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Acta Metall Sin  2023, Vol. 59 Issue (1): 87-105    DOI: 10.11900/0412.1961.2022.00430
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Current Situation and Prospect of Computationally Assisted Design in High-Performance Additive Manufactured Aluminum Alloys: A Review
GAO Jianbao1, LI Zhicheng1, LIU Jia1, ZHANG Jinliang2, SONG Bo2(), ZHANG Lijun1()
1.State Key Lab of Powder Metallurgy, Central South University, Changsha 410083, China
2.State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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GAO Jianbao, LI Zhicheng, LIU Jia, ZHANG Jinliang, SONG Bo, ZHANG Lijun. Current Situation and Prospect of Computationally Assisted Design in High-Performance Additive Manufactured Aluminum Alloys: A Review. Acta Metall Sin, 2023, 59(1): 87-105.

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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.

Key words:  additive manufactured aluminum alloy      computational thermodynamics      phase-field simulation      machine learning      integrated computational materials engineering      multi-objective optimization     
Received:  31 August 2022     
ZTFLH:  TG146.2  
Fund: 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)
About author:  SONG Bo, professor, Tel: (027)87558155, E-mail:
ZHANG Lijun, professor, Tel: (0731)88836812, E-mail:;

URL:     OR

Fig.1  Plot of solid solution strengthening increment as a function of solute concentration (atomic fraction) in the matrix (a) and plot of diffusivity at 400oC vs maximum equilibrium solid solubility (atomic fraction) for selected solutes in Al (b)[37]
Fig.2  Finally solidified structures of the aluminum nano-powder bed processed by micro-selective laser melting (μ-SLM) model using molecular dynamics. Ten laser tracks have fused together a total of 453 nano-powders of average diameter 9.67 ?nm in three different layers shown in first layer (a), second layer (b), and third layer (c) (Dark semi-circles outline the contours of the melt-pool boundaries; the grains that span across the three layers are explicitly numbered in Fig.2c; the solidified nanostructure is dominated by epitaxially grown grains. Other important structural features such as equiaxed grains, nano-pores, twin boundaries, and stacking faults are labeled in Fig.2c)[41]
Fig.3  Strategic workflow of alloy design approach for additive manufacturing aluminum alloys driven by computational thermodynamics[53] (HSI—hot susceptibility index or crack susceptibility index (CSI), ΔTCTR—craitical temperature range, Qtrue—growth restriction factor for true alloy system)
Fig.4  Solidification structure with characteristic temperatures in hot tearing[60] (a), and Scheil-Gulliver solidification simulations (b) and comparison of the calculated brittle temperature range ΔTBTR TBTR = TZST - TZDT) (c) for AlSi10Mg, 6061, and 7075 aluminum alloys[54] (TZST—zero strength temperature, TZDT—zero ductility temperature)
Fig.5  Schematic diagram of the crack susceptibility index (CSI) in the Kou criterion[55,66] (a) and comparison of the calculated CSI (CSI = |dT / d(fS)1/2|, when (fS)1/2 near 1)[67] and experimental crack susceptibility[62,63] of wrought Al alloys (b) (t is time, R is the characteristic radius of grain)
Fig.6  Calculational results of Scheil solidification and CSI of Si-modified Al7075 alloys, and grain morphologies of SLM processed Al7075 alloy (Al-5.10Zn-2.67Mg-1.55Cu, mass fraction, %) without and with Si[58]
(a) T vs (fS)1/2 curves of the Al7075 alloys with 0.17% to 8% Si additions (mass fraction), the dashed vertical line indicates (fS)1/2 = 0.99
(b) variation of crack susceptibility of Al7075 alloys as a function of Si additions (Insets show the defect morphologies of Si-modified SLM processed Al7075 alloy)
(c-f) grain morphologies of SLM processed Al7075 with solidification cracks (c, e), and crack-free Si-modified Al7075 alloy (Al7075-3.74Si%) processed by SLM (d, f) (BD—building direction)
(g, h) schematics of epitaxial growth of the Al7075 alloy (g) and the solidification cracking mitigation mechanism in the Si-modified Al7075 (h) (Tl is the liquidus temperature, Ts or Ts1 is the terminal temperature of solidification, Ts2 is the temperature of full columnar grains zone, ΔT is solidification temperature range (ΔT = Tl - Ts), //ΔGmax represents that the growth direction of columnar grains is preferentially oriented parallel to the direction of the maximum temperature gradient G)
Fig.7  A novel crack-free Ti-modified Al-Cu-Mg alloy design flow for SLM[21]
(a) vertical section of Al-2.25Cu-1.8Mg-xTi alloys (mass fraction, %)
(b) Tvs (fS)1/2 curves of Al-Cu-Mg-xTi alloys with Ti contents ranging from 0 to 2%
(c) calculated Qtrue values in Ti/Al-Cu-Mg alloys for constrained L→α-Al solidification with Ti contents ranging from 0 to 2% (Metastable solidification conditions are used at higher values of Ti content to suppress the formation of intermetallic phases)
(d) schematic of cracking mechanism in the Al-Cu-Mg alloy
(e, f) SEM image (e) and inverse pole figure (IPF) (f) showing the microstructure in the cracked zone of Al-Cu-Mg alloy
(g, h) SEM image (g) and IPF (h) of Ti/Al-Cu-Mg alloy consisting of fine equiaxed grains without cracks



EquilibriumMaximum extended solubility
Maximum solubilityCe or Cp
Co< 0.010.450.5-5
Table 1  Solubility limits of solutes in binary aluminum alloys under equilibrium and rapid solidification conditions (~106 K/s)[79]
Fig.8  Solute trapping and kinetic phase diagram of the Al-Cu system under rapid solidification conditions using phase-field model with finite interface dissipation[102]
(a) phase-field simulated steady-state concentration profiles with three different interface moving velocities (or solidification rate) V in Al-Cu system (The red dash line denotes the phase field, the bule solid lines denote the overall concentrations, while the dotted lines denote the liquid concentrations)
(b) phase-field simulated solute segregation coefficient and solidification temperature as a function of interface velocity (or solidification rate) in Al-1.1%Cu (atomic fraction) alloy
(c) model-predicted kinetic phase diagrams at different interface velocities (or solidification rate) due to the 1-D phase-field simulation using the time-elimination relaxation scheme of the Al-Cu system
Fig.9  Evolutions of the grain structure during solidification process in the laser molten pool with time[98]
(a) t = 0 ms (b) t = 25.0 ms (c) t = 50.0 ms
Fig.10  Crack formation mechanism of Al-Cu-Mg alloy[32]
(a) simulated stress distribution of molten pool
(b) grain morphology in experimental molten pool
(c) schematic of solidification process and crack formation
Fig.11  A physics-informed machine learning towards crack-free printing[33]. Computed values of cooling rate and solidification morphology (indicated by the ratio of temperature gradient and solidification growth rate) at the trailing edge of the melt pool, the ratio of vulnerable and relaxation times, and solidification stress are used in a physics informed machine learning to accurately predict cracking during SLM of aluminum alloys. The combination of machine learning and mechanistic modeling gives a cracking susceptibility index, process maps for crack-free printing, the comparative influence of the important variables, and a decision tree to predict crack formation[33] (PBF-L—powder bed fusion- laser)
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