A Cost-Efficient Data-Driven Approach to Design Space Exploration for Personalized Geometric Design in Additive Manufacturing

Additive manufacturing (AM) is considered as a key to personalized product realization as it provides great design flexibility. As the flexibility radically expands the design space, current design space exploration methods for personalized geometric designs become time-consuming due to the use of physically based computer simulations (e.g., finite element analysis or computational fluid dynamics). This poses a significant challenge in design for an efficient personalized product realization cycle, which imposes a tight computation cost constraint to timely respond to every new requirement. To address the challenge, we propose a cost-efficient data-driven design space exploration method for personalized geometric design in AM, enabling feasible design regions under the computation constraint. Specifically, the proposed method adopts surrogate modeling of efficient voxel model-based design rules to identify feasible design regions considering both manufacturability and personalized needs. Since design rules take much less time for evaluation than physically based simulations, the proposed method can contribute to timely providing feasible design regions for an efficient personalized product realization cycle. Moreover, we develop a cost-based experimental design for surrogate modeling, which enables the evaluation of additional design points to provide more precise feasible design regions under the computation cost constraint. The merits of the proposed method are elaborated via additively manufactured microbial fuel cell (MFC) anode design.