Additive manufacturing as a tool for high-throughput experimentation

Abstract

Additive manufacturing (AM) is a disruptive technology with a unique capability in fabricating parts with complex geometry and fixing broken supply chains. However, many AM techniques are complicated with their processing features due to complex heating and cooling cycles with the melting of feedstock materials. Therefore, it is quite challenging to directly apply the materials design and processing optimization method used for conventional manufacturing to AM techniques. In this viewpoint paper, we discuss some of the ongoing efforts of high-throughput (HT) experimentation, which can be used for materials development and processing design. Particularly, we focus on the beam- and powder-based AM techniques since these methods have demonstrated success in HT experimentation. In addition, we propose new opportunities to apply AM techniques as the materials informatic tools contributing to materials genome.

Keywords

Additive manufacturing / integrated computational materials engineering / materials genome / materials informatics / machine learning

References

[1] Smith J,Yan W.Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support.Comput Mech2016;57:583-610

[2] Wang H,Chen H.Effect of cyclic rapid thermal loadings on the microstructural evolution of a CrMnFeCoNi high-entropy alloy manufactured by selective laser melting.Acta Mater2020;196:609-25

[3] Sargent N,Otis R,Delplanque J.Integration of processing and microstructure models for non-equilibrium solidification in additive manufacturing.Metals2021;11:570

[4] Sridar S,Wang X,Xiong W.Determination of location-specific solidification cracking susceptibility for a mixed dissimilar alloy processed by wire-arc additive manufacturing.Metals2022;12:284

[5] Xiong W.Integrated computational materials design for high-performance alloys.MRS Bull2015;40:1035-44

[6] Xiong W.Cybermaterials: materials by design and accelerated insertion of materials.npj Comput Mater2016;2

[7] Olson GB.Computational design of hierarchically structured materials.Science1997;277:1237-42

[8] Zhao J,Cahill DG.High-throughput diffusion multiples.Mater Today2005;8:28-37

[9] Zhao J.The diffusion-multiple approach to designing alloys.Annu Rev Mater Res2005;35:51-73

[10] Aalund R.Unveiling spark plasma sintering high-throughput processing. In: Bansal NP, Singh JP, Lamon J, Choi SR, Mahmoud MM, editors. Processing and properties of advanced ceramics and composites II. Hoboken: John Wiley & Sons, Inc.; 2010. pp. 1-10.

[11] Hui J,Wu Z.High-throughput investigation of crystal-to-glass transformation of Ti-Ni-Cu ternary alloy.Sci Rep2019;9:19932 PMCID:PMC6934457

[12] Yu Z,Li Z.Accelerated exploration of TRIP metallic glass composite by laser additive manufacturing.J Mater Sci Technol2021;78:68-73

[13] Moorehead M,Elbakhshwan M.High-throughput ion irradiation of additively manufactured compositionally complex alloys.J Nuclear Mater2021;547:152782

[14] Nie J,Li D,Jiang Y.High-throughput characterization of microstructure and corrosion behavior of additively manufactured SS316L-SS431 graded material.Add Manufact2020;35:101295

[15] Borkar T,Gwalani B.A Combinatorial approach for assessing the magnetic properties of high entropy alloys: role of Cr in AlCo_xCr_1-xFeNi: a combinatorial approach for assessing the magnetic.Adv Eng Mater2017;19:1700048

[16] Teh WH,Chen S.High throughput multi-property evaluation of additively manufactured Co-Fe-Ni materials libraries.Add Manufact2022;58:102983

[17] Schneck M,Schindler M.Capability of multi-material laser-based powder bed fusion-development and analysis of a prototype large bore engine component.Metals2022;12:44

[18] Zhao Y,Li K.A new high-throughput method using additive manufacturing for alloy design and heat treatment optimization.Materialia2020;13:100835

[19] Wei C.Gradient temperature heat treatment for efficient study of phase precipitation in a high-temperature Fe-Cr-Mo ferritic steel.Materialia2018;3:31-40

[20] Heckman NM,Roach AM.Automated high-throughput tensile testing reveals stochastic process parameter sensitivity.Mater Sci Eng2020;772:138632

[21] Jalali SIA,Jayaram V.High throughput determination of creep parameters using cantilever bending: part I - steady-state.J Mater Res2020;35:353-61

[22] Jalali SI, Kumar P, Jayaram V. High throughput determination of creep parameters using cantilever bending: part II - primary and steady-state through uniaxial equivalency.J Mater Res2020;35:362-71

[23] Vignesh B,Kumar GS.Critical assessment of high speed nanoindentation mapping technique and data deconvolution on thermal barrier coatings.Mater Design2019;181:108084

[24] Chawla N,Wunsch B.Three-dimensional (3D) microstructure visualization and finite element modeling of the mechanical behavior of SiC particle reinforced aluminum composites.Script Mater2004;51:161-5

[25] Hagita K,Jinnai H.Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning.Sci Rep2018;8:5877 PMCID:PMC5897388

[26] Pirgazi H.On the alignment of 3D EBSD data collected by serial sectioning technique.Mater Charact2019;152:223-9

[27] Zhao Y,Gargani M.A comparative analysis of Inconel 718 made by additive manufacturing and suction casting: microstructure evolution in homogenization.Add Manufact2020;36:101404

[28] Zhao Y,Liu C,Xiong W.Impact of homogenization on microstructure-property relationships of Inconel 718 alloy prepared by laser powder bed fusion.Mater Sci Eng2021;826:141973

[29] Zhao L,Yang L.High throughput synthesis enabled exploration of CoCrFeNi-based high entropy alloys.J Mater Sci Technol2022;110:269-82