Creep damage quantification in Ni-based LPBF alloys to improve creep performance and lifetime prediction

Wednesday, September 30, 2026: 9:00 AM
308A (Québec City Convention Centre)
Dr. Sebastien Dryepondt , Oak Ridge National Laboratory, Oak Ridge, TN
Dr. Holden C. Hyer , Oak Ridge National Laboratory, Oak Ridge, TN
Dr. Rahul Franklin , Oak Ridge National Laboratory, Oak ridge, TN
Dr. Amir Ziabari , Oak Ridge National Laboratory, Oak Ridge, TN
Dr. Patxi Fernandez Zelaia , Oak Ridge National Laboratory, Knoxville, TN
Mr. Chase Joslin , Oak Ridge National Laboratory, Oak Ridge, TN
The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the deployment of novel materials and manufacturing methods for nuclear systems. Creep performance is a key requirement for high temperature structural materials and testing was conducted on four Ni-based alloys (718, 625, 282 and 244) fabricated by laser powder bed fusion at 600°C-750°C, with long-term data generated on as printed and annealed alloy 718 and 625. Time-to-rupture for these low-defect LPBF materials was comparable to nominally similar wrought counterparts; however, strain at rupture was less than 10%, even after recrystallization heat treatment. Metallography identified creep cavitation as the dominant damage mechanism. To quantify cavity nucleation and growth, X-ray computed tomography (XCT) was applied to interrupted creep tests of LPBF 625 at 725°C. Three-dimensional cavity segmentation enabled tracking of cavity number density and size distributions as a function of creep exposure, revealing pronounced differences between short-term and long-term tests. To directly examine cavity evolution at grain boundaries, additional creep tests were conducted in vacuum on flat LPBF 625 specimens polished to a mirror finish. Interrupted tests confirmed the critical roles of grain boundary precipitates in cavitation nucleation and grain boundary orientation with respect to the loading direction. Finally, we outline an analysis workflow that combines automated (AI-assisted) characterization of large XCT/microstructure datasets with physics-based creep life models to accelerate qualification of additively manufactured high-temperature materials for nuclear reactors.