Advancing Nitinol Implant Design and Simulation Through Data-Driven Methodologies

Recent advances in the Data Science methods for acquiring and analyzing large amounts of materials deformation data have the potential to tremendously benefit Nitinol (Nickel-Titanium shape memory alloy) implant design and simulation. We review some of these data-driven methodologies and provide a perspective on adapting these techniques to Nitinol design and simulation. We organize the review in a three-tiered approach. The methods in the first tier relate to data acquisition. We review methods for acquiring full-field deformation data from implants and methods for quantifying uncertainty in such data. The second-tier methods relate to combining data from multiple sources to gain a holistic understanding of complex deformation phenomena such as fatigue. Methods in the third tier relate to making data-driven simulation of the deformation response of Nitinol. A wide adaption of these methods by the Nitinol cardiovascular implant community may be facilitated by building consensus on best practices and open exchange of computational tools.

Automated summary

Recent advances in data science methods for acquiring and analyzing large amounts of material deformation data have the potential to greatly benefit the design and simulation of Nitinol implants. This review explores various data-driven methodologies and proposes their adaptation to Nitinol design and simulation. The review is organized into three tiers, covering methods for data acquisition, combining data from multiple sources, and data-driven simulation of Nitinol deformation. Widespread adoption of these methods by the Nitinol cardiovascular implant community can be facilitated through consensus building and open exchange of computational tools.