Do Shape-Memory Alloys Remember Past Strains in Their Life?
Endowed with the power of memory, certain alloys can magically return to their original shape when heated or deformed. However, the repeated back-and-forth between the original and new configuration may leave permanent imprints on the alloy's microscopic features, which could then impact its ability to reversibly transform shape. Thus, unraveling the impact of the strain history on these alloys' functionality is essential to improving predictive capabilities, but it has not received enough attention.
To fill this knowledge gap, the National Science Foundation has awarded a multiyear grant to Texas A&M University researchers Drs. Jean-Briac le Graverend, associate professor in the Department of Aerospace Engineering; Shuiwang Ji, professor in the Department of Computer Science & Engineering; and Daniel Bernard Hajovsky, associate professor in the Department of Educational Psychology. The investigators will also direct a portion of the NSF grant toward STEM outreach and education for neurodivergent middle and high schoolers.
"We are delighted to have received this grant, which not only lets us investigate important theoretical and scientific questions related to shape-memory alloys but also connect with kids in the community who may not have interacted with engineers or visited a lab," said le Graverend. "We look forward to learning from them, too, since they might view engineering problems in untraditional ways."
With critical applications spanning heart stents to airplane wing flaps, shape memory alloys have captured the imagination of researchers and engineers alike. The mechanism underlying shape memory is a phenomenon known as solid-state phase transformation. Simply put, this is when a material changes its internal structure from martensite to austenite, and vice versa, because of a change in temperature or stress.
Studies show that these martensite-austenite transformations are only sometimes perfectly reversible, especially if permanent deformations occur. Thus, the investigators will use a synergistic experimental and numerical approach combining sophisticated experiments, crystal-plasticity modeling, and a machine learning technique called graph neural network to understand and predict history effects in shape-memory alloys.
"Deformation history will make identical samples react differently to the thermo-mechanical loading applied," said le Graverend. "Our research into improving predictions of history effects on solid-state transformation and shape-memory alloys, in particular, will directly affect cost savings and life-cycle management of any materials and structures depending on solid-state transformations."
As part of the NSF grant, the team will also open the doors of the engineering departments to neurodivergent children, specifically those with learning disabilities. The team hopes to spark these students' interest in cutting-edge engineering research.
"In 8th grade, 79% of the students with a learning disability are below basic in mathematics. One thing that tells us is that these children and adolescents are at higher risk for academic failure, school dropout and setbacks," said Hajovsky. "One of the goals is to help promote interest and skills in STEM fields by targeting this specific population, bringing them to campus, and working with them to familiarize themselves with STEM fields and bring new ways of thinking to the engineering fields."
More information:
www.nsf.gov/awardsearch/showAw … storicalAwards=false
Provided by Texas A&M University College of Engineering