In Press

Investigation of Superelasticity of Iron based Shape Memory Alloy

Vol. 13, Issue 01, PP. 07-12, January 2026

Published online Investigation of Superelasticity of Iron based Shape Memory Alloy

Abstract

Iron-based shape memory alloys (Fe-SMAs) have recently attracted significant attention as cost-effective and mechanically robust alternatives to conventional Ni–Ti alloys for smart structural and engineering applications. This study investigates the superelastic behavior of an iron-based shape memory alloy through systematic mechanical characterization under controlled loading–unloading conditions. Uniaxial tensile tests were conducted to evaluate stress–strain response, reversible strain capacity, critical transformation stresses, and hysteresis behavior associated with stress-induced martensitic transformation and reverse transformation. The influence of cyclic loading on superelastic stability, energy dissipation, and residual strain accumulation was also examined. Microstructural observations were correlated with mechanical responses to elucidate the role of phase transformation mechanisms in governing superelastic performance. The results demonstrate that the investigated Fe-based alloy exhibits pronounced superelasticity with substantial recoverable strain and stable cyclic behavior, highlighting its potential for applications in vibration control, seismic damping, and adaptive structural components. The findings provide fundamental insight into the deformation and recovery mechanisms of Fe-based SMAs and contribute to the development of reliable, large-scale superelastic materials for civil and mechanical engineering systems.

Author

Hamza Kamran, Rana Atta ur Rahman, Ahmed Usman Yasir

Cite

Hamza Kamran, Rana Atta ur Rahman, Ahmed Usman Yasir “Investigation of Superelasticity of Iron based Shape Memory Alloy” International Journal of Engineering Works, Vol. 13, Issue 01, PP. 07-12, January 2026. https://doi.org/10.5281/zenodo.18242902.

References

[1] A. Sato, E. Chishima, K. Soma, and T. Mori, “Shape memory effect in γ to ε transformation in Fe–Mn–Si alloys,” Acta Metallurgica, vol. 30, no. 6, pp. 1177–1183, 1982.
[2] K. Otsuka and C. M. Wayman, Shape Memory Materials. Cambridge, U.K.: Cambridge University Press, 1998.
[3] D. C. Lagoudas, Shape Memory Alloys: Modeling and Engineering Applications. New York, NY, USA: Springer, 2008.
[4] S. Kajiwara, “Characteristic features of shape memory effect and related transformation behavior in Fe-based alloys,” Materials Science and Engineering A, vol. 273–275, pp. 67–88, 1999.
[5] T. Omori, Y. Iwaizako, R. Kainuma, and K. Ishida, “Superelasticity in polycrystalline ferrous alloys,” Science, vol. 333, no. 6043, pp. 68–71, 2011.
[6] Z. Dong, S. Kajiwara, T. Kikuchi, and T. Sawaguchi, “Martensitic transformation and superelastic behavior in Fe-based shape memory alloys,” Acta Materialia, vol. 58, no. 3, pp. 935–942, 2010.
[7] T. Sawaguchi, T. Maruyama, Y. Otsuka, and K. Tsuzaki, “Design concept and applications of Fe-based superelastic alloys,” Materials Science and Engineering A, vol. 585, pp. 1–11, 2014.
[8] M. Czaderski, M. Shahverdi, and M. Motavalli, “Iron-based shape memory alloys for prestressing and seismic applications,” Construction and Building Materials, vol. 56, pp. 94–101, 2014.

[9] M. Shahverdi, M. Czaderski, and M. Motavalli, “Superelastic behavior of Fe-based shape memory alloys for civil engineering applications,” Journal of Materials in Civil Engineering, vol. 32, no. 6, pp. 04020131, 2020.
[10] Y. Wang, H. Peng, X. Liu, and Y. Wen, “Compression superelasticity and energy dissipation capacity of Fe-Mn-Si-based shape memory alloys,” Materials & Design, vol. 197, pp. 109220, 2021.
[11] C. Leinenbach, H. Kramer, C. Bernhard, and D. Eifler, “Thermomechanical processing and functional properties of Fe-based shape memory alloys,” Materials Science and Engineering A, vol. 677, pp. 261–270, 2016.
[12] T. Vollmer, J. Frenzel, and G. Eggeler, “Cyclic stability and functional fatigue of Fe-based shape memory alloys,” Materials Science and Engineering A, vol. 786, pp. 139420, 2020.

Detetion and Prevention of Irrelevent Data Through Data Cleansing

Published online Detetion and Prevention of Irrelevent Data Through Data Cleansing

Abstract

A file oriented unstructured data collected and transformed into the data warehouse .Two or more records identified separately actually represent same real world entity, detection and prevention to improve data quality. The proposed technique introduces smart tokens of most representative attributes by sorting those tokens identical records are bring into close neighborhood, record duplicates are identified and removed from the data. Clean consistent and non duplicated data loaded into warehouse. The technique is a mile stone for cleaning data as with the explosive amount of data recording it is the need of time that more corrected data to be provided to the data mangers for effective decisions making.