Uncertainty Quantification in ICMSE: Application to Metal Alloys

This paper presents a methodology to propagate the uncertainties in the manufacturing process parameters to bulk material properties through multi-scale modeling. Randomness of material initial condition and uncertainties in the manufacturing process lead to variability in the microstructure, which in turn leads to variability in the macro-level properties of the material. In this paper, 2-D dual phase polycrystalline microstructure is simulated based on the initial condition of the grain cores and the manufacturing environment, instead of Voronoi tessellation which assumes equal grain growth velocities for different phases and therefore is unable to link variability in grain growth velocity to the manufacturing process variability. Then a homogenization method is applied to predict macro-level properties. Cooling schedule of a dual phase alloy is used to illustrate the methodology, and Young’s Modulus is the prediction quantity of interest. Even with a given cooling schedule, spatial variation of temperature affects the microstructure and properties; this variability is also incorporated through a random field representation. The uncertainty quantification methodology uses Gaussian Process surrogate modeling for computational efficiency. The relative contributions of both aleatory and epistemic sources to the overall bulk property uncertainty are quantified using an innovative global sensitivity analysis approach; this provides guidance for manufacturing process control in order to meet the desired uncertainty bounds in the bulk property estimates.