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 through sequential steps of the computational design and manufacturing workflow. In particular, we examine how LLMs can aid in tasks including: converting a text-based prompt into a quantitative design specification, transforming a design into manufacturing instructions, producing a design space and variations within that space, computing the performance of a given design, and optimizing for designs predicated on performance goals. Through a series of examples, we highlight overarching capabilities and limitations of the current LLMs. By exposing these aspects, we aspire to catalyze the continued improvement and progression of these models, providing a roadmap to build on their strengths and reduce their weaknesses.

“How Can Large Language Models Help Humans in Design And Manufacturing?” is a two-part article. Part 2, “Synthesizing an End-To-End LLM-Enabled Design and Manufacturing Workflow” can be read here.

Keywords: large language models (LLMs), GPT-4, computational design, computational fabrication, CAD, CAM, design for manufacturing

05/28/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

©2024 Liane Makatura, Michael Foshey, Bohan Wang, Felix Hähnlein, Pingchuan Ma, Bolei Deng, Megan Tjandrasuwita, Andrew Spielberg, Crystal Owens, Peter Yichen Chen, Allan Zhao, Amy Zhu, Wil Norton, Edward Gu, Joshua Jacob, Yifei Li, Adriana Schulz, and Wojciech Matusik. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.