Digital Twins for Defect Detection in FDM 3D Printing Process

Abstract

Additive manufacturing (AM, also known as 3D printing) is a bottom–up process where variations in process conditions can significantly influence the quality and performance of the printed parts. Digital twin (DT) technology can measure process parameters and printed part characteristics in real-time, achieving online monitoring, analysis, and optimization of the AM process. Existing DT research on AM focuses on simulating the printing process and lacks real-time defect detection and twinning of actual printed objects, which hinders the timely detection and correction of defects. This study developed a DT system for fused deposition modeling (FDM) AM technology that not only accurately simulates the printing process but also performs real-time quality monitoring of the printed parts. A laser profilometer and industrial camera were integrated into the printer to detect and collect real-time morphological data on the printed object. The custom-developed DT software could convert the morphological data of the printed parts into a DT model. By comparing the DT model of the printed object with its three-dimensional model, defect detection of the printed parts was achieved, where the quality of the printed parts was evaluated using a defect percentage index. This study combines DT and AM to achieve process quality monitoring, demonstrating the potential of DT technology in reducing printing defects and improving the quality of printed parts.