| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
L-27: Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-informed Data-driven Modeling with Experimental Validation |
| Author(s) |
Lei Chen, Zhuo Wang, Zhen Hu, Sankaran Mahadevan |
| On-Site Speaker (Planned) |
Lei Chen |
| Abstract Scope |
The complicated metal-based additive manufacturing (AM) process involves various uncertainty sources, leading to variability in AM products. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework with experimental validation, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by experimentally validated multi-scale multi-physics AM models. It starts with computationally inexpensive surrogate models for which uncertainty can be readily quantified, followed by a global sensitivity analysis for a comprehensive UQ study. Using AM fabricated Ti-6Al-4V components as examples, this study demonstrates the capability of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations and data-driven surrogate modeling. The model correction and parameter calibration for the constructed surrogate models using limited amount of experimental data is discussed. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |