A digital twin -driven dynamic prediction method for robotized production line operational perfomance

被引:1
作者
Deng, Zhaolin [1 ]
Tian, Sisi [1 ]
Xu, Wenjun [1 ]
Lu, Jue [2 ]
Hu, Yang [3 ]
机构
[1] Wuhan Univ Technol, Sch Informat Engn, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan, Peoples R China
[2] Wuhan Univ Technol, Sch Informat Engn, Wuhan, Peoples R China
[3] China Ship Dev & Design Ctr, Wuhan, Peoples R China
来源
2022 IEEE 17TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2022年
关键词
Digital Twin; Robotized Production Line; Online Extreme Learning Machine Algorithm; CYCLE TIME;
D O I
10.1109/ICIEA54703.2022.10006259
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the widespread use of robotized production lines (RPI.) in manufacturing plants, timely and accurate prediction of production line operation performance is very important, which guarantees reasonable adjustment of production planning and resource allocation. The current prediction methods of production line operation performance are mostly off-line prediction, which cannot adjust the prediction model timely according to the dynamic changing characteristics of the manufacturing process, and have the problems of functional lag and low accuracy. In order to address the above issues, a digital twin -driven dynamic prediction method for the operational performance of RPL is proposed. Firstly, the manufacturing process description model and operational performance evaluation indicator are constructed based on multi-source manufacturing data from products, industrial robot equipment, and system operation dimensions. Exploring the time-varying mechanism of the manufacturing system, the digital twin model of RPL is constructed from multi-dimension. Then the correlation between manufacturing data and operational performance is explored using grey relational analysis (GRA). The online extreme learning machine algorithm is proposed to predict the dynamic performance of RPL, which is self-correcting and self-optimizing to realize the dynamic prediction based on the digital twin. Finally, a case study is implemented to verify the feasibility and effectiveness of the proposed method.
引用
收藏
页码:1222 / 1227
页数:6
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