Multi-objective optimization of 6-DOF deposition trajectories using NSGA-II

• Published in: Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 45; no. 11
• Main Authors: Guacheta-Alba, Juan C., Nunez, Diego A., Dutra, Max Suell, Mauledoux, Mauricio, Aviles, Oscar F.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Degrees of freedom; Deposition; Engineering; Fused deposition modeling; Genetic algorithms; Linear programming; Mechanical Engineering; Multiple objective analysis; Optimization techniques; Sorting algorithms; Surface properties; Technical Paper; Trajectory analysis; Trajectory optimization; Trajectory planning; Fused filament fabrication; Trajectory planning; Systems optimization; Parallel kinematics; Mechanism
• ISSN: 1678-5878; 1806-3691
• DOI: 10.1007/s40430-023-04495-1

In fused filament fabrication, an emerging challenge lies in assuring heightened performance during material deposition, being an objective that trajectory optimization techniques can achieve. A study of the trajectory planning techniques used for material deposition and their challenges and benefits are presented. Trajectory planning focuses on movements of six degrees of freedom, carried out by a designed Hexa parallel manipulator. These layered paths are linked through proximate spatial connections, generating a continuous trajectory for the printing process. The problem of multi-objective optimization arises with decision variables that modify the fill line distance, the corner smoothing, and the layers. Also, metrics to evaluate the trajectories are defined, focused on motors’ consumption, movements’ precision, surface quality, printing time, and material used. The non-dominated sorting genetic algorithm (NSGA-II) solves this problem, and the linear programming technique for multidimensional analysis of preference (LINMAP) fixes the trajectory selection. Finally, on a gallery of parts, the trajectory optimization algorithm is implemented, and the performance of the optimal trajectories is compared against trajectories generated by free commercial software, improving its performance above 70% on superficial underfilling and coated volume error, and without control effort generated by travel movements.