Towards 4D CT in Additive Manufacturing: Evaluating Algorithmic Limitations and Opportunities for Industrial Non-Destructive Testing
Computed tomography (CT) is widely used in industrial applications for non-destructive testing (NDT), enabling the visualization and analysis of internal structures within components. As such, industrial CT is particularly valuable for defect detection and quality control. In the context of additiv...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-02-01
|
| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30715 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Computed tomography (CT) is widely used in industrial applications for non-destructive testing (NDT), enabling the visualization and analysis of internal structures within components. As such, industrial CT is particularly valuable for defect detection and quality control. In the context of additive manufacturing (AM) of metal components produced via powder bed fusion, defects such as inclusions or lack-of-fusion errors can occur. Traditionally, the analysis of these defects has been limited to the final state of the component, leaving the temporal evolution of defects unknown. However, by estimating the motion during production or fatigue testing for quality control, it becomes possible not only to detect defects but also to determine their origin, track their development over time, and understand the interactions responsible for their formation. Time-resolved CT scans provide a promising approach to visualize the behavior of structures under varying conditions, offering a valuable foundation for models that predict defect evolution. In our previous work [1], an algorithm was developed to model the motion between a static initial state and a final state, enabling the reconstruction of temporal intermediate volumes. This article builds on that approach by exploring its application under different conditions, such as varying motion sizes, in order to identify both the possibilities and limitations of the method. This work aims to lay a solid foundation for reliably extending the algorithm to dynamic states, with the ultimate goal of determining dynamic material properties, e.g., draw conclusions on the Wöhler curve by time-resolved investigation of pore evolution.
|
|---|---|
| ISSN: | 1435-4934 |