Constructing Mechanical Design Agent Based on Large Language Models
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Aug 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2024ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertisecited by 21
- 2025LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineercited by 5
- 2025Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Modelscited by 3
- 2026PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Modelscited by 3
- 2026GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embeddingcited by 0
- 2026GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding5 Jun 2026
- 2026PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models27 Mar 2026
- 2025LLM-Agent-Controller: A Universal Multi-Agent Large Language Model System as a Control Engineer26 May 2025
- 2025Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models28 Mar 2025
- 2024ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise17 Oct 2024