How Can Large Language Models Help Humans in Design And Manufacturing? Part 2: Synthesizing an End-to-End LLM-Enabled Design and Manufacturing Workflow
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Published version
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4.03 MB
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Author(s)
Makatura, Liane
Foshey, Michael
Wang, Bohan
Hähnlein, Felix
Ma, Pingchuan
Deng, Bolei
Tjandrasuwita, Megan
Spielberg, Andrew
Owens, Crystal
Chen, Peter Yichen
Date Issued
May 28, 2024
Journal
Harvard Data Science Review
Publisher
MIT Press
Citation
Makatura, L., Foshey, M., Wang, B., Hähnlein, F., Ma, P., Deng, B., Tjandrasuwita, M., Spielberg, A., Owens, C., Chen, P. Y., Zhao, A., Zhu, A., Norton, W., Gu, E., Jacob, J., Li, Y., Schulz, A., & Matusik, W. (2024). How Can Large Language Models Help Humans in Design And Manufacturing? Part 2: Synthesizing an End-to-End LLM-Enabled Design and Manufacturing Workflow . Harvard Data Science Review, (Special Issue 5).
Version
Final published version
Abstract
Generative AI models provide foundational knowledge and sufficient reasoning to aid in individual aspects of a computational design and modeling workflow (see Part 1). In this work, we analyze the ability of state-of-the-art LLMs (in particular GPT-4) to reason about the entire end-to-end workflow, from conceptualizing to realization, for two target domains: static physical objects (here, furniture), and dynamical cyberphysical systems (here, quadcopters). Our investigation serves as an exemplar of how to use LLMs to build out such workflows, as well as a means of identifying which steps are most brittle and especially warrant future study. We conclude with an analysis of the capabilities and limitations of LLMs in the context of design and manufacturing as a whole, as well as a brief discussion of the grander opportunities and ethical risks put forth by such systems.
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DOI of Published Version
https://doi.org/10.1162/99608f92.0705d8bd
