How Can Large Language Models Help Humans in Design and Manufacturing? Part 1: Elements of the LLM-Enabled Computational Design and Manufacturing Pipeline
Abstract
The advancement of large language models (LLMs), including GPT-4, provides exciting new opportunities for generative design. We investigate the application of this tool across the entire design and manufacturing workflow. Specifically, we scrutinize the utility of LLMs in tasks such as: converting a text-based prompt into a design specification, transforming a design into manufacturing instructions, producing a design space and design variations, computing the performance of a design, and searching for designs predicated on performance. Through a series of examples, we highlight both the benefits and the limitations of the current LLMs. By exposing these limitations, we aspire to catalyze the continued improvement and progression of these models.
Acknowledgements
This material is based upon work supported in part by Defense Advanced Research Projects Agency (DARPA) Grant No. FA8750-20-C-0075, DARPA Fellowship Grant No. HR00112110007, and the National Science Foundation (NSF) under Grant No. 2141064. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.
Citation
@article{Makatura2024a_LLMsForCDAM,
author = {Makatura, Liane and Foshey, Michael and Wang, Bohan and H{"a}hnlein, Felix and Ma, Pingchuan and Deng, Bolei and Tjandrasuwita, Megan and Spielberg, Andrew and Owens, Crystal and Chen, Peter Yichen and Zhao, Allan and Zhu, Amy and Norton, Wil and Gu, Edward and Jacob, Joshua and Li, Yifei and Schulz, Adriana and Matusik, Wojciech},
journal = {Harvard Data Science Review},
number = {Special Issue 5},
year = {2024},
month = {dec},
note = {https://hdsr.mitpress.mit.edu/pub/15nqmdzl},
publisher = {The MIT Press},
title = {{How} {Can} {Large} {Language} {Models} {Help} {Humans} in {Design} and {Manufacturing}? {Part} 1: Elements of the {LLM}-{Enabled} {Computational} {Design} and {Manufacturing} {Pipeline}},
}