Processes such as wire arc additive manufacturing (WAAM) involve rapid solidification phenomena that significantly complicate the study and prediction of microstructures. Accurate prediction of the solidification microstructure at the interfaces of the weld beads requires a full-field approach. Accurate knowledge of the transient temperature field during rapid solidification of « additive-manufactured » parts is necessary to use such a model. Indeed, a coupled thermal equation with a phase evolution equation must be solved. In this work, a tool is developed to accelerate computation time using data obtained from light optical and SEM observations. This tool also provides information on solidification microstructure using successive low-cost modeling methods. The first method is based on light optical images of the weld beads. It allows identifying zones of crystallographic textures as well as the orientation of the thermal gradient based on the knowledge of the bead contours. The next two methods analyze the connectivity and continuity of these crystallographic zones. These steps use a convolutional neural network (CNN) method based on scanning electron microscope (SEM) images. Finally, the microstructure of an entire bead is performed using Kobayashi-Warren-Carter phase field model. Coupled solving of phase, thermal and crystallographic orientation equations is done by a finite volume solver. EBSD Images are used as input data to adjust and verify this latter method. A mapping of several neighboring beads is also performed with information derived from the previous methods. The combined used of selected experimental input, simple and extremely fast methods based on geometric considerations with advanced phase field modeling allows new insight in the solidification microstructure of wire arc deposited material.