| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
An Efficient Machine Learning Enhanced Image-Based Framework for Micromechanical Analysis of Additively Manufactured Ti-6Al-4V |
| Author(s) |
Lucas Prata Ferreira, Nolan Strauss, Brayan Murgas, Steven Storck, Somnath Ghosh |
| On-Site Speaker (Planned) |
Lucas Prata Ferreira |
| Abstract Scope |
The increase in high-performance industrial applications of additively manufactured (AM) Ti-6Al-4V necessitates the development of robust physics-based computational models that relate the microstructural characteristics and defect state to the overall material response. With this motivation, the present paper develops a novel image-based crystal plasticity finite element model (CPFEM) for efficient micromechanical simulation of AM Ti-6Al-4V. The Widmanstätten microstructure of the alloy is characterized by 12 HCP α lath variants, whose statistics of size, shape, orientation, and crystallography are parametrically represented in the parent BCC β grain polycrystalline ensembles. Defects in the form of small voids in the microstructure are manifested as porosity volume fraction distribution in the crystal plasticity model, while larger voids are represented explicitly in the statistically equivalent microstructural volume element (SEMVE) model. The source image data used come from Electron Back-Scatter Diffraction (EBSD) and micro-focus X-ray Computational Tomography (XCT) scans. |