Generating Optimized 3d Designs for Manufacturing Using a Guided Voxel Diffusion Model
Authors: Chilukuri Premith Kumar , Song Binyang , Kang SungKu , Jin Ran 
Published: August 2024

In the age of digital manufacturing, efficient realization of high-performing designs into high-quality products through advanced manufacturing remains a challenge. One of the critical reasons for this challenge is the need for more design knowledge at the manufacturers’ end, which severely hinders manufacturers’ exploration and exploitation of innovative designs. Manufacturers often have no choice but to adopt designer-optimized designs without the ability to make enhancements. However, the advent of data-driven reverse engineering based on generative modeling has shown promise for addressing this issue. As industries increasingly adopt digital manufacturing strategies, integrating AI-assisted design generation and optimization tools could enable efficient production outcomes. By leveraging large datasets and powerful machine learning techniques, manufacturers can now explore the vast design space in an efficient, automated manner, enabling them to drive innovation by uncovering novel design opportunities. The expanded design knowledge gained through generative modeling allows manufacturers to move beyond just implementing others’ designs and truly innovate themselves to create higher-quality and more optimized products. Nevertheless, while generative models have been widely explored for two-dimensional (2D) design and optimization, generative modeling of three-dimensional (3D) shapes still remains under-explored. Although a variety of 3D shape generative models have been developed, the resolutions and surface qualities of the generated designs can hardly meet manufacturing requirements. To bridge this gap, this paper proposes a generative denoising diffusion model (DDM) to generate new voxel-represented 3D designs, where the model is trained with historical 3D design data available to manufacturers. This research is important, not only because manufacturers’ design knowledge can be improved, but also because design exploration can be expanded and expedited when more feasible designs can be generated faster to pursue designs with improved manufacturability, higher feasibility, and optimized performance. DDMs have been extensively explored to synthesize 2D pixel images, and also have achieved state-of-the-art generation quality. Motivated by the capabilities of DDMs in generating high-quality images, we adapt the application of DDMs from 2D space to 3D space and develop a voxel-DDM for high-quality 3D topology generation. Moreover, we develop a surrogate model for efficient and gradient-based evaluation of the manufacturability of 3D designs via additive manufacturing. We further integrate the surrogate model as a guidance module into the voxel-DDM to steer the denoising process toward generating new designs with optimized manufacturability. To showcase the proposed guided voxel-DDM and validate its effectiveness, we implement it through a real-world manufacturing case study to design a Microbial Fuel Cell (MFC) anode structure that enables an efficient, reliable, and high-quality manufacturing process. This research can help manufacturers overcome their lack of design knowledge in design space exploration and lead to high-quality designs for the manufacturing process in pursuit of optimized manufacturability.