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

被引:17
作者
Yang, Jing [1 ,2 ]
Li, Shimeng [1 ]
Wang, Xiaoxing [3 ]
Lu, Jingwei [1 ,4 ]
Wu, Huaiyu [1 ]
Wang, Xiao [5 ,6 ]
机构
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
[3] Beijing SANBODY Technol Co Ltd, Beijing 214000, Peoples R China
[4] Qingdao Acad Intelligent Ind, Qingdao 66109, Peoples R China
[5] Anhui Univ, Sch Artificial Intelligence, Hefei 266114, Peoples R China
[6] Qingdao Acad Intelligent Ind, Qingdao 230031, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2023年 / 53卷 / 04期
关键词
Artificial systems; computational experiments; and parallel execution (ACP); cyber-physical-social systems (CPSSs); decentralized autonomous organization (DAO); ManuVerse; parallel manufacturing; smart manufacturing; CYBER-PHYSICAL SYSTEMS; INTELLIGENCE; METAVERSES; AUTOMATION; AWARENESS; PARADIGM; INTERNET; CONTEXT; TIME; CPS;
D O I
10.1109/TSMC.2022.3228817
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:2188 / 2199
页数:12
相关论文
共 94 条
[1]  
Ball M., 2022, The Metaverse: And How it Will Revolutionize Everything, V1st
[2]   Streaming Data Analysis: Clustering or Classification? [J].
Bezdek, James C. ;
Keller, James M. .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (01) :91-102
[3]  
Bommasani R., 2021, On the opportunities and risks of foundation models
[4]  
Brown TB, 2020, ADV NEUR IN, V33
[5]   VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments [J].
Cai, Peide ;
Sun, Yuxiang ;
Wang, Hengli ;
Liu, Ming .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2021, 6 (03) :419-429
[6]   Machine Learning-Based Target Classification for MMW Radar in Autonomous Driving [J].
Cai, Xiuzhang ;
Giallorenzo, Michael ;
Sarabandi, Kamal .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2021, 6 (04) :678-689
[7]   Cognitive Computing: Architecture,Technologies and Intelligent Applications [J].
Chen, Min ;
Herrera, Francisco ;
Hwang, Kai .
IEEE ACCESS, 2018, 6 :19774-19783
[8]   Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks [J].
Chen, Yuanyuan ;
Lv, Yisheng ;
Wang, Fei-Yue .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 21 (04) :1624-1630
[9]  
Devlin J., 2018, NAACLHLT
[10]  
Dosovitskiy A., 2021, arXiv