Abstract
This paper details the design process of a ternary NiTiHf shape memory alloy (SMA) with an austenite finish temperature (Af) beyond 400 °C. Specifically, available experimental data on the ternary NiTiHf SMA system was utilized to construct a database, which was employed to train and test a machine learning (ML) algorithm to predict the ideal NiTiHf SMA composition to exhibit an Af beyond 400 °C and a relatively smaller hysteresis. For this purpose, a multi-layer feedforward neural network (MLFFNN) model was proposed, trained, and tested. Consequently, the Ni49.7Ti26.6Hf23.7 and Ni50Ti27Hf23 alloys predicted by this ML algorithm were selected for validation experiments to assess the accuracy of the ML model’s predictions. As a result, the Ni49.7Ti26.6Hf23.7 alloy with an Af temperature of 403.5 °C and remarkable cyclic stability was established as a new NiTiHf SMA composition, which can be utilized in applications demanding reversible austenite-to-martensite phase transformation beyond 400 °C.
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