피지컬 AI 활용을 위한 마이크로팩토리의 핵심기능 및 속성 정보 구조 시뮬레이션에 관한 연구

초록

Micro factories have emerged as a flexible evolution of smart factories to address diversified and personalized manufacturing demands. Unlike conventional mass-production systems, they support small-batch, customized, and decentralized manufacturing by integrating digital technologies such as IoT, AI, and robotics. This study proposes a hierarchical attribute information model for representing manufacturing capabilities based on materials, processes, equipment, and production capacity. A hybrid database framework combining the Entity-Attribute-Value (EAV) model and JSON-based data structures is designed to support efficient management and matching of micro factories. The proposed framework enables systematic classification of manufacturing resources and is expected to improve production efficiency, reduce logistics costs, and support distributed manufacturing for Physical AI based smart manufacturing.

키워드