SENSOR-BASED DIGITAL TWIN FOR FUSED DEPOSITION MODELING (FDM) 3D PRINTERS

Abstract

The development of Digital Twin for 3D printing is crucial to optimize the printing process and achieve high-quality printed objects. It allows to improve the current limitations of Fused Deposition Modeling (FDM) 3D printing such as long printing time, need for monitoring, and defects of printed parts. There are many studies on Digital Twin development for FDM 3D printing, including IoT-based monitoring, machine learning, and image processing. However, sensor-based approaches with proper sensor selection, data transfer, and visualization have not been fully explored yet. The aim of this work is the development of a Digital Twin for FDM 3D printing with improved accuracy, resulting in better control and optimization of the printing process. The main approach to building the proposed DT system consists of several important steps such as data collection, data transfer, data storage, data analysis, and a graphical user interface (GUI) that allows monitoring and control of the printing process. The system has two types of data which are data from a 3D printer and data from embedded sensors. Data from the printer were retrieved using Python, while sensor data were collected via Arduino modules and stored in a real-time database. Different sensors were compared for parameters like filament flow rate and nozzle/bed position. The Firebase database was chosen after comparison, and Unity 3D was selected as the GUI. The controller sends GCode commands to the printer line by line, enabling real-time editing and automatic defect detection. Key research results include successful integration of sensor data with printer data, selection of appropriate database and GUI platforms, and implementation of real-time control, monitoring and autonomous defect detection capabilities. The novelty of this research is that it proposes the application of affordable and accurate sensors that have not been suggested before. Furthermore, it does not use third-party hosts to control the 3D printer but instead employs Python, which allows full flexibility for defect detection and print optimization.