Compliant Mechanism Design Using Multi-Agent Large Language Models
Abstract
The transition from rigid-body to compliant mechanisms offers well-recognized advantages in mechanical design, including reduced part count, improved energy efficiency, and expanded functional integration. However, existing catalogue-based and artificial intelligence–assisted design approaches often rely on abstract functional archetypes, limiting practical applicability and hindering identification of integrated, application-specific compliant solutions. This study presents a methodological framework for extracting compliant mechanism solutions from patent literature based on a structured, multi-agent pipeline employing Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) architecture. The proposed approach integrates product decomposition, lexical and technological expansion, patent retrieval, compatibility assessment, and taxonomy-grounded classification using compliant mechanism archetypes. The framework is demonstrated through a case study involving 40 patents related to pedal-actuated waste bin mechanisms, enabling qualitative evaluation against expert judgment. The results indicate consistent identification of compliant alternatives to conventional rigid mechanisms, ranging from localized compliant substitutions (e.g., living hinges and leaf springs) to more integrated compliant transmission architectures. The patent-derived solutions provide application-specific structural embodiments, including geometric details and implementation information…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.