Year: 2025
Type: conference-paper
Abstract: In the Industry 4.0 era, 3D printing has become a cornerstone of intelligent manufacturing, necessitating precise real-time monitoring and control to ensure efficiency and quality. This research developed an IoT-based cloud monitoring system for 3D printers to enhance remote monitoring and control capabilities. The system collects real-time sensor data on parameters such as temperature and humidity, along with live video feeds. These multimodal data are pre-processed at the edge and further analyzed in the cloud. Users can monitor the 3D printing status via a custom visual interface and adjust parameters based on data analysis to optimize printing quality. To evaluate the system's effectiveness, experiments were conducted to explore the relationship between process parameters and surface roughness and to optimize the printing process and quality using the Taguchi design of experiments. Five factors were considered: nozzle temperature, bed temperature, printing speed, layer height, and ambient temperature. The significant printing process factors were identified, and optimal factor levels were determined to enhance print quality. The experimental results demonstrated that the system can significantly improve the quality and response speed of 3D printing process monitoring. Additionally, it enhances the reliability and user experience of 3D printing.
Source: IET conference proceedings.
Authors: Yanzhang Xie, Qingping Yang, Wenyi Liu, Xizhi Sun
Institutions: North University of China, Brunel University of London
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406171699