Session: 13-06-01: Multiscale Models and Experimental Techniques for Composite Materials and Structures I

Paper Number: 171506

Multiscale Modeling and Validation of Tib and Ti-Tib Composites: From Atomistic Potentials to Macroscale Behavior 

Titanium (Ti) and its alloys are widely utilized in advanced engineering applications due to their exceptional specific strength, corrosion resistance, and biocompatibility. Among potential reinforcement candidates, titanium boride (TiB) whiskers or needles offer superior strength and stiffness, as well as excellent compatibility with the Ti matrix. Due to its monocrystalline molecular structure and minimal lattice mismatch with Ti, TiB forms clean, semi-coherent, and well-bonded interfaces at the nanoscale, making it ideal for reinforcing Ti-based composites. However, TiB does not naturally occur in pure form, and its intrinsic properties are difficult to characterize experimentally. As a result, our current understanding of TiB mechanics relies heavily on indirect inference from Ti-TiB composites through experiments or limited-scale computational studies.

To address these gaps, this research develops a comprehensive multiscale computational framework to study the mechanics of Ti-TiB composites. The study begins by developing a reliable interatomic potential for the Ti-B system based on Density Functional Theory (DFT) calculations, enabling molecular dynamics (MD) simulations of TiB. This step overcomes a critical barrier in the field, as MD simulations have so far been infeasible due to the absence of a suitable Ti-B potential. With this potential, we systematically investigate TiB’s intrinsic mechanical properties under a variety of conditions, including the presence of atomic defects and temperature effects. These simulations provide new insights into TiB behavior at the nanoscale that have not been captured in prior studies.

Building on the MD results, the study advances to the microscale and macroscale through peridynamics modeling. Material properties of TiB derived from MD simulations are incorporated into peridynamics to model the behavior of Ti-TiB composites at larger length scales. This multiscale integration allows for the prediction of composite-level mechanical properties based on nanoscale phenomena, establishing a crucial bridge across length scales. To validate the computational framework, simulated results for TiB whiskers and Ti-TiB composites are compared with existing experimental measurements. The results demonstrate consistency with experimental observations of mechanical behavior, supporting the validity and robustness of the proposed methodology. This integrated approach not only improves our understanding of TiB’s intrinsic behavior but also offers a predictive tool for designing Ti-TiB composites with tailored properties.

To the best of our knowledge, this is the first numerical study to systematically examine TiB mechanics and Ti-TiB composite performance using a multiscale modeling strategy. Several important research gaps are addressed in this work. First, we provide the first comprehensive MD simulation results for TiB whiskers, explicitly considering vacancy defects and thermal effects. Second, we establish a novel and practical multiscale pipeline that connects intrinsic nanoscale material behavior with microscale and macroscale composite performance using peridynamics. Third, our work offers an efficient and cost-effective alternative to purely experimental approaches, which are limited by high costs, difficulties in isolating TiB whiskers for measurement, and variability introduced by fabrication processes.

Presenting Author: Shaoping Xiao Unversity of Iowa

Presenting Author Biography: Dr. Shaoping Xiao is a professor in the Department of Mechanical Engineering at The University of Iowa. He graduated from Northwestern University with a Ph.D. degree in mechanical engineering before joining the University of Iowa in 2003. His original expertise lies in computational nanomechanics and materials science, especially multiscale modeling and simulations. In the past several years, he has extended his efforts to artificial intelligence (AI) and its applications in science and engineering problem-solving. His group's current research interests include machine-learning enhanced multiscale modeling of composite materials, reinforcement learning and linear temporal logics for robotics and control, AI-powered design of distributed reservoir systems for flood mitigation, agriculture management subject to climate change and farmers' trust, and quantum computing.

Authors:

Shaoping Xiao Unversity of Iowa