Multidimensional information model-driven digital twin for the intelligent evaluation of production capacity

The digital twin (DT) for application development in specific fields or specific factors has emerged as a foundational framework for intelligent manufacturing. The increasing demand for customized and diverse production poses enormous challenges for the effective evaluation of production line proces...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2024-07, Vol.133 (3-4), p.1621-1638
Main Authors: Zhang, Lu, He, Songping, Li, Bin, Mao, Xinyong, Tan, Xin
Format: Article
Language:English
Subjects:
Advanced manufacturing technologies
Artificial intelligence
Assembly lines
Automation
CAE) and Design
Commutators
Computer-Aided Engineering (CAD
Delphi method
Design
Digital twins
Engineering
Engineering research
Industrial and Production Engineering
Intelligent manufacturing systems
Kinematics
Manufacturing
Mechanical Engineering
Media Management
Monte Carlo simulation
Original Article
Process planning
Processing industry
Production capacity
Production lines
Software
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Summary:The digital twin (DT) for application development in specific fields or specific factors has emerged as a foundational framework for intelligent manufacturing. The increasing demand for customized and diverse production poses enormous challenges for the effective evaluation of production line processing capability (PLPC). Therefore, this paper presents a multidimensional information model-driven digital twin specifically designed for PLPC evaluation. The information model describes the characteristics of PLPC in the structural, physical, and kinematic dimensions, and it provides multidimensional information for constructing a comprehensive evaluation model in combination with the fuzzy Delphi method. An information model-driven digital twin enables more efficient extraction of processing capabilities relevant to product manufacturing and provides a digital and visual assessment of PLPC. In the case of the commutator processing industry, this paper highlights the feasibility of the digital twin model and its potential impact on forward-looking guidance for product process planning.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-13840-0