Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys
Source abstract
Abstract
Abstract With the increasing use of CubeSats in space exploration, the demand for reliable high-temperature shape memory alloys (HTSMA) continues to grow. A wide range of HTSMAs has been investigated over the past decade but finding suitable alloys by means of trial-and-error experiments is cumbersome and time-consuming. The present work uses a data-driven approach to identify NiTiHf alloys suitable for actuator applications in space. Seven machine learning (ML) models were evaluated, and the best fit model was selected to identify new alloy compositions with targeted transformation temperature (Ms), thermal hysteresis, and work output. Of the studied models, the K-nearest neighbouring ML model offers more reliable and accurate prediction in developing NiTiHf alloys with balanced functional properties and aids our existing understanding on compositional dependence of transformation temperature, thermal hysteresis and work output. For instance, the transformation temperature of NiTiHf alloys is more sensitive to Ni variation with increasing Hf content. A maximum Ms reduction rate of 6.12 °C per 0.01 at.% Ni is attained at 30 at.% Hf, and with a Ni content between 50 and 51 at.%. Graphical abstract
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Shape Memory Alloy Transformations · Magnetic and transport properties of perovskites and related materials · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (66)
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1. Elahinia MH, Hashemi M, Tabesh M, Bhaduri SB (2012) Manufacturing and processing of NiTi implants: a review. Prog Mater Sci 57(5):911–946. https://doi.org/10.1016/j.pmatsci.2011.11.00110.1016/j.pmatsci.2011.11.001 ↗
2. Carvalho A, Freitas M, Reis L, Montalvão D, Fonte M (2016) Rotary Fatigue testing to determine the fatigue life of NiTi alloy wires: an experimental and numerical analisys. Proced Struct Int 1:34–41. https://doi.org/10.1016/j.prostr.2016.02.00610.1016/j.prostr.2016.02.006 ↗
3. Otsuka K, Ren X (2005) Physical metallurgy of Ti–Ni-based shape memory alloys. Prog Mater Sci 50(5):511–678. https://doi.org/10.1016/j.pmatsci.2004.10.00110.1016/j.pmatsci.2004.10.001 ↗
4. Evirgen A, Pons J, Karaman I, Santamarta R, Noebe RD (2018) H-Phase precipitation and martensitic transformation in Ni-rich Ni–Ti–Hf and Ni–Ti-Zr High-temperature shape memory alloys. Shape Memory and Superelasticity 4(1):85–92. https://doi.org/10.1007/s40830-018-0165-010.1007/s40830-018-0165-0 ↗
5. Ma J, Karaman I, Noebe RD (2013) High temperature shape memory alloys. Int Mater Rev 55(5):257–315. https://doi.org/10.1179/095066010x1264689872836310.1179/095066010x12646898728363 ↗
6. Bucsek AN, Hudish GA, Bigelow GS, Noebe RD, Stebner AP (2016) Composition, compatibility, and the functional performances of ternary NiTiX high-temperature shape memory alloys. Shape Memory and Superelasticity 2(1):62–7910.1007/s40830-016-0052-5 ↗
7. Benafan O, Bigelow GS, Young AW (2020) Shape memory materials database tool—a Compendium of functional data for shape memory materials. Adv Eng Mater 22(7):1901370. https://doi.org/10.1002/adem.20190137010.1002/adem.201901370 ↗
8. Benafan O, Bigelow GS, Garg A, Noebe RD (2019) Viable low temperature shape memory alloys based on Ni-Ti-Hf formulations. Scripta Mater 164:115–120. https://doi.org/10.1016/j.scriptamat.2019.01.01910.1016/j.scriptamat.2019.01.019 ↗
9. Umale T, Salas D, Tomes B, Arroyave R, Karaman I (2019) The effects of wide range of compositional changes on the martensitic transformation characteristics of NiTiHf shape memory alloys. Scripta Mater 161:78–83. https://doi.org/10.1016/j.scriptamat.2018.10.00810.1016/j.scriptamat.2018.10.008 ↗
10. Karakoc O, Atli KC, Benafan O, Noebe RD, Karaman I (2022) Actuation fatigue performance of NiTiZr and comparison to NiTiHf high temperature shape memory alloys. Mater Sci Eng, A 829:142154. https://doi.org/10.1016/j.msea.2021.14215410.1016/j.msea.2021.142154 ↗
11. Prasher M, Sen D, Tewari R, Krishnan M (2021) Tuning the thermal cyclic stability of martensitic transformation in Ni50.3Ti29.7Hf20 high temperature shape memory alloy. Mater Res Bull 133:111056. https://doi.org/10.1016/j.materresbull.2020.11105610.1016/j.materresbull.2020.111056 ↗
12. Mehrpouya M, Gisario A, Nematollahi M, Rahimzadeh A, Baghbaderani KS, Elahinia M (2021) The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy. Mater Today Commun 26:102022. https://doi.org/10.1016/j.mtcomm.2021.10202210.1016/j.mtcomm.2021.102022 ↗
13. Benafan O, Bigelow GS, Garg A, Noebe RD, Gaydosh DJ, Rogers RB (2021) Processing and scalability of NiTiHf High-temperature shape memory alloys. Shape Memory Super 7(1):109–165. https://doi.org/10.1007/s40830-020-00306-x10.1007/s40830-020-00306-x ↗
14. Toker GP et al (2020) Shape memory behavior of NiTiHf alloys fabricated by selective laser melting. Scripta Mater 178:361–365. https://doi.org/10.1016/j.scriptamat.2019.11.05610.1016/j.scriptamat.2019.11.056 ↗
15. Costanza G, Tata ME (2020) Shape memory alloys for aerospace, recent developments, and new applications: a short review. Materials 13(8):1856. https://doi.org/10.3390/ma1308185610.3390/ma13081856 ↗
16. Matovic J, Reichenberger K (2010) Two - way SMA actuators for space application: performances and reliability. Proced Eng 5:1372–1375. https://doi.org/10.1016/j.proeng.2010.09.37010.1016/j.proeng.2010.09.370 ↗
17. Guo W et al (2017) A self-driven temperature and flow rate co-adjustment mechanism based on Shape-Memory-Alloy (SMA) assembly for an adaptive thermal control coldplate module with on-orbit service characteristics. Appl Therm Eng 114:744–755. https://doi.org/10.1016/j.applthermaleng.2016.12.04810.1016/j.applthermaleng.2016.12.048 ↗
18. Mohd Jani J, Leary M, Subic A, Gibson MA (2014) A review of shape memory alloy research, applications and opportunities. Mater Design 56:1078–1113. https://doi.org/10.1016/j.matdes.2013.11.08410.1016/j.matdes.2013.11.084 ↗
19. Meng XL, Cai W, Fu YD, Li QF, Zhang JX, Zhao LC (2008) Shape-memory behaviors in an aged Ni-rich TiNiHf high temperature shape-memory alloy. Intermetallics 16(5):698–705. https://doi.org/10.1016/j.intermet.2008.02.00510.1016/j.intermet.2008.02.005 ↗
20. Meng XL, Cai W, Chen F, Zhao LC (2006) Effect of aging on martensitic transformation and microstructure in Ni-rich TiNiHf shape memory alloy. Scripta Mater 54(9):1599–1604. https://doi.org/10.1016/j.scriptamat.2006.01.01710.1016/j.scriptamat.2006.01.017 ↗
21. Meng XL, Zheng YF, Cai W, Zhao LC (2004) Two-way shape memory effect of a TiNiHf high temperature shape memory alloy. J Alloy Compd 372(1–2):180–186. https://doi.org/10.1016/j.jallcom.2003.10.02010.1016/j.jallcom.2003.10.020 ↗
22. Meng XL, Cai W, Zheng YF, Tong YX, Zhao LC, Zhou LM (2002) Stress-induced martensitic transformation behavior of a Ti–Ni–Hf high temperature shape memory alloy. Mater Lett 55(1–2):111–115. https://doi.org/10.1016/s0167-577x(01)00631-010.1016/s0167-577x(01)00631-0 ↗
23. Karakoc O et al (2017) Effects of upper cycle temperature on the actuation fatigue response of NiTiHf high temperature shape memory alloys. Acta Mater 138:185–197. https://doi.org/10.1016/j.actamat.2017.07.03510.1016/j.actamat.2017.07.035 ↗
24. Liu S, Kappes BB, Amin-Ahmadi B, Benafan O, Zhang X, Stebner AP (2021) Physics-informed machine learning for composition – process – property design: shape memory alloy demonstration. Appl Mater Today 22:100898. https://doi.org/10.1016/j.apmt.2020.10089810.1016/j.apmt.2020.100898 ↗
25. Akgul O, Tugrul HO, Kockar B (2020) Effect of the cooling rate on the thermal and thermomechanical behavior of NiTiHf high-temperature shape memory alloy. J Mater Res 35(12):1572–1581. https://doi.org/10.1557/jmr.2020.13910.1557/jmr.2020.139 ↗
26. Wang W, Wang H, Zhou J, Fan H, Liu X (2021) Machine learning prediction of mechanical properties of braided-textile reinforced tubular structures. Mater Des 212:110181. https://doi.org/10.1016/j.matdes.2021.11018110.1016/j.matdes.2021.110181 ↗
27. Zeng Y, Man M, Bai K, Zhang Y-W (2021) Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study. Mater Des 202:109532. https://doi.org/10.1016/j.matdes.2021.10953210.1016/j.matdes.2021.109532 ↗
28. Huang X, Jin C, Zhang C, Zhang H, Fu H (2021) Machine learning assisted modelling and design of solid solution hardened high entropy alloys. Mater Des 211:110177. https://doi.org/10.1016/j.matdes.2021.11017710.1016/j.matdes.2021.110177 ↗
29. Huang DJ, Li H (2021) A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing. Mater Des 203:109606. https://doi.org/10.1016/j.matdes.2021.10960610.1016/j.matdes.2021.109606 ↗
30. Tkachenko R, Duriagina Z, Lemishka I, Izonin I, Trostianchyn A (2018) Development of machine learning method of titanium alloy properties identification in additive technologies. Eastern-Eur J Enterp Technol 3(12 (93)):23–31. https://doi.org/10.15587/1729-4061.2018.13431910.15587/1729-4061.2018.134319 ↗
31. Ling J et al (2018) Machine learning for alloy composition and process optimization. Am Soc Mech Eng. https://doi.org/10.1115/gt2018-7520710.1115/gt2018-75207 ↗
32. Xue D et al (2017) An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater 125:532–541. https://doi.org/10.1016/j.actamat.2016.12.00910.1016/j.actamat.2016.12.009 ↗
33. Trehern W, Ortiz-Ayala R, Atli KC, Arroyave R, Karaman I (2022) Data-driven shape memory alloy discovery using artificial intelligence materials selection (AIMS) framework. Acta Mater 228:117751. https://doi.org/10.1016/j.actamat.2022.11775110.1016/j.actamat.2022.117751 ↗
34. Wen C et al (2019) Machine learning assisted design of high entropy alloys with desired property. Acta Mater 170:109–117. https://doi.org/10.1016/j.actamat.2019.03.01010.1016/j.actamat.2019.03.010 ↗
35. Wu C-T et al (2020) Machine learning recommends affordable new Ti alloy with bone-like modulus. Materials Today 34:41–50. https://doi.org/10.1016/j.mattod.2019.08.00810.1016/j.mattod.2019.08.008 ↗
36. Zhang Y, Xu X (2020) Transformation temperature predictions through computational intelligence for NiTi-based shape memory alloys. Shape Memory Super 6(4):374–386. https://doi.org/10.1007/s40830-020-00303-010.1007/s40830-020-00303-0 ↗
37. Catal AA, Bedir E, Yilmaz R, Canadinc D (2022) Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence. J Alloys Compd 904:164135. https://doi.org/10.1016/j.jallcom.2022.16413510.1016/j.jallcom.2022.164135 ↗
38. Abedi H, Baghbaderani KS, Alafaghani A, Nematollahi M, Kordizadeh F, Attallah MM, Qattawi A, Elahinia M (2022) Neural network modeling of NiTiHf shape memory alloy transformation temperatures. J Mater Eng Perform. https://doi.org/10.1007/s11665-022-06995-y10.1007/s11665-022-06995-y ↗
39. Evirgen A, Karaman I, Santamarta R, Pons J, Hayrettin C, Noebe RD (2016) Relationship between crystallographic compatibility and thermal hysteresis in Ni-rich NiTiHf and NiTiZr high temperature shape memory alloys. Acta Mater 121:374–383. https://doi.org/10.1016/j.actamat.2016.08.06510.1016/j.actamat.2016.08.065 ↗
40. Karakoc O et al (2019) Role of microstructure on the actuation fatigue performance of Ni-Rich NiTiHf high temperature shape memory alloys. Acta Mater 175:107–120. https://doi.org/10.1016/j.actamat.2019.05.05110.1016/j.actamat.2019.05.051 ↗
41. Demblon A et al (2022) Compositional and microstructural sensitivity of the actuation fatigue response in NiTiHf high temperature shape memory alloys. Mater Sci Eng: A 838:142786. https://doi.org/10.1016/j.msea.2022.14278610.1016/j.msea.2022.142786 ↗
42. Gama J, Brazdil P (2000) Cascade Generalization. Mach Learn 41(3):315–343. https://doi.org/10.1023/a:100765211487810.1023/a:1007652114878 ↗
43. Tugrul HO, Saygili HH, Velipasaoglu MS, Kockar B (2019) Comparison of the transformation behavior of cold rolling with aging and hot extrusion with aging processed Ni50.3Ti29.7Hf20 high temperature shape memory alloy. Smart Mater Struct 28(10):105029. https://doi.org/10.1088/1361-665x/ab39f310.1088/1361-665x/ab39f3 ↗
44. Saygili HH, Tugrul HO, Kockar B (2019) Effect of aging heat treatment on the high cycle fatigue life of Ni50.3Ti29.7Hf20 high-temperature shape memory alloy. Shape Memory Super 5(1):32–41. https://doi.org/10.1007/s40830-018-00202-510.1007/s40830-018-00202-5 ↗
45. Karakoc O, Hayrettin C, Canadinc D, Karaman I (2018) Role of applied stress level on the actuation fatigue behavior of NiTiHf high temperature shape memory alloys. Acta Mater 153:156–168. https://doi.org/10.1016/j.actamat.2018.04.02110.1016/j.actamat.2018.04.021 ↗
46. Benafan O, Bigelow GS, Scheiman DA (2018) Transformation behavior in NiTi-20Hf shape memory alloys—transformation temperatures and hardness. Scripta Mater 146:251–254. https://doi.org/10.1016/j.scriptamat.2017.11.05010.1016/j.scriptamat.2017.11.050 ↗
47. Amin-Ahmadi B, Pauza JG, Shamimi A, Duerig TW, Noebe RD, Stebner AP (2018) Coherency strains of H-phase precipitates and their influence on functional properties of nickel-titanium-hafnium shape memory alloys. Scripta Mater 147:83–87. https://doi.org/10.1016/j.scriptamat.2018.01.00510.1016/j.scriptamat.2018.01.005 ↗
48. Saghaian HEKM, Tobe H, Pons J, Santamarta R, Chumlyakov YI, Noebe RD (2016) Effects of Ni content on the shape memory properties and microstructure of Ni-rich NiTi-20Hf alloys. Smart Mater Struct. https://doi.org/10.1088/0964-1726/2510.1088/0964-1726/25 ↗
49. Evirgen A, Karaman I, Santamarta R, Pons J, Noebe RD (2015) Microstructural characterization and shape memory characteristics of the Ni50.3Ti34.7Hf15 shape memory alloy. Acta Mater 83:48–60. https://doi.org/10.1016/j.actamat.2014.09.02710.1016/j.actamat.2014.09.027 ↗
50. Stebner AP et al (2014) Transformation strains and temperatures of a nickel–titanium–hafnium high temperature shape memory alloy. Acta Mater 76:40–53. https://doi.org/10.1016/j.actamat.2014.04.07110.1016/j.actamat.2014.04.071 ↗
51. Karaca HE, Acar E, Tobe H, Saghaian SM (2014) NiTiHf-based shape memory alloys. Mater Sci Technol 30(13):1530–1544. https://doi.org/10.1179/1743284714y.000000059810.1179/1743284714y.0000000598 ↗
52. Karaca HE et al (2013) Effects of nanoprecipitation on the shape memory and material properties of an Ni-rich NiTiHf high temperature shape memory alloy. Acta Mater 61(19):7422–7431. https://doi.org/10.1016/j.actamat.2013.08.04810.1016/j.actamat.2013.08.048 ↗
53. Bigelow GS, Garg A, Padula Ii SA, Gaydosh DJ, Noebe RD (2011) Load-biased shape-memory and superelastic properties of a precipitation strengthened high-temperature Ni50.3Ti29.7Hf20 alloy. Scripta Mater 64(8):725–728. https://doi.org/10.1016/j.scriptamat.2010.12.02810.1016/j.scriptamat.2010.12.028 ↗
54. Sun K, Yi X, Sun B, Meng X, Gao Z, Cai W (2021) Study on microstructure and properties of TiBw/Ti-V-Al light weight high temperature shape memory composite. J Alloy Compd 851:156837. https://doi.org/10.1016/j.jallcom.2020.15683710.1016/j.jallcom.2020.156837 ↗
55. Ma J, Karaman I, Noebe RD (2010) High temperature shape memory alloys. Int Mater Rev 55(5):257–315. https://doi.org/10.1179/095066010x1264689872836310.1179/095066010x12646898728363 ↗
56. Dalle F, Perrin E, Vermaut P, Masse M, Portier R (2002) Interface mobility in Ni49.8Ti42.2Hf8 shape memory alloy. Acta Mater 50(14):3557–3565. https://doi.org/10.1016/s1359-6454(02)00151-910.1016/s1359-6454(02)00151-9 ↗
57. Potapov PL, Shelyakov AV, Gulyaev AA, Svistunov EL, Matveeva NM, Hodgson D (1997) Effect of Hf on the structure of Ni-Ti martensitic alloys. Mater Lett 32(4):247–250. https://doi.org/10.1016/s0167-577x(97)00037-210.1016/s0167-577x(97)00037-2 ↗
58. Saghaian SM, Karaca HE, Souri M, Turabi AS, Noebe RD (2016) Tensile shape memory behavior of Ni50.3Ti29.7Hf20 high temperature shape memory alloys. Mater Des 101:340–345. https://doi.org/10.1016/j.matdes.2016.03.16310.1016/j.matdes.2016.03.163 ↗
59. Coughlin DR (2013) Characterization of stoichiometric and aging effects on NiTiHf high temperature shape memory alloys. Ohio State University, Columbus
60. Atli IKKC, Noebe RD, Bigelow G, Gaydosh D (2015) Work production using the two-way shape memory effect in NiTi and a Ni-rich NiTiHf high-temperature shape memory alloy. Smart Mater Struct. https://doi.org/10.1088/0964-1726/2410.1088/0964-1726/24 ↗
61. Hite N et al (2021) NiTiHf shape memory alloys as phase change thermal storage materials. Acta Mater 218:117175. https://doi.org/10.1016/j.actamat.2021.11717510.1016/j.actamat.2021.117175 ↗
62. Goh GD et al (2021) Machine learning for 3D printed multi-materials tissue-mimicking anatomical models. Mater Des 211:110125. https://doi.org/10.1016/j.matdes.2021.11012510.1016/j.matdes.2021.110125 ↗
63. Tagiltsev AI et al (2021) The effect of stress-induced martensite aging in tension and compression on B2–B19′ martensitic transformation in Ni503Ti32.2Hf17.5 high-temperature shape memory alloy. Smart Mater Struct 30(2):025039. https://doi.org/10.1088/1361-665x/abdaa810.1088/1361-665x/abdaa8 ↗
64. Prasher M, Sen D, Tewari R, Krishna PSR, Babu PD, Krishnan M (2020) Effect of Hf solute addition on the phase transformation behavior and hardness of a Ni-rich NiTi alloy. Mater Chem Phys 247:122890. https://doi.org/10.1016/j.matchemphys.2020.12289010.1016/j.matchemphys.2020.122890 ↗
65. Gantz F, Ley NA, Young ML (2021) Effect of nickel content on processing of Ni-Rich NiTiHf High-temperature shape memory alloys. Shape Memory Super. https://doi.org/10.1007/s40830-021-00324-310.1007/s40830-021-00324-3 ↗
66. Saghaian SM et al (2017) High strength NiTiHf shape memory alloys with tailorable properties. Acta Mater 134:211–220. https://doi.org/10.1016/j.actamat.2017.05.06510.1016/j.actamat.2017.05.065 ↗