Multisensor Data Fusion-Driven Digital Twins in Computer Numerical Control Machining: A Review

As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product d...

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Veröffentlicht in:Machines (Basel) Jg. 13; H. 10; S. 921
1. Verfasser: Cao, Yang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2025
Schlagworte:
Accelerometers
CAE
CNC machining
Computer aided engineering
Computer industry
Computers
Data integration
Deformation
digital twin
Digital twins
Fault diagnosis
Heat
Machining
Manufacturing
Microelectromechanical systems
multisensor data fusion
Multisensor fusion
Numerical controls
Optimization
Product design
Quality control
Quality management
Real time
Sensors
Signal processing
Simulation
Strain gauges
Technology application
Vibration
ISSN:2075-1702, 2075-1702
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Zusammenfassung:As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product design, process monitoring, quality control, and fault diagnosis. A DT creates a virtual replica of the physical system by integrating real-time data with simulation technologies, providing new possibilities to make CNC machining more intelligent. In the past decade, extensive research has been conducted on the implementation of CNC machining DTs (CNCDTs). This paper focuses specifically on multisensor data fusion-driven CNCDTs by introducing key technologies including sensors, data fusion, and CNCDT architecture. A comprehensive survey is conducted on existing studies of CNCDTs according to their application areas, followed by critical analysis on existing challenges. This review summarizes the current progress of CNCDTs and provides guidance for further development.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2075-1702
2075-1702
DOI:10.3390/machines13100921