A Hybrid-Model-Based CNC Machining Trajectory Error Prediction and Compensation Methtyleod

Intelligent manufacturing is the main direction of Industry 4.0, pointing towards the future development of manufacturing. The core component of intelligent manufacturing is the computer numerical control (CNC) system. Predicting and compensating for machining trajectory errors by controlling the CN...

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Vydané v:Electronics (Basel) Ročník 13; číslo 6; s. 1143
Hlavní autori: He, Wuwei, Zhang, Lipeng, Hu, Yi, Zhou, Zheng, Qiao, Yusong, Yu, Dong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.03.2024
Predmet:
Accuracy
Algorithms
Analysis
Artificial intelligence
Automation
Compensation
Computer industry
Data analysis
Data processing
Digital twins
Energy consumption
Hybrid systems
Industry 4.0
Intelligent manufacturing systems
Machining
Manufacturing
Mathematical models
Methods
Numerical controls
Optimization
Predictive control
Process parameters
Trajectory control
China
ISSN:2079-9292, 2079-9292
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Shrnutí:Intelligent manufacturing is the main direction of Industry 4.0, pointing towards the future development of manufacturing. The core component of intelligent manufacturing is the computer numerical control (CNC) system. Predicting and compensating for machining trajectory errors by controlling the CNC system’s accuracy is of great significance in enhancing the efficiency, quality, and flexibility of intelligent manufacturing. Traditional machining trajectory error prediction and compensation methods make it challenging to consider the uncertainties that occur during the machining process, and they cannot meet the requirements of intelligent manufacturing with respect to the complexity and accuracy of process parameter optimization. In this paper, we propose a hybrid-model-based machining trajectory error prediction and compensation method to address these issues. Firstly, a digital twin framework for the CNC system, based on a hybrid model, was constructed. The machining trajectory error prediction and compensation mechanisms were then analyzed, and an artificial intelligence (AI) algorithm was used to predict the machining trajectory error. This error was then compensated for via the adaptive compensation method. Finally, the feasibility and effectiveness of the method were verified through specific experiments, and a realization case for this digital-twin-driven machining trajectory error prediction and compensation method was provided.
Bibliografia:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13061143