Project Overview
This project addressed the urgent need for fast, effective characterization processes that can be applied to the complex 3D objects which are formed with increasing sophistication by advanced manufacturing (AM) methods. Objects formed by laser powder bed fusion (LPBF) and direct energy deposition (DED) are of particular interest for metals, for example. In this work we focused on three modalities: x-ray, neutron and ultrasound, as tools particularly relevant for AM objects, due to their ability to penetrate realistic objects of interest, and their mutual complementarity. We demonstrated that application of two characterization modes on the same object is a powerful tool that can be broadly applied to AM objects for quantitative assessment of surface location and composition. Furthermore, we showed that uncertainty quantification is an important enabling capability, since uncertainty information is essential for rapid evaluation and improvement of manufacturing processes. Ultimately, manufactured objects must hit performance targets that, in many cases, must be assured with information obtained by nondestructive 3D characterization processes such as those developed during this project.
Mission Impact
This work directly supported the LLNL Core Competency of Advanced Materials and Manufacturing by creating the scientific foundation for new metrology systems that can provide time-critical information on fabricated components to accelerate the development of advanced manufacturing systems. It also supported the Mission Focus Area of Nuclear Weapons Stockpile Stewardship because of the characterized materials and structures. These new capabilities have potential to inform problems related to national security by enabling rapid characterization of unknown and potentially hazardous structures and components, supporting the Mission Focus Area of All-WMD (Weapons of Mass Destruction) Threat Reduction.
Publications, Presentations, and Patents
Oksuz, I., et al., 2020. "Characterization of a Reactor-Based Fast Neutron Beam Facility for Fast Neutron Imaging." (Proceedings) Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXII; 114940T (2020);
https://doi.org/10.1117/12.2569964 LLNL-PROC-814265.Cherepy, N. J., et al., 2022. "Scintillators and Detectors for MeV X-ray and Neutron Imaging." SPIE Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXII; 114940N. doi: 10.1117/12.2569519. LLNL-PROC-814596.Townsend A., et al., 2020. "Parametrically Designed Surface Topography on CAD Models of Additively Manufactured Lattice Structures for Improved Design Validation."
Additive Manufacturing (2020). doi: 10.1016/j.addma.2020.101731. LLNL-JRNL-812397.Keene, L., 2021. "A Range and Performance Optimized Version of the Computer-Aided Speckle Interferometry Algorithm for Real-Time Displacement-Strain Field Monitoring."
Experimental Techniques 46:1027–1048 (2022). LLNL-JRNL-824682.Oksuz, I., et al., 2021. "Fast Neutron Computed Tomography of Multi-Material Complex Objects." SPIE Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII, 11838, 118380 (2021).
https://doi.org/10.1117/12.2595862. LLNL-CONF-827020.Cherepy, N. J., et al., 2021. "Lens-coupled MeV X-Radiography and CT with Transparent Ceramic GLO Scintillators."
SPIE Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII, 11838, 118380P (2021).
https://doi.org/10.1117/12.2595956. LLNL-PROC-825973.Hardy, A. J., et al., 2021. "Improved X-ray Computed Tomography (CT) Feature Identification with Complementary Fast Neutron CT." Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII 11838, 118380K (2021).
https://doi.org/10.1117/12.2595919. LLNL-PROC-824740.Leach, W., et al., 2022. "Fourier Method for 3-Dimensional Data Fusion of X-ray Computed Tomography and Ultrasound."
NDT & E International 127, 102600 (2022). ISSN 0963-8695.
https://doi.org/10.1016/j.ndteint.2021.102600 LLNL-JRNL-825755.Kerr, P. L., et al., 2022. "Neutron Transmission Imaging with a Portable D-T Neutron Generator."
Radiation Detection Technology Methods 6, 234-243 (2022). doi: 10.1007/s41605-022-00315-7. LLNL-TR-826034.Oksuz, I., et al., 2022. "Quantifying Spatial Resolution in a Fast Neutron Radiography System."
Nuclear Instruments and Methods in Physics Research A, 1027, 166331 (2022).
https://doi.org/10.1016/j.nima.2022.166331. LLNL-JRNL-827019.Oksuz, I., et al., 2022. "Comparison of Thermal and Fast Neutron Computed Tomography of Complex Objects." Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIV 12241-28 (2022). LLNL-PROC-839631. Bisbee, M.G. , et al., 2022. "Experimental X-ray and Fast Neutron CT Comparative Analysis." SPIE Optics and Photonics - Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIV, 12241-29 LLNL-PROC-839475.Klacansky, P., et al., 2022. "Virtual Inspection of Additively Manufactured Parts." IEEE PacificVis 2022. 10.1109/PacificVis53943.2022.00017.Karimi, S., et al., 2022. "Cross-Talk and Spatial Resolution on a Flat Panel-Based CT System." Developments in X-ray Computed Tomography XV, International Society for Optics and Photonics (2022). LLNL-CONF-837154.Mukherjee, S., et al., 2022. "Uncertainty Quantification in Immersion Ultrasound Measurements Using a Bayesian Inferencing Approach." ASNT Research Symposium 2022. LLNL-PROC-832220.
