Six-Axis Robotic Milling for Enhancing Surface Quality and Dimensional Accuracy of Fused Granular Fabrication Parts

Fused granular fabrication (FGF) offers high deposition efficiency and low material cost for large-scale mold production, but commonly yields parts with surface defects and dimensional deviations. This study develops a six-axis robotic post-processing workstation that integrates multi-DOF toolpath planning and real-time communication to flexibly machine FGF components with complex geometries. Using short-fiber-reinforced polypropylene (PP-GF), robotic milling experiments were performed, and spindle speed, feed rate, and cutting depth were systematically optimized to enhance surface quality and dimensional accuracy. The NSGA-III algorithm optimizes parameters, thereby increasing machining efficiency by 4.9% and reducing surface roughness by 12.35%. Results show that the proposed platform effectively improves the machining performance of FGF-printed parts, demonstrating its feasibility for high-precision post-processing. The work provides a practical technical route for the hybrid additive–subtractive manufacturing of large 3D-printed structures.