Inner surface smoothing for layered manufacturing of fluid-flowing channels based on generative support pattern

Abstract

This paper proposes an inner surface smoothing methodology for additive manufacturing of fluid-flowing nozzle based on generative support pattern (GSP). Most fluid-flowing channels have distinct characteristics, including variable thickness, internal crisscrossing runner channels, etc, leading to inevitable distortion difficulty among multiple features during conceptual prototype manufacturing process. The novelty of the work lies in attentively improving surface quality of fluid-flowing channels, which are commonly correlative with mechanical performance, by comprehensively arranging relative manufacturing parameters via GSP. The volume errors, caused due to slicing strategy, is ensured via the Hausdorff distance, which is applied as the metric of the deviation between the voxelization and original manifold. The curing time of layers is designed with the aim of diminishing the reaction shrinkage. And the patterns of external supports are generated by jointly considering the surfaces integrity of functional features and mechanical performance during printing. The vat photopolymerization simulation based on finite element analysis is conducted jointly considering the chemical reaction kinetics and evolution of material properties. The designed manufacturing parameters through GSP is virtually verified by evaluating the surface accuracy of manufactured elements. The physical experiment is conducted to verify GSP via digital light processing (DLP). Moreover, the roughness measuring instrument and 3D optical scanner could be implemented to confirm the distortion distribution. Most significantly, the maximum peak-to-valley height is physically verified to be reduced by 7.08% via GSP, which contributes to enhancing surface quality of target fluid-flowing channels.