MSE 2022
Lecture
28.09.2022
CalPhaD-assissted Evaluation of Martensite Formation in Fe-based Shape Memory Alloys
MK

Dr.-Ing. Mario J. Kriegel

Kriegel, M.J. (Speaker)¹; Leineweber, A.²; Walnsch, A.²
¹ ; ²TU Bergakademie Freiberg
Vorschau
20 Min. Untertitel (CC)

Shape memory alloys (SMAs) based on the Fe–Mn–Al–Ni system provide promising advantages over conventional NiTi-based alloys in terms of materials costs and cold workability. To understand the processes involved in the martensitic transformation of these alloys and to improve their properties, thermodynamic calculations were applied, considering the energetics of the transforming phases (A2 + B2 ⇌ A1 + L10). The thermoelastic martensitic transformation was studied on homogenized and quenched samples (1250°C / 8 h / ice water quenching) using dilatometry measurements between -80°C and 100°C. Complementary investigation methods e.g. SEM, EPMA and TEM were applied to correlate the experimentally obtained phase equilibria and transformation temperatures (MS) with thermodynamic calculations based on an assessed quaternary Al–Fe–Mn–Ni-CalPhaD (Calculation of Phase Diagrams) database [2].
The energetic contributions of the A2 and B2 phases (austenitic state) to initiate the martensitic transformation to A1 and L10 at the MS temperature were determined by equating the Gibbs energies for the formation of martensite in both phases. With the aid of extrapolated T0 temperatures for the B2 phase and energetic considerations (EAM potentials) of the differences between B2 and L10, both adopted from literature data, the thermodynamic stability of the B2 phase was approximated. The energetic contributions of the A2 and B2 particles to the overall martensitic transformation can now be calculated with the above mentioned database. Variations of the chemical alloy composition will lead to different T0 temperatures and to changes in the chemical compositions of the transforming phases that tremendously influences the pseudoelastic properties of the material. Thus, thermodynamic calculations can be applied to improve the performance of SMAs in a target-oriented manner.

REFERENCES
1.    A. Walnsch, M. J. Kriegel, M. Motylenko, G. Korpala, U. Prahl, A. Leineweber, Scr. Mater., 192 (2021), 26–31.
2.    A. Walnsch, M. J. Kriegel, O. Fabrichnaya, A. Leineweber, Calphad, 66 (2019), 101621.

Abstract

Abstract

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