AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design

• URL: http://arxiv.org/abs/2503.23315v1
• Date: Sun, 30 Mar 2025 04:57:17 GMT
• Title: AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design
• Authors: Mohamed Elrefaie, Janet Qian, Raina Wu, Qian Chen, Angela Dai, Faez Ahmed,
• Abstract summary: We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process.<n>Our framework integrates AI-driven design agents into the traditional engineering workflow to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle.
• Score: 24.258618104493532
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines.

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