Effect of Filtering Techniques in Manufacturing of Optimal Topologies Using Additive Manufacturing

Structural topology optimization is a mathematical method which seeks optimal material distribution of a given design domain under defined loading and boundary conditions. Although these approaches have been used in solving different optimization problems and generated optimal material distributions, the checkerboard effect in generated optimal topologies and manufacturing of optimal outputs due to their complex feature have been challenged. To address these challenges, different filtering techniques have been proposed including density filter, sensitivity filter, and grayscale filter. Additive manufacturing is one of the emerging technologies that enable manufacturing of almost any design and a means to fabricate topologically optimized designs. However, the effect of proposed filtering techniques on the complexity of generated optimal material distribution, amount of support material needed, and rate of convergence needs to be studied to optimize the additive manufacturing process itself. In this chapter, generated optimal topologies were manufactured using additive manufacturing, and the effect of common filtering techniques proposed to avoid checkerboard effect in structural topology optimization on complexity of generated topologies, convergence rate, and amount of support material was studied. A CubePro 3D printer, which uses plastic jet printing, was used to develop optimized structure form generated optimal topologies. Simulation results show that sensitivity filter is better in generating relatively less complex optimal topologies that can be manufactured with less amount of support material with less amount of time.