Abstract
The present study focused on estimating the complex nonlinear relationship between the composition and phase transformation temperatures of Ti–Ni–Pd shape memory alloys by artificial neural networks (ANN). The ANN models were developed by using the experimental data of Ti–Ni–Pd alloys. It was found that the predictions are in good agreement with the trained and unseen test data of existing alloys. The developed model was able to simulate new virtual alloys to quantitatively estimate the effect of Ti, Ni, and Pd on transformation temperatures. The transformation temperature behavior of these virtual alloys is validated by conducting new experiments on the Ti–rich thin film that was deposited using multi target sputtering equipment. The transformation behavior of the film was measured by varying the composition with the help of aging treatment. The predicted trend of transformational temperatures was explained with the help of experimental results.
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Acknowledgements
This study was supported by grants from the Industrial Strategic Technology Development Program (10042703) funded by the Ministry of Knowledge Economy (MKE) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03932734), Republic of Korea. This study was also partially supported by an internal fund from the Korea Institute of Materials Science (KIMS).
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Narayana, P.L., Kim, SW., Hong, JK. et al. Estimation of Transformation Temperatures in Ti–Ni–Pd Shape Memory Alloys. Met. Mater. Int. 24, 919–925 (2018). https://doi.org/10.1007/s12540-018-0109-4
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DOI: https://doi.org/10.1007/s12540-018-0109-4