Paper Infomation
Research on Tool Wear Prediction of CNC Machine Tools Based on Digital Twin
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Author: Dongjun He
Abstract: The prediction of tool wear in CNC machine tools is a critical aspect of ensuring the efficient operation and longevity of manufacturing equipment. Tool wear significantly impacts machining accuracy, surface finish quality, and operational downtime, making its prediction essential for proactive maintenance strategies. This paper explores the integration of Digital Twin technology with tool wear prediction models to enhance the precision and reliability of wear forecasting in CNC machines. We review existing methodologies for tool wear prediction, including physics-based models, data-driven approaches, and hybrid models, with an emphasis on their strengths and limitations. Furthermore, the paper highlights the role of Digital Twin technology in creating real-time, virtual replicas of CNC machines that can dynamically monitor tool wear and provide actionable insights for optimization. By leveraging real-time data and advanced simulation techniques, Digital Twin-based prediction models offer significant improvements over traditional methods. The paper concludes by discussing future directions for integrating machine learning, deep learning, and real-time data analytics into the tool wear prediction process, ultimately contributing to the development of more intelligent and adaptive manufacturing systems.
Keywords: Tool Wear Prediction; CNC Machines; Digital Twin; Predictive Maintenance; Machine Learning; Hybrid Models; Real-Time Monitoring; Optimization
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