Generic roughness meta-model in 3D printing by Fused Deposition Modeling

Design for Additive Manufacturing (DfAM) aims at optimizing the product design based on rules defined thanks to (AM) constraints. To elaborate a functional design, several parameters like part orientation and layer thickness influence results of mechanical strength, roughness, manufacturing time. Surface roughness is an important input Fused Deposition Modeling which impacts functionalities. participate in optimization DfAM development, this research suggests studying models clarify best methodology. This analysis permits propose meta-model that provides better estimation surface FDM products. A new model developed by combination genetic programming symbolic regression due experimental data helps manufacturer predict quality products before fabrication. It enables us estimate all AM fabricated different value possible orientations space regarding building direction.