About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-Dimensional Datasets
|
| Presentation Title |
3D data pipelines and workflows to mesh experimental and computational results |
| Author(s) |
Paul Chao, Chad Hovey, Brian Phung, Ashley Spear, Kyle Karlson, John Emery, Andrew Polonsky |
| On-Site Speaker (Planned) |
Paul Chao |
| Abstract Scope |
We leverage digital twin technology—a virtual representation of physical objects—to facilitate real-time monitoring, analysis, and optimization of components throughout their lifecycle. We conduct a series of experiments aimed at enhancing failure prediction in 3D-printed components, particularly focusing on the challenges of structural integrity and reliability. Specifically, we examine two dozen tensile specimens made from 316L stainless steel, produced under varying additive manufacturing (AM) processing parameters. By employing X-ray computed tomography (CT) to quantify porosity before and after tensile testing, we predict failure locations using direct numerical simulations (DNS) via finite element modeling (FEM).
Our findings compare this modeling technique with experimental data, paving the way for advancements in failure prediction models for AM parts. Additionally, we present a suite of user-friendly tools and pipelines designed for high-throughput 3D analysis. This work not only informs processing methods such as additive manufacturing but also enhances our understanding of structure-property relationships. |