Automated Assembly Instruction Generation from CAD Models Using Grounded Large Language Models: A Human-in-the-Loop Framework
This study addresses the reliance on manual labor, fragmented workflows, and lack of collaborative oversight in converting CAD models into assembly documentation. To overcome these challenges, this work proposes an automated assembly instruction generation framework grounded in large language models. The framework employs a five-layer architecture that leverages STEP parsing and a ProductGraph intermediate representation to achieve structured information extraction and deterministic topological sorting. By integrating constrained contextual generation with visual augmentation, it realizes an end-to-end pipeline, while incorporating rule-based verification and human-in-the-loop mechanisms to ensure engineering accuracy. Experimental results validate the effectiveness of this system, which represents the first unified closed-loop approach to seamlessly integrate CAD interpretation, sequence planning, instruction writing, and human supervision.