Digital Twin-Based 3D Printing Monitoring

This paper proposes a digital twin-based 3D printer and model monitoring method, aiming to address the shortcomings of the current digital twin technology, which can only perform one-dimensional monitoring. The core of the research lies in constructing a comprehensive 3D printer digital twin model covering geometric, physical, and data models, to realize an all-round mapping of 3D printers. In addition, this paper proposes a monitoring method based on the improved YOLOv5 and Long Short-Term Memory (LSTM) network, which is capable of tracking and analyzing the status of 3D printing models and devices in real-time to achieve multi-dimensional monitoring of the printing process. Through experimental verification, the proposed method shows good feasibility and effectiveness, and can significantly improve the monitoring capability and response speed of the 3D printing process. The research results provide new ideas and solutions for the future development of intelligent manufacturing and promote the in-depth application of digital twin technology in the field of 3D printing.