Editors’ Choice—Review—Sensor-Based and Computational Methods for Error Detection and Correction in 3D Printing

Abstract

Additive manufacturing in the realm of 3D printing has transformed the manufacturing sector, providing unmatched freedom in design and rapid prototyping abilities. However, a significant obstacle hindering its broader acceptance is the susceptibility to errors. These errors can take diverse forms, including layer shifting, under extrusion, and surface imperfections, ultimately resulting in unsuccessful prints or parts with weakened mechanical characteristics. Advanced error detection and correction methods are crucial for ensuring reliability and enhancing productivity. This paper reviews the current state-of-the-art in error detection techniques using various sensors in 3D printing, summarizing vision-based and fluctuation-based approaches for data collection and the use of a model-based approach for data interpretation. It further describes these techniques enable correction in 3D printing through sensor calibration, predictive modeling, specialized tools, and equipment specific techniques. The paper concludes by proposing a novel approach of combining advanced error detection and correction techniques into a comprehensive end-to-end error detection and correction methodology as a foundational building block for significantly improving the efficiency and yield in additive manufacturing processes.

Keywords

• 3D printing
• error detection
• error correction
• additive manufacturing
• computer vision
• artificial intelligence