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Meeting MS&T25: Materials Science & Technology
Symposium Applications of Uncertainty Quantification (UQ) in Science and Engineering
Presentation Title Representative microstructure for macro-scale property prediction using multi-scale models
Author(s) Arulmurugan Senthilnathan, Pranav Karve, Sankaran Mahadevan
On-Site Speaker (Planned) Arulmurugan Senthilnathan
Abstract Scope Micromechanical models require a representative microstructure of the meso-scale coupon to predict macro-scale properties. However, microstructures typically obtained using the Electron Back Scattered Diffraction (EBSD) technique may not be representative of the meso-scale coupon due to spatial variability of microstructural features that influence macro-scale properties. This work develops a novel comprehensive methodology to determine the representative microstructure (RM) size of a meso-scale coupon for predicting macro-scale properties using micromechanical model. RM size is determined by using a dissimilarity metric that quantifies the spatial variability of the microstructure features and a reliability metric that estimates the accuracy of mechanical property prediction. The developed methodology is illustrated on synthetically generated conventionally forged Titanium-7%(wt) Aluminum (Ti-7Al) microstructure and the RM size of a Ti-7Al is determined. Developing a methodology to determine RM size paves the way for accurate macro-scale property prediction that is used for material design, uncertainty quantification, and optimum process design.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A case study of Bayesian parameter estimation for thermal property inference and uncertainty quantification
Leveraging Archival Additively Manufacturing Fatigue Data to Investigate the Role of Processing Porosity with Greater Precision
Representative microstructure for macro-scale property prediction using multi-scale models
Sparse grids for magneto-hydrodynamics
Uncertainty Quantification via Deep Kernel Learning for Predicting Multimodal β-phase Volume Fraction from SXRD Patterns

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