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Article

Digital Twins for Defect Detection in FDM 3D Printing Process

1
Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130025, China
2
Institute of Structured and Architected Materials, Liaoning Academy of Materials, Shenyang 110167, China
3
Weihai Institute for Bionics, Jilin University, Weihai 264207, China
4
College of Construction Engineering, Jilin University, Changchun 130025, China
*
Authors to whom correspondence should be addressed.
Machines 2025, 13(6), 448; https://doi.org/10.3390/machines13060448
Submission received: 8 April 2025 / Revised: 10 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025
(This article belongs to the Section Advanced Manufacturing)

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.
Keywords: fused deposition modeling; digital twin; morphological data; online monitoring; defect detection fused deposition modeling; digital twin; morphological data; online monitoring; defect detection

Share and Cite

MDPI and ACS Style

Xu, C.; Lu, S.; Zhang, Y.; Zhang, L.; Song, Z.; Liu, H.; Liu, Q.; Ren, L. Digital Twins for Defect Detection in FDM 3D Printing Process. Machines 2025, 13, 448. https://doi.org/10.3390/machines13060448

AMA Style

Xu C, Lu S, Zhang Y, Zhang L, Song Z, Liu H, Liu Q, Ren L. Digital Twins for Defect Detection in FDM 3D Printing Process. Machines. 2025; 13(6):448. https://doi.org/10.3390/machines13060448

Chicago/Turabian Style

Xu, Chao, Shengbin Lu, Yulin Zhang, Lu Zhang, Zhengyi Song, Huili Liu, Qingping Liu, and Luquan Ren. 2025. "Digital Twins for Defect Detection in FDM 3D Printing Process" Machines 13, no. 6: 448. https://doi.org/10.3390/machines13060448

APA Style

Xu, C., Lu, S., Zhang, Y., Zhang, L., Song, Z., Liu, H., Liu, Q., & Ren, L. (2025). Digital Twins for Defect Detection in FDM 3D Printing Process. Machines, 13(6), 448. https://doi.org/10.3390/machines13060448

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