Additive Manufacturing (AM) plays a crucial role in the revolution towards Industry 4.0, by enabling the direct translation of digital 3D models into physical objects while reducing process steps and minimizing human intervention. While conventional AM machines are generally limited to three-axis movement, multi-axis AM equipment extends the manufacturing flexibility by enabling the fabrication of freeform layers, thus creating new opportunities to improve part quality, though at the cost of increased complexity in process planning. Wire Arc Additive Manufacturing (WAAM) is a multi-axis technique for producing large metal components, with potential applications in maritime, aerospace and civil infrastructure. However, this potential is hindered by factors such as deformation during fabrication, which compromises part precision and can lead to process failure. Recently, a computational approach has been developed to reduce distortion by optimizing the fabrication sequence. While promising, the optimized sequence is characterized by large variations in layer thickness, rendering them non-manufacturable. This research proposes numerical methods to evaluate and restrict layer thickness in fabrication sequence optimization, ensuring uniform thickness within each layer and a consistent average thickness across the entire sequence. A 3D computational framework has been developed, integrating latest advancements in sequence optimization. To address the intensive computation in 3D, this framework features a parallel implementation using the PETSc library. This framework enables the numerical assessment of the method’s performance and provides a foundation for future experimental validation.