Abstract
Advancements in manufacturing technologies have enabled material system design optimization across multiple length scales. However, microstructural anomalies (defects) that are present at different scales have not been considered comprehensively enough for systems to be robust to manufacturing variations and uncertainties. Addressing these anomalies through uncertainty quantification and propagation frameworks can help in understanding their effects on a part's response to design engineered components that can withstand various sources of uncertainty. However, the high-dimensional design space of multiscale material systems can make these frameworks computationally intensive and data-demanding. This work presents an efficient bottom-up hierarchical uncertainty quantification and propagation framework bridging multiple scales to establish a design allowable range for material systems at the part-scale. Specifically, the hierarchical sampling framework integrates (i) an innovative microstructure characterization and reconstruction method, (ii) a mechanistic reduced-order model for fast property predictions in high-dimensional microstructural design spaces, and (iii) an efficient copula-based sampling across multiple scales that reduces the sampling budget by 95%. We demonstrate the framework on an additively manufactured polymer nanocomposite material system that exhibits agglomeration defects formed due to attractive forces between nanoparticles at the microscale and structural variations caused by the voids resulting from different processing conditions at the mesoscale.
| Original language | English (US) |
|---|---|
| Article number | 117531 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 435 |
| DOIs | |
| State | Published - Feb 15 2025 |
Funding
The authors would like to gratefully acknowledge the support of Air Force Office of Scientific Research (AFOSR) (Grant No: FA9550-18-1-0381) and Army Research Laboratory under Cooperative Agreement Number W911NF-22-0121. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the AFOSR and Army Research Laboratory of the U.S. Government. The authors also thank Prof. Linda Schadler from the University of Vermont for providing the experimental images of PMMA-SiO2 nanocomposites. We also thank many insightful discussions with Dr. Richard Sheridan and Dr. Marc Palmeri from Duke University, and Gourav Pravin Kumbhojkar from Northwestern University for helping with the simulations. The authors would like to gratefully acknowledge the support of AFOSR (Grant No: FA9550-18-1-0381 ) and Army Research Laboratory under Cooperative Agreement Number W911NF-22-0121. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the AFOSR and Army Research Laboratory of the U.S. Government. The authors also thank Prof. Linda Schadler from the University of Vermont for providing the experimental images of PMMA-SiO 2 nanocomposites. We also thank many insightful discussions with Dr. Richard Sheridan and Dr. Marc Palmeri from Duke University, and Gourav Pravin Kumbhojkar from Northwestern University for helping with the simulations.
Keywords
- 3D-printed nanocomposites
- Copula sampling
- Dimension reduction
- Material defects
- Multiscale material systems
- Reduced order modeling
- Uncertainty quantification and propagation
ASJC Scopus subject areas
- Computational Mechanics
- Mechanics of Materials
- Mechanical Engineering
- General Physics and Astronomy
- Computer Science Applications
