Generative Design for Manufacturing: Integrating Generation with Optimization Using a Guided Voxel Diffusion Model
| dc.contributor.author | Song, Binyang | en |
| dc.contributor.author | Chilukuri, Premith Kumar | en |
| dc.contributor.author | Kang, Sungku | en |
| dc.contributor.author | Jin, Ran | en |
| dc.date.accessioned | 2024-02-22T18:31:43Z | en |
| dc.date.available | 2024-02-22T18:31:43Z | en |
| dc.date.issued | 2024 | en |
| dc.description.abstract | In digital manufacturing, converting advanced designs into quality products is hampered by manufacturers' limited design knowledge, restricting the adoption and enhancement of innovative solutions. This paper addresses this challenge through a novel generative denoising diffusion model (DDM) trained on historical 3D design data, enabling the creation of voxel-based designs that meet manufacturing standards. By integrating a surrogate model for evaluating the manufacturability of generated designs, the proposed DDM is able to optimize manufacturability during the generative process. This paper takes a leap forward from the predominant 2D focus of existing generative models towards 3D generative design, which not only broadens manufacturers' design capabilities but also accelerates the development of practical and optimized products. We demonstrate the efficacy of this approach via a case study on Microbial Fuel Cell (MFC) anode design, illustrating how this method can significantly enhance manufacturing workflows and outcomes. Our research offers a path for manufacturers to deepen their design expertise and foster innovation in digital manufacturing. | en |
| dc.description.notes | Yes, full paper (Peer reviewed?) | en |
| dc.description.version | Accepted version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | https://hdl.handle.net/10919/118111 | en |
| dc.language.iso | en | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.title | Generative Design for Manufacturing: Integrating Generation with Optimization Using a Guided Voxel Diffusion Model | en |
| dc.type | Conference proceeding | en |
| dc.type.dcmitype | Text | en |
| pubs.finish-date | 2024-02-27 | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/Engineering | en |
| pubs.organisational-group | /Virginia Tech/Engineering/Industrial and Systems Engineering | en |
| pubs.start-date | 2024-02-20 | en |
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