Keara Frawley
Advisors: Rampi Ramprasad & Naresh Thadhani
will defend a doctoral thesis entitled
Design Of Materials Tolerant To Dynamic Tensile Spall Failure
On
Tuesday, November 26 at 1:00 p.m.
Virtually via MS Teams
Committee
Dr. Rampi Ramprasad – School of Materials Science and Engineering
Dr. Naresh Thadhani – School of Materials Science and Engineering
Dr. Chaitanya Deo – School of Mechanical Engineering
Dr. Chao Zhang – School of Computational Science and Engineering
Dr. Jennifer Jordan – UK Program Office Technical Deputy, Los Alamos National Laboratory
Abstract
Materials tolerant to dynamic tensile or “spall” failure are of interest for applications involving high-velocity impact and blast loading. Metals and polymers generally have favorable responses to such extreme conditions and are therefore useful materials in shock-absorbing applications, such as the automotive industry, body armor, or other protection and shielding devices. The complex stress states and high strain rates involved in events leading to spall failure are typically different from the conditions under which most material testing is conducted to determine mechanical properties. Hence, it has been difficult to predict how spall strength, i.e., resistance to dynamic tensile failure, relates with typical mechanical properties such as hardness, toughness, strength, and moduli. This work focuses on utilizing machine learning (ML) to determine the relationship between these key properties and spall strength, with the goal of developing predictive models and a better understanding of the spall response behavior of metals, alloys, and polymers.
Various methods were utilized to generate databases of spall strengths and key properties of metals and polymers through literature surveys and experiments. Sources included peer-reviewed journal articles and gas gun plate-on-plate impact experiments. Data analytic methods, such as the Pearson correlation and the coefficient of determination, were used to correlate the properties to spall strength.
The first main result of this work is a model that predicts the spall strength of metals and alloys. The study provides design guidelines for efficiently screening metals based on commonly available mechanical properties that most influence the spall strength values, minimizing reliance on intensive experimental procedures. Furthermore, the model has been extended to predict the spall strength values of a class of complex alloys for which there is limited data available: high entropy alloys (HEAs). The second main result is a database of the spall strengths of 22 unique polymers, either experimentally determined in this work or available in the literature. This database also includes the available mechanical and physical properties of the various polymers, and correlates those to predict the spall strength based on a physically-based energy balance model available in the literature. Additionally, an initial exploration using Molecular Dynamics (MD) simulations to model spall failure was conducted on a simple polymer, polyethylene, to evaluate how this computational approach might perform across a broader range of polymers. By learning more about the influence of material properties on the spall strengths of different classes of materials, we can better understand and predict the spall response of untested materials.