NiTiHf high temperature shape memory alloy design with the assistance of machine learning

The shape memory alloys demonstrate a unique property that allows them to recover enormous shape changes above certain temperatures; therefore, they are a notable option as a compliant actuator. Increasing demand in the aerospace and oil industry for high-temperature actuators motivates the search for high-temperature shape memory alloys. High operating temperature range, medium-ductility, and remarkably lower cost of NiTiHf alloys distinguish them from various high-temperature shape memory alloy systems. However, as a ternary alloy system, NiTiHf alloys have vast search space to be analyzed, which requires significant investment. On the other hand, the implementation of machine learning in material science has been proven promising and affordable alternative for experimental search, which gave inspiration to current work: designing NiTiHf shape memory alloy that can exhibit phase transformation beyond 400 ℃ with the assistance of machine learning. In this work, a comprehensive dataset was established from the available literature on NiTiHf alloy for training by a multilayer feedforward neural network algorithm. Via the optimized neural network model, phase transformation temperatures of unexplored NiTiHf search space were estimated. Two novel compositions with desired functions, namely the Ni49.7Ti26.6Hf23.7, and the Ni50Ti27Hf23 alloys, were selected to validate machine learning predictions. The former demonstrated an Af temperature of 403.5 °C with high cyclic stability, which verifies the success of machine learning in new alloy design.