Digital Twin-Based Smart Feeding System Design for Machine Tools

With the continuous development of intelligent manufacturing technology, the application of intelligent feed systems in modern machine tools is becoming increasingly widespread. Digital twin technology achieves the monitoring and optimization of the entire life cycle of a physical system by construc...

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Bibliographic Details
Published in:Electronics (Basel) 2024-12, Vol.13 (23), p.4831
Main Authors: Yuce, Baris, Li, Haobing, Wang, Linlin, Sucala, Voicu Ion
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Ball screws
Complex systems
Control systems
Controllers
Costs (Law)
Decision making
Design optimization
Digital imaging
Digital twins
Dynamic characteristics
Efficiency
Energy consumption
Failure
Feed systems
Intelligent manufacturing systems
Machine learning
Machine tools
Machinists' tools
Manufacturing
Manufacturing execution systems
Mathematical models
Neural networks
Nonlinear control
Nonlinear systems
Optimization
Preventive maintenance
Product quality
Real time
Sensors
Systems design
Technology application
Tool industry
Vibration
Virtual reality
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Summary:With the continuous development of intelligent manufacturing technology, the application of intelligent feed systems in modern machine tools is becoming increasingly widespread. Digital twin technology achieves the monitoring and optimization of the entire life cycle of a physical system by constructing a virtual image of the system, while neural network controllers, with their powerful nonlinear fitting ability, can accurately capture and simulate the dynamic behavior of complex systems, providing strong support for the optimization control of intelligent feed systems. This article discusses the design and implementation of an intelligent feed system based on digital twins and neural network controllers. Firstly, this article establishes a mathematical model based on the traditional ball screw structure and analyzes the dynamic characteristics and operating mechanism of the system. Subsequently, the mathematical model is fitted using a neural network controller to improve control accuracy and system response speed. The experimental results demonstrate that the neural network controller shows good consistency in fitting traditional mathematical models, not only effectively capturing the nonlinear characteristics of the system but also maintaining stable control performance under complex operating conditions.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13234831