Multi-level uncertainty quantification in additive manufacturing

Quantifying the uncertainty in additive manufacturing (AM) process plays an important role in the quality control of additively manufactured products. This work presents an uncertainty quantification (UQ) framework to quantify the uncertainty of material microstructure due to multiple uncertainty (aleatory and epistemic) sources present in the AM simulation process. A multi-scale, multi-physics simulation model is first developed to simulate the melting and solidification processes. The melt pool profile obtained from macro-scale finite element analysis is coupled with a micro-scale cellular automata model to predict the microstructure evolution during solidification. Based on the simulation model, various sources of uncertainty are aggregated to quantify the uncertainty in the grain size distribution of the microstructure. The contributions of the various sources of uncertainty to the uncertainty of microstructure grain size distribution are analyzed using variance-based global sensitivity analysis. The results show that the proposed approach can effectively perform UQ of the AM process and the uncertainty in the grain size distribution is mainly affected by material properties and grain growth parameters.