Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods

References

• Elahinia M, Shayesteh Moghaddam N, Taheri Andani M, et al. Fabrication of NiTi through additive manufacturing: a review. Prog Mater Sci. 2016;83:630–663. doi:10.1016/j.pmatsci.2016.08.001
   Web of Science ®Google Scholar
• DebRoy T, Wei HL, Zuback JS, et al. Additive manufacturing of metallic components-process, structure and properties. Prog Mater Sci. 2018;92:112–224. doi:10.1016/j.pmatsci.2017.10.001
   Web of Science ®Google Scholar
• Zhang Z, Demir KG, Gu GX. Developments in 4D-printing: a review on current smart materials, technologies, and applications. Int J Smart Nano Mater. 2019;10(3):205–224. doi:10.1080/19475411.2019.1591541
   Web of Science ®Google Scholar
• Guo W, Feng B, Yang Y, et al. Effect of laser scanning speed on the microstructure, phase transformation and mechanical property of NiTi alloys fabricated by LPBF. Mater Des. 2022;215:110460. doi:10.1016/j.matdes.2022.110460
   Web of Science ®Google Scholar
• Xue L, Atli KC, Picak S, et al. Controlling martensitic transformation characteristics in defect-free NiTi shape memory alloys fabricated using laser powder bed fusion and a process optimization framework. Acta Mater. 2021;215:117017. doi:10.1016/j.actamat.2021.117017
   Web of Science ®Google Scholar
• Lu HZ, Ma HW, Cai WS, et al. Stable tensile recovery strain induced by a Ni4Ti3 nanoprecipitate in a Ni50.4Ti49.6 shape memory alloy fabricated via selective laser melting. Acta Mater. 2021;219:117261. doi:10.1016/j.actamat.2021.117261
   Web of Science ®Google Scholar
• Zhang Q, Hao S, Liu Y, et al. The microstructure of a selective laser melting (SLM)-fabricated NiTi shape memory alloy with superior tensile property and shape memory recoverability. Appl Mater Today. 2020;19:100547. doi:10.1016/j.apmt.2019.100547
   Web of Science ®Google Scholar
• Sharma N, Jangra KK, Raj T. Fabrication of NiTi alloy: a review. Proc Inst Mech Eng, Part L: J Mater: Des Appl. 2015;232(3):250–269. doi:10.1177/1464420715622494
   Google Scholar
• DebRoy T, Mukherjee T, Milewski JO, et al. Scientific, technological and economic issues in metal printing and their solutions. Nat Mater. 2019;18(10):1026–1032. doi:10.1038/s41563-019-0408-2
   PubMed Web of Science ®Google Scholar
• Wang X, Yu J, Liu J, et al. Effect of process parameters on the phase transformation behavior and tensile properties of NiTi shape memory alloys fabricated by selective laser melting. Add Manuf. 2020;36:101545. doi:10.1016/j.addma.2020.101545
   Web of Science ®Google Scholar
• Alagha AN, Hussain S, Zaki W. Additive manufacturing of shape memory alloys: A review with emphasis on powder bed systems. Mater Des. 2021;204:109654. doi:10.1016/j.matdes.2021.109654
   Web of Science ®Google Scholar
• Feng B, Wang C, Zhang Q, et al. Effect of laser hatch spacing on the pore defects, phase transformation and properties of selective laser melting fabricated NiTi shape memory alloys. Mater Sci Eng A. 2022;840:142965. doi:10.1016/j.msea.2022.142965
   Web of Science ®Google Scholar
• Shen FL, Li HQ, Guo H, et al. Effect of energy density on the superelastic property of Ni-rich NiTi alloy fabricated by laser powder bed fusion. Mater Sci Eng A. 2022;854:143874. doi:10.1016/j.msea.2022.143874
   Web of Science ®Google Scholar
• Lu L-X, Jiang H, Bian Q, et al. Integrated modelling and simulation of NiTi alloy by powder bed fusion: single track study. Mater Des. 2023;227:111755. doi:10.1016/j.matdes.2023.111755
   Web of Science ®Google Scholar
• Wen Y, Wu X, Huang A, et al. Laser powder bed fusion of immiscible steel and bronze: A compositional gradient approach for optimum constituent combination. Acta Mater. 2024;264:119572. doi:10.1016/j.actamat.2023.119572
   Web of Science ®Google Scholar
• Gao J, Li Z, Liu J, et al. Current situation and prospect of computationally assisted design in high-performance additive manufactured aluminum alloys: a review. Acta Metall Sinica. 2023;59(1):87–105. doi:10.11900/0412.1961.2022.00430
   Web of Science ®Google Scholar
• Sing SL, Kuo CN, Shih CT, et al. Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing. Virtual Phys Prototyp. 2021;16(3):372–386. doi:10.1080/17452759.2021.1944229
   Web of Science ®Google Scholar
• Gu D, Ma C, Dai D, et al. Additively manufacturing-enabled hierarchical NiTi-based shape memory alloys with high strength and toughness. Virtual Phys Prototyp. 2021;16(sup1):19–38. doi:10.1080/17452759.2021.1892389
   Web of Science ®Google Scholar
• Lu HZ, Yang C, Luo X, et al. Ultrahigh-performance TiNi shape memory alloy by 4D printing. Mater Sci Eng A. 2019;763:138166. doi:10.1016/j.msea.2019.138166
   Web of Science ®Google Scholar
• Xiong Z, Li Z, Sun Z, et al. Selective laser melting of NiTi alloy with superior tensile property and shape memory effect. J Mater Sci Technol. 2019;35(10):2238–2242. doi:10.1016/j.jmst.2019.05.015
   Web of Science ®Google Scholar
• Zhang J, Yin C, Xu Y, et al. Machine learning applications for quality improvement in laser powder bed fusion: a state-of-the-art review. Int J AI Mater Des. 2024;1(1). doi:10.36922/ijamd.2301
   Google Scholar
• Goh GD, Huang X, Huang S, et al. Data imputation strategies for process optimization of laser powder bed fusion of Ti6Al4V using machine learning. Mater Sci Addit Manuf. 2023;2(1). doi:10.36922/msam.50
   Google Scholar
• Zhang T, Zhong J, Zhang L. Pareto optimal driven automation framework for quantitative microstructure simulation towards spinodal decomposition. MRS Commun. 2023;13(5):877–884. doi:10.1557/s43579-023-00429-z
   Web of Science ®Google Scholar
• Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–260. doi:10.1126/science.aaa8415
   PubMed Web of Science ®Google Scholar
• Hu Z, Mahadevan S. Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities. Int J Adv Manuf Technol. 2017;93(5):2855–2874. doi:10.1007/s00170-017-0703-5
   Web of Science ®Google Scholar
• Gan J, Duan L, Li F, et al. Effect of laser energy density on the evolution of Ni4Ti3 precipitate and property of NiTi shape memory alloys prepared by selective laser melting. J Alloys Compd. 2021;869:159338. doi:10.1016/j.jallcom.2021.159338
   Web of Science ®Google Scholar
• Guo W, Sun Z, Yang Y, et al. Study on the junction zone of NiTi shape memory alloy produced by selective laser melting via a stripe scanning strategy. Intermetallics. 2020;126:106947. doi:10.1016/j.intermet.2020.106947
   Web of Science ®Google Scholar
• Lu HZ, Liu LH, Yang C, et al. Simultaneous enhancement of mechanical and shape memory properties by heat-treatment homogenization of Ti2Ni precipitates in TiNi shape memory alloy fabricated by selective laser melting. J Mater Sci Technol. 2022;101:205–216. doi:10.1016/j.jmst.2021.06.019
   Web of Science ®Google Scholar
• Lu HZ, Ma HW, Cai WS, et al. Altered phase transformation behaviors and enhanced bending shape memory property of NiTi shape memory alloy via selective laser melting. J Mater Process Technol. 2022;303:117546. doi:10.1016/j.jmatprotec.2022.117546
   Web of Science ®Google Scholar
• Mahmoudi M, Tapia G, Franco B, et al. On the printability and transformation behavior of nickel-titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing. J Manuf Process. 2018;35:672–680. doi:10.1016/j.jmapro.2018.08.037
   Web of Science ®Google Scholar
• Ren Q, Chen C, Lu Z, et al. Effect of a constant laser energy density on the evolution of microstructure and mechanical properties of NiTi shape memory alloy fabricated by laser powder bed fusion. Opt Laser Technol. 2022;152:108182. doi:10.1016/j.optlastec.2022.108182
   Web of Science ®Google Scholar
• Saedi S, Shayesteh Moghaddam N, Amerinatanzi A, et al. On the effects of selective laser melting process parameters on microstructure and thermomechanical response of Ni-rich NiTi. Acta Mater. 2018;144:552–560. doi:10.1016/j.actamat.2017.10.072
   Web of Science ®Google Scholar
• Shayesteh Moghaddam N, Saghaian SE, Amerinatanzi A, et al. Anisotropic tensile and actuation properties of NiTi fabricated with selective laser melting. Mater Sci Eng A. 2018;724:220–230. doi:10.1016/j.msea.2018.03.072
   Web of Science ®Google Scholar
• Shi G, Li L, Yu Z, et al. The interaction effect of process parameters on the phase transformation behavior and tensile properties in additive manufacturing of Ni-rich NiTi alloy. J Manuf Process. 2022;77:539–550. doi:10.1016/j.jmapro.2022.03.027
   Web of Science ®Google Scholar
• Yang Y, Zhan JB, Li B, et al. Laser beam energy dependence of martensitic transformation in SLM fabricated NiTi shape memory alloy. Materialia. 2019;6:100305. doi:10.1016/j.mtla.2019.100305
   Google Scholar
• Yang Y, Zhan JB, Sun ZZ, et al. Evolution of functional properties realized by increasing laser scanning speed for the selective laser melting fabricated NiTi alloy. J Alloys Compd. 2019;804:220–229. doi:10.1016/j.jallcom.2019.06.340
   Web of Science ®Google Scholar
• Yu C, Hu Y, lu W, et al. Effect of hatch spacing on phase transformation behavior and mechanical properties of NiTi shape memory alloy fabricated by selective laser melting. Laser Optoelectron Prog. 2021;58(19):1–10. doi:10.3788/LOP202158.1914008
   Google Scholar
• Yu Z, Xu Z, Guo Y, et al. Study on properties of SLM-NiTi shape memory alloy under the same energy density. J Mater Res Technol. 2021;13:241–250. doi:10.1016/j.jmrt.2021.04.058
   Google Scholar
• Gong X, Groeneveld-Meijer W, Manogharan G. Additive manufacturing: application and validation of machine learning-based process-structure-property linkages in Ti-6Al-4V. Mater Sci Addit Manuf. 2023;2(3). doi:10.36922/msam.0999
   Google Scholar
• Koh HK, Moo JGS, Leong Sing S, et al. Use of fumed silica nanostructured additives in selective laser melting and fabrication of steel matrix nanocomposites. Materials. 2022;15(5):1869. doi:10.3390/ma15051869.
   PubMed Web of Science ®Google Scholar
• Sow MC, De Terris T, Castelnau O, et al. Influence of beam diameter on Laser Powder Bed Fusion (L-PBF) process. Addit Manuf. 2020;36:101532. doi:10.1016/j.addma.2020.101532
   Web of Science ®Google Scholar
• Agrawal AK, Rankouhi B, Thoma DJ. Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions. Curr Opin Solid State Mater Sci. 2022;26(6):101024. doi:10.1016/j.cossms.2022.101024
   Web of Science ®Google Scholar
• Dai R, Yang S, Zhang T, 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. Front Mater. 2022;9:924294. doi:10.3389/fmats.2022.924294
   Web of Science ®Google Scholar
• Liu P, Huang H, Wen C, et al. The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization. NPJ Computat Mater. 2023;9(1):140. doi:10.1038/s41524-023-01090-9
   Web of Science ®Google Scholar
• Otsuka K, Ren X. Physical metallurgy of Ti-Ni-based shape memory alloys. Prog Mater Sci. 2005;50(5):511–678. doi:10.1016/j.pmatsci.2004.10.001
   Web of Science ®Google Scholar
• Xue L, Atli KC, Zhang C, et al. Laser powder bed fusion of defect-free NiTi shape memory alloy parts with superior tensile superelasticity. Acta Mater. 2022;229:117781. doi:10.1016/j.actamat.2022.117781
   Web of Science ®Google Scholar