DeFACT in ManuVerse for Parallel Manufacturing: Foundation Models and Parallel Workers in Smart Factories

In cyber-physical-social systems, smart manufacturing has to overcome challenges, such as uncertainty, diversity, complexity in modeling, long-delayed responses to market changes, and human engineer dependency. DeFACT is a framework of parallel manufacturing in ManuVerse where the Decentralized Autonomous Organization-based interactions between parallel workers consisting of robotic, digital, and human workers are elaborated to transform from professional division to real-virtual division. In DeFACT, human workers are only responsible for 5% physical and mental work that is complex and creative, and the robotic and digital workers can take care of the rest. The perceptual and cognitive intelligence of digital workers are intensified by a manufacturing foundation model (MF-PC), where calibration and certification (C & C), and verification and validation (V & V) guarantee not only the accuracy of task models, but also the interpretability and controllability of feature learning. As a case study, the workflow of customized shoes of SANBODY Technology Company is illustrated to show how DeFACT breaks the time and space constraints, avoids production waste caused by aesthetic discrepancies with consumers, and truly realizes flexible manufacturing.

Automated summary

In cyber-physical-social systems, smart manufacturing faces challenges like uncertainty and complexity in modeling, long-delayed responses to market changes, and dependence on human engineers. DeFACT is a framework for parallel manufacturing that transforms professional division to real-virtual division through Decentralized Autonomous Organization-based interactions among robotic, digital, and human workers. Human workers are responsible for only 5% of complex and creative physical and mental work, while robotic and digital workers handle the rest. The perceptual and cognitive intelligence of digital workers are enhanced by a manufacturing foundation model (MF-PC) that ensures accurate task models and interpretable and controllable feature learning.