Predicting How New Materials Behave

In severe operating environments encountered in Naval ship, aircraft, and hypersonic systems, new materials are essential for advances in reliability, performance, and efficiency. However, a grand challenge in implementing new materials into advanced engineering systems is the ability to predict properties of these materials with statistically significant confidence, along varying processing paths and without costly and time-consuming experimental testing. In the design process, it’s particularly important to know the lower bound values, often designated as design minimum values for a broad suite of properties. Determining these values to better predict material behavior would allow a substantial reduction in the time and cost of introducing new materials.

The overarching goal of Matthew P. Miller and Paul R. Dawson, Mechanical and Aerospace Engineering, along with collaborators at University of California, Santa Barbara and Ohio State University, is to build an integrated infrastructure for prediction of monotonic and cyclic plastic properties of polycrystalline metallic materials. The basic research challenge is to capture the three-dimensional heterogeneity of material structure and to quantify its influence on stress distributions that develop during deformation and their resultant influence on property variability. The team hypothesizes that characterizing and understanding coincident distributions of stress and structure are required to capture the true statistical character of the mechanical behavior. Researchers’ initial inquiries are using titanium (Ti) alloys as model materials, focusing on strength, ductility and fatigue. The project’s research groups have complementary experimental, theoretical, and modeling expertise, and extensive experience with Ti alloys in engineering environments. 

Cornell Researchers

Funding Received

$615 Thousand spanning 3 years

Sponsored by