DMREF: Accelerating the Development of High Temperature Shape Memory Alloys
- View All
High Temperature Shape Memory Alloys (HTSMAs) are alloys that exhibit large shape changes at high stresses and high temperatures. If the shape change behavior had be controlled and tailored, HTSMAs can be used as robust and compact solid-state actuators with performance exceeding any other current technology. Since the behavior of HTSMAs is highly dependent on chemistry and processing, tailoring of HTSMAs for specific applications using solely experimental means is unrealistic. This award supports the development of a framework that can allow for the design of chemistry and processing steps to achieve a given performance requirement in these materials. The immediate technological impact of the work is the accelerated development of high-temperature solid-state actuators for the aerospace and automotive industries. Furthermore, the award will expose seven graduate and two to four undergraduate students to a highly interdisciplinary research project, combining ideas from materials science, mechanics, computer science, machine learning and design. The work supports efforts related to the Materials Genome Initiative by integrating experimental and computational research, making digital data accessible, and training the future workforce.The current investigators and their collaborators have recently discovered that nano-recipitation in NiTiHf HTSMAs leads to unprecedented cyclic stability with reversible phase transformation under significant stresses at elevated temperatures. To accelerate their development, this research team will develop a framework to prescribe the necessary initial composition and subsequent processing schedule of a NiTiHf HTSMA based on arbitrary performance requirements: A two-level physically rigorous modeling approach links chemistry and processing to performance. The first modeling level connects chemistry and processing through a precipitation model, while the second level connects microstructure to shape memory response through a thermodynamics-based micromechanics formulation. Within a Bayesian framework, models are initially calibrated using prior knowledge about the likely value of their parameters. Calibrated models are in turn used to design optimal experiments, that maximize the utility of experiments in terms of information gain or desired materials response, that then lead to enhanced model refinement and predictability. Models are in turn used to optimize shape memory response by prescribing feasible composition plus processing sets taking into account uncertainty in model parameters and heterogeneities in microstructure. The overall framework will be disseminated through conventional channels, while the models, model parameters and data generated through this research will be made available to the wider scientific community through an instance of the Materials Data Curation System developed by the National Institute of Standards and Technology.