Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, the ability to model the evolution of microstructures from changes in processing condition or even predict the microstructures from given processing condition would greatly reduce the time frame and the cost of the optimization process. In 1, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. We also demonstrate the capability of predicting new microstructures from the representation with deep learning and we can explore the physical insights of the implicitly expressed microstructure representations. We validate our framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures. In 2, we further improve and generalize the generating framework, a set of metrics is used to quantitatively analyze the effectiveness of the generation by comparing the microstructure characteristics between the generated samples and the originals. We also take advantage of image processing techniques to aid the calculation of metrics that require grain segmentation.