International Journal of Engineering Works

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Investigation of Superelasticity of Iron based Shape Memory Alloy

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

Published online https://doi.org/10.5281/zenodo.18242902

ePub PDF

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

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Detetion and Prevention of Irrelevent Data Through Data Cleansing

Published online https://doi.org/

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.

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Current Issue - 2026

Neural Network based Portable Spectrometer for Fluid Classification

Vol. 13, Issue 01, PP. 01-06, January 26

Published online https://doi.org/10.5281/zenodo.18183364

ePub PDF

Abstract

The spectrometer, a powerful analytical instrument, plays a vital role in the fields of Chemistry, Material Sciences, Biochemistry, and Physics by allowing researchers to test, discover, and measure the spectral content of various fluids. This paper details the development of a portable, lightweight, cost-effective spectrometer device, which incorporates artificial intelligence for fluid classification. In a practical application, milk is used as the test fluid, with different qualities achieved by introducing controlled impurities. The spectrometer collected spectral data using a camera sensor, and the dataset is subsequently randomized and divided into training and testing data. Employing a range of machine learning algorithms and neural networks, the device accurately predicts the class of the fluid. The integration of optical components and microcontrollers facilitated model deployment. Notably, this device provides real-time fluid classification and displays results on an Organic LED. Beyond milk, its versatility allows for quality analysis of various fluids containing impurities, such as gasoline, human blood, and saliva. Remarkably compact at 70 x 70 x 50 mm and lightweight at 0.2 kilograms, the device had achieved an impressive average accuracy of 93.45 percent. It stands out for its ease of use, recharge ability, accuracy, and cost effectiveness compared to traditional spectrophotometers, positioning it as a valuable tool in the realm of scientific research and quality assessment.

Author

Hassan Nawazish Rasool, Muhammad Zeb

Cite

Hassan Nawazish Rasool, Muhammad Zeb “Neural Network based Portable Spectrometer for Fluid Classification” Internationa, Vol. 13, Issue 01, PP. 01-06, January 2026. https://doi.org/10.5281/zenodo.18183364.,

References

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[3] P. Carpentier, A. Royant, J. Ohana, and D. Bourgeois, “Advances in spectroscopic methods for biological crystals. 2. Raman spectroscopy,” J. Appl. Crystallogr., vol. 40, no. 6, pp. 1113–1122, 2007, doi: 10.1107/S0021889807044202.

[4] R. A. Ahmadi, F. Hasanvand, G. Bruno, H. A. Rudbari, and S. Amani, “Synthesis, spectroscopy, and magnetic characterization of copper(II) and cobalt(II) complexes with 2-amino-5-bromopyridine as ligand,” Russ. J. Coord. Chem. Khimiya, vol. 39, no. 12, pp. 867–871, 2013, doi: 10.1134/S1070328413110018.

[5] K. Laganovska et al., “Portable low-cost open-source wireless spectrophotometer for fast and reliable measurements,” HardwareX, vol. 7, p. e00108, 2020, doi: 10.1016/j.ohx.2020.e00108.

[6] J. Riu, G. Gorla, D. Chakif, R. Boqué, and B. Giussani, “Rapid analysis of milk using low-cost pocket-size NIR spectrometers and multivariate analysis,” Foods, vol. 9, no. 8, 2020, doi: 10.3390/foods9081090.

[7] J. A. Diaz-Olivares, I. Adriaens, E. Stevens, W. Saeys, and B. Aernouts, “Online milk composition analysis with an on-farm near-infrared sensor,” Comput. Electron. Agric., vol. 178, no. September, p. 105734, 2020, doi: 10.1016/j.compag.2020.105734.

[8] L. L. Monteiro, P. Zoio, B. B. Carvalho, L. P. Fonseca, and C. R. C. Calado, “Quality Monitoring of Biodiesel and Diesel/Biodiesel Blends: A Comparison between Benchtop FT-NIR versus a Portable Miniaturized NIR Spectroscopic Analysis,” Processes, vol. 11, no. 4, 2023, doi: 10.3390/pr11041071.

[9] R. M. Correia et al., “Portable near infrared spectroscopy applied to fuel quality control,” Talanta, vol. 176, no. August 2017, pp. 26–33, 2018, doi: 10.1016/j.talanta.2017.07.094.

F. D. Santos et al., “Discrimination of oils and fuels using a portable NIR spectrometer,” Fuel, vol. 283, no. August 2020, p. 118854, 2021, doi: 10.1016/j.fuel.2020.118854.

view all

Neural Network based Portable Spectrometer for Fluid Classification

Vol. 13, Issue 01, PP. 01-06, January 26

Published online https://doi.org/10.5281/zenodo.18183364

ePub PDF

Abstract

Author

Hassan Nawazish Rasool, Muhammad Zeb

Cite

References

[5] K. Laganovska et al., “Portable low-cost open-source wireless spectrophotometer for fast and reliable measurements,” HardwareX, vol. 7, p. e00108, 2020, doi: 10.1016/j.ohx.2020.e00108.

[9] R. M. Correia et al., “Portable near infrared spectroscopy applied to fuel quality control,” Talanta, vol. 176, no. August 2017, pp. 26–33, 2018, doi: 10.1016/j.talanta.2017.07.094.

F. D. Santos et al., “Discrimination of oils and fuels using a portable NIR spectrometer,” Fuel, vol. 283, no. August 2020, p. 118854, 2021, doi: 10.1016/j.fuel.2020.118854.