About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing of Thick Films Using Dry Aerosol Processes: Process Development, Materials, Process Optimization and Applications
|
| Presentation Title |
A Deep Learning-Based Molecular Dynamics Study of the Effect of an Oxide Layer on the Deposition of Tantalum Particles |
| Author(s) |
Stephen G. Bierschenk, Michael F. Becker, Desiderio Kovar |
| On-Site Speaker (Planned) |
Desiderio Kovar |
| Abstract Scope |
The effect of the native oxide layer on the deformation of tantalum powders deposited via kinetic spray processes is not fully understood. This work compares the deformation behavior of tantalum particles with and without an oxide layer using single-particle impact simulations performed via molecular dynamics simulations using a deep learning interatomic potential representation of the Ta/Ta2O5 system. The deep learning-based potential to reproduces the impact behavior for this complex material system at relatively low computational costs, which allow for Ta/Ta2O5 particles comparable in size to those deposited experimentally (50 nm) to be studied across a wide range of impact velocities (250-750 m/s). These simulations reveal that the presence of the 3 nm thick oxide layer reduces overall deformation by >40%. For particles 40-50 nm in diameter impacted at high velocity, the rupture of the oxide layer allows comparable kinetic energy dissipation compared to particles without an oxide layer. |