Large Language Models for Design and Manufacturing

作者：Liane Makatura, Michael Foshey, Bohan Wang, Felix Hähnlein, Pingchuan Ma, Bolei Deng, Megan Tjandrasuwita, Andrew Spielberg, Crystal E. Owens, Peter Yichen Chen, Allan Zhao, Amy Zhu, Wil J. Norton, Edward Gu, Joshua Jacob, Yifei Li, Adriana Schulz, Wojciech Matusik · 年份：2024 · DOI：10.21428/e4baedd9.745b62fa · 被引用次数：30 · 研究领域：BIM and Construction Integration、Machine Learning in Materials Science、Manufacturing Process and Optimization

The progress in generative AI, particularly large language models (LLMs), opens new prospects in design and manufacturing. Our research explores the use of these tools throughout the entire design and manufacturing workflow. We assess the capabilities of LLMs in various tasks: converting text prompts into designs, generating design spaces and variations, transforming designs into manufacturing instructions, evaluating design performance, and searching for designs based on performance metrics. We identify and discuss the current strengths and limitations of LLMs, suggesting areas for potential enhancements. Additionally, we examine the ethical implications and propose strategies to mitigate risks associated with employing generative AI in design and manufacturing.