New shape-memory alloy: Research & Innovation

Funded by the National Science Foundation’s Designing Materials to Revolutionize and Engineer Our Future program, researchers from the department of materials science and engineering at Texas A&M University used an artificial intelligence materials selection framework to discover a new shape-memory alloy. It showed the highest operational efficiency thus far for nickel-titanium-base materials. In addition, the data-driven framework offers proof of concept for future materials development.

Shape-memory alloys are utilized in various fields when compact, lightweight and solid-state actuations are needed, replacing hydraulic or pneumatic actuators because the alloys can deform when cold and then return to their original shapes when heated. This unique property is critical for applications, such as airplane wings, jet engines and automotive components, that must withstand repeated, recoverable large-shape changes.

Doctoral student William Trehern operates a vacuum arc melter, a synthesis method commonly used to create high-purity alloys of various compositions. Image courtesy of Texas A&M University

There have been advancements in shape-memory alloys since their beginnings in the mid-1960s but at a cost. The understanding and discovery of new shape-memory alloys has required extensive research through experimentation and ad hoc trial and error. About every decade, a significant shape-memory alloy composition or system has been discovered. But even with advances, the alloys are hindered by their low energy efficiency caused by incompatibilities in their microstructures during large-shape changes. They also are notoriously difficult to design from scratch.

To address these shortcomings, Texas A&M researchers combined experimental data to create an artificial intelligence materials selection computational framework capable of determining optimal material compositions and processing them, which led to the discovery of a new shape-memory alloy composition.