Identifying build orientation of 3D-printed materials using convolutional neural networks
The advent of additive manufacturing (AM) processes brought with it intense research into various materials and manufacturing processes. At the same time, the need for validation of material properties, as well as study and forecasting of aging, has arisen. Modern imaging techniques, like X-ray computed tomography (XCT), are a convenient vehicle for such studies; however, the large datasets they produce require novel analysis techniques to efficiently extract critical information. Here, we present our work on developing a 3D extension of the ResNet architecture to distinguish between two build orientations of tensile bars produced by AM. Using only information from XCT, our method achieves a 99.3% correct classification at a misidentification of 1%.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF)
- Grant/Contract Number:
- AC05-76RL01830; DGE-1633216
- OSTI ID:
- 1834047
- Report Number(s):
- PNNL-SA-153051
- Journal Information:
- Statistical Analysis and Data Mining, Vol. 14, Issue 6; ISSN 1932-1864
- Publisher:
- WileyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models
Algorithm-Based Fault Tolerance for Convolutional Neural Networks