Publication Date
1-1-2026
Document Type
Conference Proceeding
Publication Title
Procedia CIRP
Volume
138
DOI
10.1016/j.procir.2026.01.104
Abstract
Although ceramic additive manufacturing technologies have greatly advanced over the past decade, they are still far from perfect. Even small printing defects can have a severe impact on the mechanical properties of additively manufactured ceramic parts due to their low fracture toughness. The objective of this study was to detect these defects during the printing process for a new ceramic additive manufacturing technique, called ceramic on-demand extrusion (CODE). Images were collected, pre-processed, augmented, and separated based on their distinctive classes (i.e., type of defects). Deep convolutional neural networks were developed to identify multiple types of defects in real-time.
Keywords
3D Printing, Advanced ceramics, Defect detection, Machine learning, Process monitoring
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Mechanical Engineering
Recommended Citation
Dylan Doan, Amir Armani, and Marcia Golmohamadi. "Real-time Defect Detection in Ceramic Additive Manufacturing Using Deep Convolutional Neural Networks" Procedia CIRP (2026). https://doi.org/10.1016/j.procir.2026.01.104
