Jiaxing Lu, Heran Li, Fangwei Ning, Yixuan Wang, Xinze Li, Yan Shi
{"title":"Constructing Mechanical Design Agent Based on Large Language Models","authors":"Jiaxing Lu, Heran Li, Fangwei Ning, Yixuan Wang, Xinze Li, Yan Shi","doi":"arxiv-2408.02087","DOIUrl":null,"url":null,"abstract":"Since ancient times, mechanical design aids have been developed to assist\nhuman users, aimed at improving the efficiency and effectiveness of design.\nHowever, even with the widespread use of contemporary Computer-Aided Design\n(CAD) systems, there are still high learning costs, repetitive work, and other\nchallenges. In recent years, the rise of Large Language Models (LLMs) has\nintroduced new productivity opportunities to the field of mechanical design.\nYet, it remains unrealistic to rely on LLMs alone to complete mechanical design\ntasks directly. Through a series of explorations, we propose a method for\nconstructing a comprehensive Mechanical Design Agent (MDA) by guiding LLM\nlearning. To verify the validity of our proposed method, we conducted a series\nof experiments and presented relevant cases.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computational Engineering, Finance, and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.02087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Since ancient times, mechanical design aids have been developed to assist
human users, aimed at improving the efficiency and effectiveness of design.
However, even with the widespread use of contemporary Computer-Aided Design
(CAD) systems, there are still high learning costs, repetitive work, and other
challenges. In recent years, the rise of Large Language Models (LLMs) has
introduced new productivity opportunities to the field of mechanical design.
Yet, it remains unrealistic to rely on LLMs alone to complete mechanical design
tasks directly. Through a series of explorations, we propose a method for
constructing a comprehensive Mechanical Design Agent (MDA) by guiding LLM
learning. To verify the validity of our proposed method, we conducted a series
of experiments and presented relevant cases.