About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
| Presentation Title |
Quantification and Prediction of Defect-Induced Fracture Using Digital Twins |
| Author(s) |
Paul Chao, Chad Hovey, Brian Phung, Kyle Karlson, John Emery, Ashley Spear, Andrew Polonsky |
| On-Site Speaker (Planned) |
Paul Chao |
| Abstract Scope |
We leverage digital twin technology to facilitate real-time monitoring 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. This research specifically examines over two dozen tensile specimens made from 316L stainless steel, produced under varying additive manufacturing (AM) processing parameters. By employing x-ray computed tomography to quantify defects such as porosity before and after tensile testing, we predict failure locations using direct numerical simulations via Finite Element Modeling.
Our comparisons between this modeling technique and experimental data improves advancements in failure prediction models for AM parts. Additionally, we will present a suite of user-friendly tools and pipelines designed for high-throughput 3D analysis to assist in quantifying and modelling deformation. This work not only informs processing methods like additive manufacturing but also enhances our understanding of structure-property relationships. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, Modeling and Simulation |