Comparison of In-situ Nondestructive Testing and Ex-situ Methods in Additive Manufactured Specimens for Internal Feature Detection
Source abstract
Abstract
The effectiveness and repeatability of Additive Manufacturing (AM) technologies has been often measured through rigorous testing, both in-situ and ex-situ. In-situ nondestructive testing (NDT) has been used to understand the AM process and detect anomalies during the build. Ex-situ testing has been used for material characterization and internal defect detection. In this work, in-situ NDT and ex-situ testing methods are compared as applied to a Fused Filament Fabrication (FFF) process to detect defects that have formed internally in the part. Intentionally embedded features and defects are monitored and analyzed in-situ and ex-situ. Results from an automated image-based real-time defect detection system, an ultrasound ex-situ system, and a tensile testing system are compared to detect defects and their effects on mechanical properties. Real-time image-based defect detection is found to effectively spot defects as they form and allow the potential for implementing defect correction methods, while ex-situ tensile testing can be used as a form of validation to in-situ results.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Composite Materials and Mechanics Laboratory, Mechanical and Aerospace Engineering Department, New York University, Tandon School of Engineering, Brooklyn, New York, USA
Funding
American Society for Nondestructive Testing
References (27)
Show source-provided references
1. 10.1016/j.polymertesting.2022.10758110.1016/j.polymertesting.2022.107581 ↗
2. 10.1016/j.polymertesting.2022.10785910.1016/j.polymertesting.2022.107859 ↗
3. 10.1016/j.ijfatigue.2019.10528710.1016/j.ijfatigue.2019.105287 ↗
4. 10.1016/j.matdes.2022.11106310.1016/j.matdes.2022.111063 ↗
5. 10.1016/j.addma.2023.10348310.1016/j.addma.2023.103483 ↗
6. 10.1016/j.addma.2020.10133610.1016/j.addma.2020.101336 ↗
7. 10.1007/s11837-020-04291-510.1007/s11837-020-04291-5 ↗
8. 10.1016/j.rcim.2017.07.00110.1016/j.rcim.2017.07.001 ↗
9. 10.1016/j.addma.2021.10232810.1016/j.addma.2021.102328 ↗
10. 10.1016/j.addma.2021.10232810.1016/j.addma.2021.102328 ↗
11. 10.3390/met1001010310.3390/met10010103 ↗
12. 10.3390/ma1415416810.3390/ma14154168 ↗
13. 10.1007/s11837-020-04469-x10.1007/s11837-020-04469-x ↗
14. 10.1007/s00170-020-06344-010.1007/s00170-020-06344-0 ↗
15. 10.1016/j.addlet.2022.10010410.1016/j.addlet.2022.100104 ↗
16. G. L. Chen and N. Gupta, Image processing and machine learning methods applied to additive manufactured composites for defect detection and toolpath reconstruction, in Machine Learning Applied to Composite Materials, edited by V. Kushvaha, M. R. Sanjay, P. Madhushri, and S. Siengchin (Springer Nature, Singapore, 2021), pp. 19–44.
17. 10.1007/s10921-020-00721-110.1007/s10921-020-00721-1 ↗
18. 10.1016/j.matchar.2015.02.00810.1016/j.matchar.2015.02.008 ↗
19. 10.1016/j.csndt.2015.09.00110.1016/j.csndt.2015.09.001 ↗
20. 10.1109/LES.2022.314041710.1109/LES.2022.3140417 ↗
21. E. C. Koskelo and E. B. Flynn, Scanning laser ultrasound and wavenumber spectroscopy for in-process inspection of additively manufactured parts, in Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure (Las Vegas, Nevada, USA, 2016), pp. 980418.
22. 10.1016/j.ndteint.2020.10234010.1016/j.ndteint.2020.102340 ↗
23. 10.1016/j.prostr.2017.12.01610.1016/j.prostr.2017.12.016 ↗
24. 10.1007/s10853-008-3165-110.1007/s10853-008-3165-1 ↗
25. 10.1109/TUFFC.2006.12310.1109/TUFFC.2006.123 ↗
26. N. Gupta et al., On the associated problems of using ultrasonic imaging in characterizing closed cell rigid foam and their sandwich configurations. J. Non-Destruct Evalu, Indian Soci Non-Destruct Testing 18(3–4), 27–32 (1998).
27. 10.1016/j.addma.2018.03.02010.1016/j.addma.2018.03.020 ↗