Void region restriction for additive manufacturing via a diffusion physics approach

Abstract

Summary A longstanding challenge in additive manufacturing (AM), the presence of void regions in additively manufactured components, causes two main issues: the enclosing of build material powder in powder bed fusion techniques and limiting tool access in critical post‐processing operations to remove sacrificial support structures. As topology optimization has embraced and overcome many of the obstacles of incorporating AM constraints into the underlying numerical optimization statement, there exist few solutions that directly address this fundamental void region issue. By developing computationally efficient and effective solutions to this problem, the integration of these two advanced technologies can be fully realized. Drawing on inspiration from the principles of diffusion physics, a particle diffusion void restriction (PDVR) method is presented in this work that is capable of encouraging the optimization scheme to generate final designs that are fully accessible. Additionally, this method empowers the user to choose the type of post‐processing method to clear support material (eg, three‐axis or five‐axis milling operations, number and orientation of part set‐ups) and, therefore, quantify the level of costs associated with the post‐processing operation. The PDVR optimization framework is demonstrated on multiple two‐ and three‐dimensional test problems, with physically manufactured examples depicting the real‐world benefits this method admits.

Topology optimization for infill in MEx

“…Fritz and Kim (2020) and Olsen and Kim (2020) simultaneously formulated multiple approaches for integrating the build orientation to minimize the cost and time of AM parts through TO. Separately, Sabiston and Kim (2020) researched void region restriction to minimize the surface area of printed components, directly improving the fatigue life via a reduced amount of rough locations for crack initiation to occur. A unique approach taken by Wu et al (2018) focused on using TO to generate a design for the infill optimization of components based on a porous bone-like approach, governed by constraining each region of the design space to adhere to a volume fraction range.…”