Editors’ Choice—Review—Sensor-Based and Computational Methods for Error Detection and Correction in 3D Printing
Article
Mehta, P, Mujawar, MA, Lafrance, S et al. (2024). Editors’ Choice—Review—Sensor-Based and Computational Methods for Error Detection and Correction in 3D Printing
. 3(3), 10.1149/2754-2726/ad7a88
Mehta, P, Mujawar, MA, Lafrance, S et al. (2024). Editors’ Choice—Review—Sensor-Based and Computational Methods for Error Detection and Correction in 3D Printing
. 3(3), 10.1149/2754-2726/ad7a88
Highlights Additive manufacturing revolutionizes the manufacturing sector with design freedom and rapid prototyping, but faces challenges from errors like layer shifting, under extrusion, and surface imperfections that affect print success and mechanical integrity. State-of-the-art sensor-based error detection methods in 3D printing, including vision-based, fluctuation-based, and model-based approaches for identifying and analyzing various printing defects. Utilizes machine learning, computer vision, and mathematical modeling alongside diverse sensors (e.g., cameras, thermocouples, load cells) to enhance the accuracy and real-time detection of printing errors. Proposes a comprehensive framework that combines advanced error detection and correction techniques, aiming to improve the efficiency and yield of additive manufacturing processes. Emphasizes the necessity for ongoing advancements in error detection and correction methods to ensure high-quality, reliable, and precise 3D printed parts, laying a foundation for the future of additive manufacturing.
