Additive manufacturing (AM) simulations offer an alternative to expensive AM experiments to study the effects of processing conditions on granular microstructures. Existing AM simulations lack support from reliable validation techniques. The stochastic nature and spatial heterogeneity of microstructures make it difficult to validate the simulated microstructures against experimentally obtained images through statistical measures such as average grain size. Another challenge is the lack of reliable and automated methods to calibrate the model parameters, which are unknown and difficult to measure directly from experiments. To overcome these two challenges, we first present a novel metric to quantify the difference between granular microstructures. Then, using this metric in conjunction with Bayesian optimization, we present a framework that can be used to reliably and efficiently calibrate the model parameters. We employ this framework to first calibrate the substrate microstructure simulation and then the laser scan microstructure simulation for Inconel 625. Results show that the framework allows successful calibration of the model parameters in just a small number of simulations.