| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
| Presentation Title |
Enhanced Predictive Modelling of Laser Weld Failure Using 3D Characterization of 304L |
| Author(s) |
Andrew Polonsky, Mary Arnhart, Alyssa Skulborstad, Helena Jin, Kyle Karlson, Jonathan Madison |
| On-Site Speaker (Planned) |
Andrew Polonsky |
| Abstract Scope |
The mechanical response of laser welds in complex load states can be highly variable, underlying the need for models that can accurately predict mechanical behavior to ensure component performance. Here we present a finite element modelling framework designed to capture this variability through the incorporation of 3D micro-computed tomography (μCT) characterization of partial penetration butt welds of 304L under a variety of weld conditions. Idealized models utilizing homogenization approaches for local material properties fail to accurately predict mechanical response trends, revealing the need to include more detailed weldment geometry obtained via μCT. The improved model demonstrates the sensitivity of mechanical response to weld geometry, as well as the role of porosity in controlling weld failure. The tradeoffs between computational complexity and predictive capability for modelling failure of highly ductile materials will also be discussed. |
| Proceedings Inclusion? |
Planned: |