MIT introduces a human-machine collaborative topology optimization system
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MIT researchers have developed a structural design system similar to an AI copilot that, during the process of generating structures with a topology optimization algorithm, hands some regions over to human designers for feedback. MIT News says that compared with purely algorithmic methods, the system adds only 15 seconds of design time while improving manufacturability and linear buckling load by 39%. The system uses machine learning to quickly identify the regions designers are most likely to want to modify, and currently only supports thickening or thinning specified regions. The researchers hope this will lower the barrier to using topology optimization and enable more engineers, researchers, and students to use the technology.
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- MIT News AINew AI “copilot” helps engineers design better structures
MIT researchers developed a human-in-the-loop topology optimization system that presents regions of an algorithmic design to a human designer for feedback as it is generated, using machine learning trained on synthetic human-feedback data to identify likely regions of interest. In demonstrations, the approach improved manufacturability and linear buckling load by 39 percent while adding only 15 seconds to total design time compared to all-algorithmic approaches. The system currently only lets users make structures within identified regions thicker or thinner, and the researchers hope to add other modification options in future versions.
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