Wire and arc additive manufacturing (WAAM) is a promising alternative to traditional subtractive methods for fabricating large aerospace metal components that feature high buy-to-fly ratios. This study focuses on the development of an automated manufacturing system in order to free the operator from intervening in the analysis of the CAD model, planning the deposition path, and then manually setting the welding process parameters. Firstly, the relationship between single bead geometry and welding process parameters is established through an artificial neural network (ANN) model. Then, the adaptive medial axis transformation (MAT) algorithm for void-free deposition with high geometrical accuracy is introduced. The adaptive MAT path is implemented by using the single bead ANN model together with a previously developed multi-bead overlapping model. Finally, the adaptive MAT path planning strategy and the established bead models are tested through experimental deposition of two metal components. The results show that the developed bead model and adaptive MAT-based path are capable of producing depositions with high quality (void-free) and geometrical accuracy through automated selection of process variables for the WAAM process.