEBM) of a biomedical Co‒Cr‒Mo alloy. Addit Manuf, 2019, 26: 202 [69] Li J, Zhou X, Brochu M, et al. Solidification microstructure simulation of Ti‒6Al‒4V in metal additive manufacturing: A review. Addit Manuf, 2020, 31: 1100989 [70] Körner C, Markl M, Koepf J A. Modeling and simulation of microstructure evolution for additive manufacturing of metals: A critical review. Metall Mater Trans A, 2020, 51: 4970 doi: 10.1007/s11661-020-05946-3 [71] Zhang T, Lu S H, Wu Y X, et al. Optimization of deformation parameters of dynamic recrystallization for 7055 aluminum alloy by cellular automaton. Trans Nonferrous Met Soc China, 2017, 27(6): 1327 doi: 10.1016/S1003-6326(17)60154-7 [72] Zinoviev A, Zinovieva O, Ploshikhin V, et al. Evolution of grain structure during laser additive manufacturing. Simulation by a cellular automata method. Mater Des, 2016, 106: 321 [73] Zhang Y, Zhang J. Modeling of solidification microstructure evolution in laser powder bed fusion fabricated 316L stainless steel using combined computational fluid dynamics and cellular automata. Addit Manuf, 2019, 28: 750 [74] Ao X, Xia H, Liu J, et al. Simulations of microstructure coupling with moving molten pool by selective laser melting using a cellular automaton. Mater Des, 2020, 185: 108230 doi: 10.1016/j.matdes.2019.108230 [75] Rätz A, Voigt A. PDE's on surfaces — a diffuse interface approach. Commun Math Sci, 2006, 4(3): 575 doi: 10.4310/CMS.2006.v4.n3.a5 [76] Zhang J, Wu L, Zhang Y, et al. Phase field simulation of dendritic microstructure in additively manufactured titanium alloy. Met Powder Rep, 2019, 74(1): 20 doi: 10.1016/j.mprp.2018.11.001 [77] Chu S, Guo C, Zhang T, et al. Phase-field simulation of microstructure evolution in electron beam additive manufacturing. Eur Phys J E, 2020, 43(6): 35 doi: 10.1140/epje/i2020-11952-1 [78] Sahoo S, Chou K. Phase-field simulation of microstructure evolution of Ti‒6Al‒4V in electron beam additive manufacturing process. Addit Manuf, 2016, 9: 14 [79] Karayagiz K, Johnson L, Seede R, et al. Finite interface dissipation phase field modeling of Ni–Nb under additive manufacturing conditions. Acta Mater, 2020, 185: 320 doi: 10.1016/j.actamat.2019.11.057 [80] Liu P W, Ji Y Z, Wang Z, et al. Investigation on evolution mechanisms of site-specific grain structures during metal additive manufacturing. J Mater Process Technol, 2018, 257(2010): 191 [81] Acharya R, Sharon J A, Staroselsky A. Prediction of microstructure in laser powder bed fusion process. Acta Mater, 2017, 124: 360 doi: 10.1016/j.actamat.2016.11.018 [82] Yang C, Xu Q, Liu B. GPU-accelerated three-dimensional phase-field simulation of dendrite growth in a nickel-based superalloy. Comput Mater Sci, 2017, 136: 133 doi: 10.1016/j.commatsci.2017.04.031 [83] Rodgers T M, Moser D, Abdeljawad F, et al. Simulation of powder bed metal additive manufacturing microstructures with coupled finite difference-Monte Carlo method. Addit Manuf, 2021, 41: 101953 [84] Sunny S, Yu H, Mathews R, et al. Improved grain structure prediction in metal additive manufacturing using a dynamic kinetic Monte Carlo framework. Addit Manuf, 2021, 37: 101649 [85] Ren G W. Multiscale Simulation of Material [Dissertation]. Shanghai: Fudan University, 2010任国武. 材料的多尺度模拟[学位论文]. 上海: 复旦大学, 2010 [86] Zhang X X, Andrä H. Crystal plasticity simulation of the macroscale and microscale stress–strain relations of additively manufactured AlSi10Mg alloy. Comput Mater Sci, 2021, 200: 110832 doi: 10.1016/j.commatsci.2021.110832 [87] Zhang X X, Lutz A, Andrä H, et al. Evolution of microscopic strains, stresses, and dislocation density during in-situ tensile loading of additively manufactured AlSi10Mg alloy. Int J Plast, 2021, 139: 102946 doi: 10.1016/j.ijplas.2021.102946 [88] Watanabe I, Sun Z, Kitano H, et al. Multiscale analysis of mechanical behavior of multilayer steel structures fabricated by wire and arc additive manufacturing. Sci Technol Adv Mater, 2020, 21(1): 461 doi: 10.1080/14686996.2020.1788908 [89] Gu D, Ma C, Xia M, et al. A multiscale understanding of the thermodynamic and kinetic mechanisms of laser additive manufacturing. Engineering, 2017, 3(5): 675 doi: 10.1016/J.ENG.2017.05.011 -
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