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.
As the field of design for additive manufacturing continues to evolve and accelerate towards admitting more robust designs requiring fewer instances of user-intervention, we will see the conventional design cycle evolve dramatically. However, to fully take advantage of this emerging technology — particularly with respect to large scale manufacturing operations — considerations of productivity from a fiscal perspective are sure to become of the utmost importance. A mathematical model incorporating the cost and time factors associated with additive manufacturing processes has been developed and implemented as a multi-weighted single-objective topology optimization algorithm. The aforementioned factors have been identified as component surface area and volume of support material. These quantities are optimized alongside compliance, producing a design tool that gives the user the option to choose the relative weighting of performance over cost. In two academic examples, minimization of compliance alongside surface area and support structure volume yield geometries demonstrating that considerable decreases in support material in particular can be achieved without sacrificing significant part compliance.
This paper presents a method for a system level design optimization, using currently available commercial tools. A process outlining the optimization steps to be used was created based on performing topology optimization on important components and performing a conceptual topology optimization on the entire system. Using this process, a study was performed on a ceiling structure provided by an industry partner. From the design requirements, three primary areas were targeted for design optimization, the component level optimization of the cross beam component, the component level optimization of a roof attachment bracket, and the system level of the general roof structure. This study produced a design for the ceiling structure that reduced the total mass of the system by 34%, while also reducing the amount of total components in the system by 30%.
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