Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1563037 | Computational Materials Science | 2009 | 11 Pages |
Quantification and propagation of uncertainty in process conditions and initial microstructure on the final product properties in a deformation process are presented. The stochastic deformation problem is modeled using a sparse grid collocation approach that allows the utilization of a deterministic simulator to build interpolants of the main solution variables in the stochastic support space. The ability of the method in estimating the statistics of the macro-scale microstructure-sensitive properties and constructing the convex hull of these properties is shown through examples featuring randomness in initial texture and process parameters. A data-driven model reduction methodology together with a maximum entropy approach are used for representing randomness in initial texture in Rodrigues space. Comparisons are made with the results obtained from the Monte-Carlo method.