Topology Optimization Design for Additive Manufacturing Using Moving Morphable Components and Method of Moving Asymptotes

This research explores the topology optimization design for additive manufacturing (AM) using the Moving Morphable Components (MMC) technique and the Method of Moving Asymptotes (MMA). The proposed algorithm involves various steps, starting with problem definition. Then, choosing the topology describing functions (TDF) and the initial setting of TDF components is carried out. Finally, the algorithm performs post-processing to enhance the structure’s manufacturability. Topology optimization of a cantilever beam under end-loading is analyzed as a case study. The main objective was to minimize strain energy under a volume constraint. Results indicated that selecting the MMA parameters can significantly impact algorithm stability and convergence. The results also showed the crucial role of the MMA hyperparameters in preventing oscillation near the global optimum answer. The final topology-optimized model showed enhanced structural stiffness, reduced strain energy, and appropriate manufacturability. Finally, sharp corners are removed to prevent stress concentration in the studied beam. The obtained results demonstrate that employing the MMC method and MMA can significantly enhance topology optimization performance in various design objectives.

Keywords:

Topology optimization , Additive manufacturing , Moving morphable components , Method of moving asymptotes , Topology describing function