journal article·2025-09

Phenomenological modeling for ferromagnetic shape memory alloys with lumped-element formulation and artificial neural network

Yuxiang Han, XueRong Hu, JunYan Lu, Linxiang Wang, Roderick Melnik

Applied Physics A· Volume 131· Issue 9
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Abstract

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Research context

OpenAlex topics: Shape Memory Alloy Transformations · Magnetic and transport properties of perovskites and related materials

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References (36)

Show source-provided references
1. K. Ullakko, Magnetically controlled shape memory alloys: a new class of actuator materials. J. Mater. Eng. Perform. 5(3), 405–409 (1996)10.1007/BF02649344 ↗
2. K. Ullakko et al., Large magnetic-field-induced strains in Ni2MnGa single crystals. Appl. Phys. Lett. 69(13), 1966–1968 (1996)10.1063/1.117637 ↗
3. A. Likhachev, K. Ullakko, Magnetic-field-controlled twin boundaries motion and giant magneto-mechanical effects in Ni–Mn–Ga shape memory alloy. Phys. Lett. A 275(1–2), 142–151 (2000)10.1016/S0375-9601(00)00561-2 ↗
4. S.J. Murray et al., 6% magnetic-field-induced strain by twin-boundary motion in ferromagnetic Ni–Mn–Ga. Appl. Phys. Lett. 77(6), 886–888 (2000)10.1063/1.1306635 ↗
5. I. Karaman et al., Energy harvesting using martensite variant reorientation mechanism in a NiMnGa magnetic shape memory alloy. Appl. Phys. Lett. 90(17), 172505 (2007)10.1063/1.2721143 ↗
6. A. Saren et al., Modeling and design of a vibration energy harvester using the magnetic shape memory effect. Smart Mater. Struct. 24(9), 095002 (2015)10.1088/0964-1726/24/9/095002 ↗
7. M.A.A. Farsangi et al., Energy harvesting from structural vibrations of magnetic shape memory alloys. Appl. Phys. Lett. (2017). https://doi.org/10.1063/1.497825810.1063/1.4978258 ↗
8. Y. Jing, W. Luping, X. Jin, Design and implementation of vibration energy harvester based on MSMA cantilever beam. Trans. Electr. Electron. Mater. 21(4), 399–405 (2020)10.1007/s42341-020-00192-1 ↗
9. M. Veligatla et al., Energy conversion in Ni-Mn-Ga with asymmetrical bias magnetic field. J. Magn. Magn. Mater. 551, 169183 (2022)10.1016/j.jmmm.2022.169183 ↗
10. L. Riccardi et al., A precise positioning actuator based on feedback-controlled magnetic shape memory alloys. Mechatronics. 22(5), 568–576 (2012)10.1016/j.mechatronics.2011.12.004 ↗
11. H. Shi et al., Design and performance analysis of magnetic shape memory alloy actuator with a compact electromagnetic coil configuration. IEEE Trans. Magn. 56(8), 1–13 (2020)10.1109/TMAG.2020.3000039 ↗
12. Y. Yu, C. Zhang, M. Zhou, NARMAX Model-Based hysteresis modeling of magnetic shape memory alloy actuators. IEEE Trans. Nanotechnol. 19, 1–4 (2020)10.1109/TNANO.2019.2953933 ↗
13. J. Jugo, J. Feuchtwanger, J. Corres, Numerical optimization based control design for a ferromagnetic shape memory alloy actuator. Sens. Actuators A: Phys. 331, 112835 (2021)10.1016/j.sna.2021.112835 ↗
14. B. Minorowicz, A. Milecki, Design and control of magnetic shape memory alloy actuators. Materials. 15(13), 4400 (2022)10.3390/ma15134400 ↗
15. J.X. Zhang, L.Q. Chen, Phase-field model for ferromagnetic shape-memory alloys. Philos. Mag. Lett. 85(10), 533–541 (2005)10.1080/09500830500385527 ↗
16. L.J. Li et al., Magnetoelastic domains and magnetic field-induced strains in ferromagnetic shape memory alloys by phase-field simulation. Appl. Phys. Lett. (2008). https://doi.org/10.1063/1.291812710.1063/1.2918127 ↗
17. Q. Peng, Y.J. He, Z. Moumni, A phase-field model on the hysteretic magneto-mechanical behaviors of ferromagnetic shape memory alloy. Acta Mater. 88, 13–24 (2015)10.1016/j.actamat.2015.01.044 ↗
18. H.H. Wu et al., Real-space phase field investigation of evolving magnetic domains and twin structures in a ferromagnetic shape memory alloy. J. Appl. Phys. (2016). https://doi.org/10.1063/1.496753110.1063/1.4967531 ↗
19. X. Xie, A non-isothermal phase field-finite element study of magnetic-induced deformation for ferromagnetic shape memory alloy. J. Mater. Sci. 57(44), 20746–20763 (2022)10.1007/s10853-022-07890-6 ↗
20. B. Kiefer, D.C. Lagoudas, Magnetic field-induced martensitic variant reorientation in magnetic shape memory alloys. Philosophical Magazine 85(33–35), 4289–4329 (2005)10.1080/14786430500363858 ↗
21. A.B. Waldauer et al., Improved thermodynamic model for magnetic shape memory alloys. Smart Mater. Struct. 21(9), 094015 (2012)10.1088/0964-1726/21/9/094015 ↗
22. H. Sayyaadi, M.A.A. Farsangi, Frequency-dependent energy harvesting via magnetic shape memory alloys. Smart Mater. Struct. 24(11), 115022 (2015)10.1088/0964-1726/24/11/115022 ↗
23. M. Shirani, M. Kadkhodaei, A geometrical approach to determine reorientation start and continuation conditions in ferromagnetic shape memory alloys considering the effects of loading history. Smart Mater. Struct. 23(12), 125008 (2014)10.1088/0964-1726/23/12/125008 ↗
24. F. Ehle et al., A thermodynamically consistent lumped-element model for magnetic shape memory components. J. Intell. Mater. Syst. Struct. 34(12), 1389–1405 (2023)10.1177/1045389X221136299 ↗
25. X. Chen et al., A three-dimensional model of magneto-mechanical behaviors of martensite reorientation in ferromagnetic shape memory alloys. J. Mech. Phys. Solids 64, 249–286 (2014)10.1016/j.jmps.2013.11.005 ↗
26. D.H. LaMaster et al., A full 3D thermodynamic-based model for magnetic shape memory alloys. J. Intell. Mater. Syst. Struct. 26(6), 663–679 (2015)10.1177/1045389X14546655 ↗
27. H.P. Feigenbaum et al., Experimental characterization and modeling of a three-variant magnetic shape memory alloy. Smart Mater. Struct. 25(10), 104004 (2016)10.1088/0964-1726/25/10/104004 ↗
28. M.R. Mousavi, J. Arghavani, A three-dimensional constitutive model for magnetic shape memory alloys under magneto-mechanical loadings. Smart Mater. Struct. 26(1), 015014 (2017)10.1088/1361-665X/26/1/015014 ↗
29. C. Zhang et al., Takagi–Sugeno fuzzy neural network hysteresis modeling for magnetic shape memory alloy actuator based on modified bacteria foraging algorithm. Int. J. Fuzzy Syst. 22(4), 1314–1329 (2020)10.1007/s40815-020-00826-9 ↗
30. Y. Yu et al., Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties. Mech. Syst. Signal Process. 187, 109950 (2023)10.1016/j.ymssp.2022.109950 ↗
31. A. Aharoni, Demagnetizing factors for rectangular ferromagnetic prisms. J. Appl. Phys. 83(6), 3432–3434 (1998)10.1063/1.367113 ↗
32. A.A. Karakalas et al., Modeling of partial transformation cycles of SMAs with a modified hardening function. Smart Mater. Struct. 28(3), 035014 (2019)10.1088/1361-665X/aafcd9 ↗
33. G. Rizzello et al., An accurate dynamic model for polycrystalline shape memory alloy wire actuators and sensors. Smart Mater. Struct. 28(2), 025020 (2019)10.1088/1361-665X/aae3b8 ↗
34. S. Zhang et al., Thermal effects on high-frequency magnetic-field-induced martensite reorientation in ferromagnetic shape memory alloys: an experimental and theoretical investigation. Int. J. Plast. 108, 1–20 (2018)10.1016/j.ijplas.2018.04.008 ↗
35. O. Heczko, Magnetic shape memory effect and magnetization reversal. J. Magn. Magn. Mater. 290–291, 787–794 (2005)10.1016/j.jmmm.2004.11.397 ↗
36. S. Rashidi et al., Potentials of magnetic shape memory alloys for energy harvesting. J. Magn. Magn. Mater. 537, 168112 (2021)10.1016/j.jmmm.2021.168112 ↗