Current Situation and Prospect of Computationally Assisted Design in High-Performance Additive Manufactured Aluminum Alloys: A Review
Received date: 2022-08-31
Revised date: 2022-10-10
Online published: 2022-10-24
Supported by
National Key Research and Development Program of China(2019YFB2006500);National Natural Science Foundation of China(51922044);Key Research and Development Program of Guangxi(AB21220028);Natural Science Foundation of Hunan Province(2021JJ10062);China Post-doctoral Science Foundation(2021M701293)
Additive manufacturing technology has greatly increased opportunities in the production of high-strength aluminum alloy complex parts. However, current additive manufactured aluminum alloy systems are still limited to castable and weldable Al-Si alloys. This impedes the development of high-performance additive manufactured aluminum alloys. Recently, various computational techniques at different scales have been gradually used to promote the development of high-performance additive manufactured aluminum alloys. This paper summarizes the research achievements in the field of computationally-assisted design of additive manufactured aluminum alloys and their preparation from domestic and foreign scholars and presents representative cases from atomic, mesoscopic, and macroscopic scales and machine learning. The different calculation methods used to assist alloy designs are analyzed and their shortcomings are presented. Finally, the prospect on how to improve the application of multi-scale computation techniques in the development of high-performance additive manufactured aluminum alloys is presented, and some specific development directions are also clarified.
Jianbao GAO , Zhicheng LI , Jia LIU , Jinliang ZHANG , Bo SONG , Lijun ZHANG . Current Situation and Prospect of Computationally Assisted Design in High-Performance Additive Manufactured Aluminum Alloys: A Review[J]. Acta Metall Sin, 2023 , 59(1) : 87 -105 . DOI: 10.11900/0412.1961.2022.00430
| 1 | Li Q, Wang F D, Wang G Q, et al. Wire and arc additive manufacturing of lightweight metal components in aeronautics and astronautics [J]. Aeronaut. Manuf. Technol., 2018, 61(3): 74 |
| 1 | 李 权, 王福德, 王国庆 等. 航空航天轻质金属材料电弧熔丝增材制造技术 [J]. 航空制造技术, 2018, 61(3): 74 |
| 2 | Qin Y L, Sun B H, Zhang H, et al. Development of selective laser melted aluminum alloys and aluminum matrix composites in aerospace field [J]. Chin. J. Lasers, 2021, 48: 1402002 |
| 2 | 秦艳利, 孙博慧, 张 昊 等. 选区激光熔化铝合金及其复合材料在航空航天领域的研究进展 [J]. 中国激光, 2021, 48: 1402002 |
| 3 | Zhu H H, Liao H L. Research status of selective laser melting of high strength aluminum alloy [J]. Laser Optoelectron. Prog., 2018, 55: 011402 |
| 3 | 朱海红, 廖海龙. 高强铝合金的激光选区熔化成形研究现状 [J]. 激光与光电子学进展, 2018, 55: 011402 |
| 4 | Zhang J L, Song B, Yang L, et al. Microstructure evolution and mechanical properties of TiB/Ti6Al4V gradient-material lattice structure fabricated by laser powder bed fusion [J]. Composites, 2020, 202B: 108417 |
| 5 | Zhang J L, Song B, Wei Q S, et al. A review of selective laser melting of aluminum alloys: Processing, microstructure, property and developing trends [J]. J. Mater. Sci. Technol., 2019, 35: 270 |
| 6 | Kotadia H R, Gibbons G, Das A, et al. A review of laser powder bed fusion additive manufacturing of aluminium alloys: Microstructure and properties [J]. Addit. Manuf., 2021, 46: 102155 |
| 7 | Kimura T, Nakamoto T. Microstructures and mechanical properties of A356 (AlSi7Mg0.3) aluminum alloy fabricated by selective laser melting [J]. Mater. Des., 2016, 89: 1294 |
| 8 | Wang M, Song B, Wei Q S, et al. Improved mechanical properties of AlSi7Mg/nano-SiCp composites fabricated by selective laser melting [J]. J. Alloys Compd., 2019, 810: 151926 |
| 9 | Yan Q, Song B, Shi Y S. Comparative study of performance comparison of AlSi10Mg alloy prepared by selective laser melting and casting [J]. J. Mater. Sci. Technol., 2020, 41: 199 |
| 10 | Van Cauwenbergh P, Samaee V, Thijs L, et al. Unravelling the multi-scale structure-property relationship of laser powder bed fusion processed and heat-treated AlSi10Mg [J]. Sci. Rep., 2021, 11: 6423 |
| 11 | Li X P, Wang X J, Saunders M, et al. A selective laser melting and solution heat treatment refined Al-12Si alloy with a controllable ultrafine eutectic microstructure and 25% tensile ductility [J]. Acta Mater., 2015, 95: 74 |
| 12 | Suryawanshi J, Prashanth K G, Scudino S, et al. Simultaneous enhancements of strength and toughness in an Al-12Si alloy synthesized using selective laser melting [J]. Acta Mater., 2016, 115: 285 |
| 13 | Starke Jr E A, Staley J T. Application of modern aluminum alloys to aircraft [J]. Prog. Aerosp. Sci., 1996, 32: 131 |
| 14 | Roberts C E, Bourell D, Watt T, et al. A novel processing approach for additive manufacturing of commercial aluminum alloys [J]. Phys. Procedia, 2016, 83: 909 |
| 15 | Del Guercio G, McCartney D G, Aboulkhair N T, et al. Cracking behaviour of high-strength AA2024 aluminium alloy produced by laser powder bed fusion [J]. Addit. Manuf., 2022, 54: 102776 |
| 16 | Panwisawas C, Tang Y T, Reed R C. Metal 3D printing as a disruptive technology for superalloys [J]. Nat. Commun., 2020, 11: 2327 |
| 17 | Yamasaki S, Okuhira T, Mitsuhara M, et al. Effect of Fe addition on heat-resistant aluminum alloys produced by selective laser melting [J]. Metals, 2019, 9: 468 |
| 18 | Nalivaiko A Y, Arnautov A N, Zmanovsky S V, et al. Al-Si-Cu and Al-Si-Cu-Ni alloys for additive manufacturing: Composition, morphology and physical characteristics of powders [J]. Mater. Res. Express, 2019, 6: 086536 |
| 19 | Zhang B, Wei W, Shi W, et al. Effect of heat treatment on the microstructure and mechanical properties of Er-containing Al-7Si-0.6Mg alloy by laser powder bed fusion [J]. J. Mater. Res. Technol., 2022, 18: 3073 |
| 20 | Tan Q Y, Zhang J Q, Sun Q, et al. Inoculation treatment of an additively manufactured 2024 aluminium alloy with titanium nanoparticles [J]. Acta Mater., 2020, 196: 1 |
| 21 | Zhang J L, Gao J B, Song B, et al. A novel crack-free Ti-modified Al-Cu-Mg alloy designed for selective laser melting [J]. Addit. Manuf., 2021, 38: 101829 |
| 22 | Martin J H, Yahata B D, Hundley J M, et al. 3D printing of high-strength aluminium alloys [J]. Nature, 2017, 549: 365 |
| 23 | Nie X J, Zhang H, Zhu H H, et al. Effect of Zr content on formability, microstructure and mechanical properties of selective laser melted Zr modified Al-4.24Cu-1.97Mg-0.56Mn alloys [J]. J. Alloys Compd., 2018, 764: 977 |
| 24 | Li G C, Brodu E, Soete J, et al. Exploiting the rapid solidification potential of laser powder bed fusion in high strength and crack-free Al-Cu-Mg-Mn-Zr alloys [J]. Addit. Manuf., 2021, 47: 102210 |
| 25 | Li R D, Wang M B, Li Z M, et al. Developing a high-strength Al-Mg-Si-Sc-Zr alloy for selective laser melting: Crack-inhibiting and multiple strengthening mechanisms [J]. Acta Mater., 2020, 193: 83 |
| 26 | Minasyan T, Hussainova I. Laser powder-bed fusion of ceramic particulate reinforced aluminum alloys: A review [J]. Materials, 2022, 15: 2467 |
| 27 | Zhou S Y, Su Y, Wang H, et al. Selective laser melting additive manufacturing of 7xxx series Al-Zn-Mg-Cu alloy: Cracking elimination by co-incorporation of Si and TiB2 [J]. Addit. Manuf., 2020, 36: 101458 |
| 28 | Plotkowski A, Rios O, Sridharan N, et al. Evaluation of an Al-Ce alloy for laser additive manufacturing [J]. Acta Mater., 2017, 126: 507 |
| 29 | Lu Z, Zhang L J. Thermodynamic description of the quaternary Al-Si-Mg-Sc system and its application to the design of novel Sc-additional A356 alloys [J]. Mater. Des., 2017, 116: 427 |
| 30 | Yi W, Liu G C, Lu Z, et al. Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning [J]. J. Mater. Sci. Technol., 2022, 112: 277 |
| 31 | Wei M, Tang Y, Zhang L J, et al. Phase-field simulation of microstructure evolution in industrial A2214 alloy during solidification [J]. Metall. Mater. Trans., 2015, 46A: 3182 |
| 32 | Zhang J L, Yuan W H, Song B, et al. Towards understanding metallurgical defect formation of selective laser melted wrought aluminum alloys [J]. Adv. Powder Mater., 2022, 1: 100035 |
| 33 | Mondal B, Mukherjee T, DebRoy T. Crack free metal printing using physics informed machine learning [J]. Acta Mater., 2022, 226: 117612 |
| 34 | Park S, Kayani S H, Euh K, et al. High strength aluminum alloys design via explainable artificial intelligence [J]. J. Alloys Compd., 2022, 903: 163828 |
| 35 | Van de Walle C G, Neugebauer J. First-principles calculations for defects and impurities: Applications to III-nitrides [J]. J. Appl. Phys., 2004, 95: 3851 |
| 36 | Uesugi T, Higashi K. First-principles studies on lattice constants and local lattice distortions in solid solution aluminum alloys [J]. Comput. Mater. Sci., 2013, 67: 1 |
| 37 | Michi R A, Plotkowski A, Shyam A, et al. Towards high-temperature applications of aluminium alloys enabled by additive manufacturing [J]. Int. Mater. Rev., 2022, 67: 298 |
| 38 | Andersen H C. Molecular dynamics simulations at constant pressure and/or temperature [J]. J. Chem. Phys., 1980, 72: 2384 |
| 39 | Nandy J, Sahoo S, Yedla N, et al. Molecular dynamics simulation of coalescence kinetics and neck growth in laser additive manufacturing of aluminum alloy nanoparticles [J]. J. Mol. Model., 2020, 26: 125 |
| 40 | Mahata A, Zaeem M A, Baskes M I. Understanding homogeneous nucleation in solidification of aluminum by molecular dynamics simulations [J]. Modell. Simul. Mater. Sci. Eng., 2018, 26: 025007 |
| 41 | Kurian S, Mirzaeifar R. Selective laser melting of aluminum nano-powder particles, a molecular dynamics study [J]. Addit. Manuf., 2020, 35: 101272 |
| 42 | Zeng Q, Wang L J, Jiang W G. Molecular dynamics simulations of the tensile mechanical responses of selective laser-melted aluminum with different crystalline forms [J]. Crystals, 2021, 11: 1388 |
| 43 | Chen H L, Chen Q, Engstr?m A. Development and applications of the TCAL aluminum alloy database [J]. Calphad, 2018, 62: 154 |
| 44 | Kaufman L, Bernstein H. Computer Calculation of Phase Diagrams[M]. New York: Academic Press Inc., 1970: 1 |
| 45 | ?gren J. Numerical treatment of diffusional reactions in multicomponent alloys [J]. J. Phys. Chem. Solids, 1982, 43: 385 |
| 46 | Zhang L J, Du Y, Steinbach I, et al. Diffusivities of an Al-Fe-Ni melt and their effects on the microstructure during solidification [J]. Acta Mater., 2010, 58: 3664 |
| 47 | Zhong J, Chen L, Zhang L J. Automation of diffusion database development in multicomponent alloys from large number of experimental composition profiles [J]. npj Comput. Mater., 2021, 7: 35 |
| 48 | Hallstedt B, Dupin N, Hillert M, et al. Thermodynamic models for crystalline phases. Composition dependent models for volume, bulk modulus and thermal expansion [J]. Calphad, 2007, 31: 28 |
| 49 | Zhang C, Du Y, Liu S H, et al. Thermal conductivity of Al-Cu-Mg-Si alloys: Experimental measurement and CALPHAD modeling [J]. Thermochim. Acta, 2016, 635: 8 |
| 50 | Zhang F, Du Y, Liu S H, et al. Modeling of the viscosity in the Al-Cu-Mg-Si system: Database construction [J]. Calphad, 2015, 49: 79 |
| 51 | Shang Y J, Yang S L, Zhang L J. Computational modeling of Young's modulus in polycrystal two-phase alloys: Application in γ + γ' Ni-Al alloys [J]. Materialia, 2019, 8: 100500 |
| 52 | Yang S L, Zhong J, Wang J, et al. A novel computational model for isotropic interfacial energies in multicomponent alloys and its coupling with phase-field model with finite interface dissipation [J]. J. Mater. Sci. Technol., 2023, 133: 111 |
| 53 | Yi W, Liu G C, Gao J B, et al. Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques [J]. J. Mater. Inf., 2021, 1: 11 |
| 54 | Dreano A, Favre J, Desrayaud C, et al. Computational design of a crack-free aluminum alloy for additive manufacturing [J]. Addit. Manuf., 2022, 55: 102876 |
| 55 | Kou S. A criterion for cracking during solidification [J]. Acta Mater., 2015, 88: 366 |
| 56 | Maxwell I, Hellawell A. A simple model for grain refinement during solidification [J]. Acta Metall., 1975, 23: 229 |
| 57 | Easton M A, StJohn D H. A model of grain refinement incorporating alloy constitution and potency of heterogeneous nucleant particles [J]. Acta Mater., 2001, 49: 1867 |
| 58 | Li G C, Jadhav S D, Martín A, et al. Investigation of solidification and precipitation behavior of Si-modified 7075 aluminum alloy fabricated by laser-based powder bed fusion [J]. Metall. Mater. Trans., 2021, 52A: 194 |
| 59 | Sha J W, Li M X, Yang L Z, et al. Si-assisted solidification path and microstructure control of 7075 aluminum alloy with improved mechanical properties by selective laser melting [J]. Acta Metall. Sin. (Engl. Lett.), 2022, 35: 1424 |
| 60 | Santillana B, Boom R, Eskin D, et al. High-temperature mechanical behavior and fracture analysis of a low-carbon steel related to cracking [J]. Metall. Mater. Trans., 2012, 43A: 5048 |
| 61 | Scheil E. Bemerkungen zur schichtkristallbildung [J]. Int. J. Mater. Res., 1942, 34: 70 |
| 62 | Dowd J D. Weld cracking of aluminum alloys [J]. Weld. J., 1952, 31: 448-s |
| 63 | Dudas J H, Collins F R. Preventing weld cracks in high-strength aluminum alloys [J]. Weld. J., 1966, 45: 241-s |
| 64 | Liu J W, Kou S. Susceptibility of ternary aluminum alloys to cracking during solidification [J]. Acta Mater., 2017, 125: 513 |
| 65 | Soysal T, Kou S. A simple test for assessing solidification cracking susceptibility and checking validity of susceptibility prediction [J]. Acta Mater., 2018, 143: 181 |
| 66 | Liu J W, Kou S. Crack susceptibility of binary aluminum alloys during solidification [J]. Acta Mater., 2016, 110: 84 |
| 67 | Kou S. Predicting susceptibility to solidification cracking and liquation cracking by CALPHAD [J]. Metals, 2021, 11: 1442 |
| 68 | Tang Z, Vollertsen F. Influence of grain refinement on hot cracking in laser welding of aluminum [J]. Weld. World, 2014, 58: 355 |
| 69 | Greer A L, Bunn A M, Tronche A, et al. Modelling of inoculation of metallic melts: Application to grain refinement of aluminium by Al-Ti-B [J]. Acta Mater., 2000, 48: 2823 |
| 70 | StJohn D H, Qian M, Easton M A, et al. The interdependence theory: The relationship between grain formation and nucleant selection [J]. Acta Mater., 2011, 59: 4907 |
| 71 | G?umann M, Trivedi R, Kurz W. Nucleation ahead of the advancing interface in directional solidification [J]. Mater. Sci. Eng., 1997, A226-228: 763 |
| 72 | Chai G, B?ackerud L, Arnberg L. Relation between grain size and coherency parameters in aluminium alloys [J]. Mater. Sci. Technol., 1995, 11: 1099 |
| 73 | Quested T E, Dinsdale A T, Greer A L. Thermodynamic modelling of growth-restriction effects in aluminium alloys [J]. Acta Mater., 2005, 53: 1323 |
| 74 | Men H, Fan Z. Effects of solute content on grain refinement in an isothermal melt [J]. Acta Mater., 2011, 59: 2704 |
| 75 | Qi X B, Chen Y, Kang X H, et al. An analytical approach for predicting as-cast grain size of inoculated aluminum alloys [J]. Acta Mater., 2015, 99: 337 |
| 76 | Schmid-Fetzer R, Kozlov A. Thermodynamic aspects of grain growth restriction in multicomponent alloy solidification [J]. Acta Mater., 2011, 59: 6133 |
| 77 | Wu G H, Tong X, Jiang R, et al. Grain refinement of as-cast Mg-RE alloys: Research progress and future prospect [J]. Acta Metall. Sin., 2022, 58: 385 |
| 77 | 吴国华, 童 鑫, 蒋 锐 等. 铸造Mg-RE合金晶粒细化行为研究现状与展望 [J]. 金属学报, 2022, 58: 385 |
| 78 | Liu Z Y, Zhao D D, Wang P, et al. Additive manufacturing of metals: Microstructure evolution and multistage control [J]. J. Mater. Sci. Technol., 2022, 100: 224 |
| 79 | Froes F H, Kim Y W, Hehmann F. Rapid solidification of Al, Mg and Ti [J]. JOM, 1987, 39(8): 14 |
| 80 | Takata N, Liu M L, Kodaira H, et al. Anomalous strengthening by supersaturated solid solutions of selectively laser melted Al-Si-based alloys [J]. Addit. Manuf., 2020, 33: 101152 |
| 81 | Ahmad N A, Wheeler A A, Boettinger W J, et al. Solute trapping and solute drag in a phase-field model of rapid solidification [J]. Phys. Rev., 1998, 58E: 3436 |
| 82 | Aziz M J. An atomistic model of solute trapping [A]. Rapid Solidification Processing: Principles and Technologies III: Proceedings of the Third Conference on Rapid Solidification Processing Held at the National Bureau of Standards [C]. Gaithersburg, Maryland: The Bureau, 1982: 113 |
| 83 | Aziz M J. Model for solute redistribution during rapid solidification [J]. J. Appl. Phys., 1982, 53: 1158 |
| 84 | Aziz M J, Kaplan T. Continuous growth model for interface motion during alloy solidification [J]. Acta Metall., 1988, 36: 2335 |
| 85 | Froes F H, Kim Y W, Krishnamurthy S. Rapid solidification of lightweight metal alloys [J]. Mater. Sci. Eng., 1989, A117: 19 |
| 86 | Fiocchi J, Tuissi A, Biffi C A. Heat treatment of aluminium alloys produced by laser powder bed fusion: A review [J]. Mater. Des., 2021, 204: 109651 |
| 87 | Liu G C, Gao J B, Che C, et al. Optimization of casting means and heat treatment routines for improving mechanical and corrosion resistance properties of A356-0.54Sc casting alloy [J]. Mater. Today Commun., 2020, 24: 101227 |
| 88 | Geng R W, Du J, Wei Z Y, et al. Current research status of phase field simulation for microstructures of additively manufactured metals [J]. Mater. Rep., 2018, 32: 1145 |
| 88 | 耿汝伟, 杜 军, 魏正英 等. 金属增材制造中微观组织相场法模拟研究进展 [J]. 材料导报, 2018, 32: 1145 |
| 89 | DebRoy T, Mukherjee T, Wei H L, et al. Metallurgy, mechanistic models and machine learning in metal printing [J]. Nat. Rev. Mater., 2020, 6: 48 |
| 90 | Wheeler A A, Boettinger W J, McFadden G B. Phase-field model of solute trapping during solidification [J]. Phys. Rev., 1993, 47E: 1893 |
| 91 | Kim S G, Kim W T, Suzuki T. Phase-field model for binary alloys [J]. Phys. Rev., 1999, 60E: 7186 |
| 92 | Steinbach I, Pezzolla F, Nestler B, et al. A phase field concept for multiphase systems [J]. Physica, 1996, 94D: 135 |
| 93 | Steinbach I, Zhang L J, Plapp M. Phase-field model with finite interface dissipation [J]. Acta Mater., 2012, 60: 2689 |
| 94 | Zhang L J, Steinbach I. Phase-field model with finite interface dissipation: Extension to multi-component multi-phase alloys [J]. Acta Mater., 2012, 60: 2702 |
| 95 | Zhang Z, Yao X X, Ge P. Phase-field-model-based analysis of the effects of powder particle on porosities and densities in selective laser sintering additive manufacturing [J]. Int. J. Mech. Sci., 2020, 166: 105230 |
| 96 | Yang Y Y W, Ragnvaldsen O, Bai Y, et al. 3D non-isothermal phase-field simulation of microstructure evolution during selective laser sintering [J]. npj Comput. Mater., 2019, 5: 81 |
| 97 | Yang M, Wang L, Yan W T. Phase-field modeling of grain evolution in additive manufacturing with addition of reinforcing particles [J]. Addit. Manuf., 2021, 47: 102286 |
| 98 | Jiang P, Gao S, Geng S N, et al. Multi-physics multi-scale simulation of the solidification process in the molten pool during laser welding of aluminum alloys [J]. Int. J. Heat Mass Transfer, 2020, 161: 120316 |
| 99 | Gunasegaram D R, Steinbach I. Modelling of microstructure formation in metal additive manufacturing: Recent progress, research gaps and perspectives [J]. Metals, 2021, 11: 1425 |
| 100 | Karayagiz K, Johnson L, Seede R, et al. Finite interface dissipation phase field modeling of Ni-Nb under additive manufacturing conditions [J]. Acta Mater., 2020, 185: 320 |
| 101 | O'Toole P I, Patel M J, Tang C, et al. Multiscale simulation of rapid solidification of an aluminium-silicon alloy under additive manufacturing conditions [J]. Addit. Manuf., 2021, 48: 102353 |
| 102 | Yang X, Zhang L J, Sobolev S, et al. Kinetic phase diagrams of ternary Al-Cu-Li system during rapid solidification: A phase-field study [J]. Materials, 2018, 11: 260 |
| 103 | Li Y L, Gu D D. Parametric analysis of thermal behavior during selective laser melting additive manufacturing of aluminum alloy powder [J]. Mater. Des., 2014, 63: 856 |
| 104 | Geng R W, Du J, Wei Z Y, et al. Modelling and experimental observation of the deposition geometry and microstructure evolution of aluminum alloy fabricated by wire-arc additive manufacturing [J]. J. Manuf. Process., 2021, 64: 369 |
| 105 | Vastola G, Zhang G, Pei Q X, et al. Controlling of residual stress in additive manufacturing of Ti6Al4V by finite element modeling [J]. Addit. Manuf., 2016, 12: 231 |
| 106 | Campoli G, Borleffs M S, Yavari S A, et al. Mechanical properties of open-cell metallic biomaterials manufactured using additive manufacturing [J]. Mater. Des., 2013, 49: 957 |
| 107 | Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects [J]. Science, 2015, 349: 255 |
| 108 | Johnson N S, Vulimiri P S, To A C, et al. Invited review: Machine learning for materials developments in metals additive manufacturing [J]. Addit. Manuf., 2020, 36: 101641 |
| 109 | Du Y, Mukherjee T, Mitra P, et al. Machine learning based hierarchy of causative variables for tool failure in friction stir welding [J]. Acta Mater., 2020, 192: 67 |
| 110 | Silbernagel C, Aremu A, Ashcroft I. Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing [J]. Rapid Prototyp. J., 2020, 26: 625 |
| 111 | Liu Q, Wu H K, Paul M J, et al. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms [J]. Acta Mater., 2020, 201: 316 |
| 112 | Caiazzo F, Caggiano A. Laser direct metal deposition of 2024 Al alloy: Trace geometry prediction via machine learning [J]. Materials, 2018, 11: 444 |
| 113 | Mishra R S, Thapliyal S. Design approaches for printability-performance synergy in Al alloys for laser-powder bed additive manufacturing [J]. Mater. Des., 2021, 204: 109640 |
| 114 | Gao J B, Zhong J, Liu G C, et al. A machine learning accelerated distributed task management system (Malac-Distmas) and its application in high-throughput CALPHAD computation aiming at efficient alloy design [J]. Adv. Powder Mater., 2022, 1: 100005 |
| 115 | Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343 |
| 115 | 谢建新, 宿彦京, 薛德祯 等. 机器学习在材料研发中的应用 [J]. 金属学报, 2021, 57: 1343 |
| 116 | Dai R, Yang S L, Zhang T D, et al. High-throughput screening of optimal process parameters for PVD TiN coatings with best properties through a combination of 3-D quantitative phase-field simulation and hierarchical multi-objective optimization strategy [J]. Front. Mater., 2022, 9: 924294 |
/
| 〈 |
|
〉 |