| Modeling of additive manufacturing processes for metals: Challenges and opportunities MM Francois, A Sun, WE King, NJ Henson, D Tourret, CA Bronkhorst, ... Current Opinion in Solid State and Materials Science 21 (4), 198-206, 2017 | 443 | 2017 |
| Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo TM Rodgers, JD Madison, V Tikare Computational Materials Science 135, 78-89, 2017 | 322 | 2017 |
| Process-structure linkages using a data science approach: application to simulated additive manufacturing data E Popova, TM Rodgers, X Gong, A Cecen, JD Madison, SR Kalidindi Integrating materials and manufacturing innovation 6, 54-68, 2017 | 134 | 2017 |
| Simulation and experimental comparison of the thermo-mechanical history and 3D microstructure evolution of 304L stainless steel tubes manufactured using LENS KL Johnson, TM Rodgers, OD Underwood, JD Madison, KR Ford, ... Computational Mechanics 61, 559-574, 2018 | 80 | 2018 |
| Predicting Mesoscale Microstructural Evolution in Electron Beam Welding TM Rodgers, JD Madison, V Tikare, MC Magure JOM 68 (5), 1419-1426, 2016 | 74 | 2016 |
| Simulation of powder bed metal additive manufacturing microstructures with coupled finite difference-Monte Carlo method TM Rodgers, D Moser, F Abdeljawad, ODU Jackson, JD Carroll, BH Jared, ... Additive Manufacturing 41, 101953, 2021 | 58 | 2021 |
| A Monte Carlo model for 3D grain evolution during welding TM Rodgers, JA Mitchell, V Tikare Modelling and Simulation in Materials Science and Engineering 25 (6), 064006, 2017 | 42 | 2017 |
| Direct numerical simulation of mechanical response in synthetic additively manufactured microstructures TM Rodgers, JE Bishop, JD Madison Modelling and Simulation in Materials Science and Engineering 26 (5), 055010, 2018 | 37 | 2018 |
| Three-dimensional additively manufactured microstructures and their mechanical properties TM Rodgers, H Lim, JA Brown JOM 72 (1), 75-82, 2020 | 29 | 2020 |
| Developing anisotropic yield models of polycrystalline tantalum using crystal plasticity finite element simulations H Lim, HJ Bong, SR Chen, TM Rodgers, CC Battaile, JMD Lane Materials Science and Engineering: A 730, 50-56, 2018 | 23 | 2018 |
| Microstructure of vapor deposited coatings on curved substrates TM Rodgers, H Zhao, HNG Wadley Journal of Vacuum Science & Technology A 33 (5), 2015 | 13 | 2015 |
| Vapor deposition on doublet airfoil substrates: Coating thickness control TM Rodgers, H Zhao, HNG Wadley Journal of Vacuum Science & Technology A 33 (6), 2015 | 12 | 2015 |
| Born qualified grand challenge LDRD final report RA Roach, N Argibay, K Allen, DK Balch, LL Beghini, JE Bishop, B Boyce, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); Sandia …, 2018 | 11 | 2018 |
| Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing JA Mitchell, F Abdeljawad, C Battaile, C Garcia-Cardona, EA Holm, ... Modelling and Simulation in Materials Science and Engineering 31 (5), 055001, 2023 | 10 | 2023 |
| Fast three-dimensional rules-based simulation of thermal-sprayed microstructures TM Rodgers, JA Mitchell, A Olson, DS Bolintineanu, A Vackel, NW Moore Computational Materials Science 194, 110437, 2021 | 10 | 2021 |
| Thermal barrier coating deposition by rarefied gas jet assisted processes: Simulations of deposition on a stationary airfoil TM Rodgers, H Zhao, HNG Wadley Journal of Vacuum Science & Technology A 31 (6), 2013 | 10 | 2013 |
| Characterization and modeling of microstructural stresses in alumina MC Teague, T Rodgers, S Grutzik, S Meserole Journal of the American Ceramic Society 101 (5), 2155-2161, 2018 | 9 | 2018 |
| Monotonic Gaussian process for physics-constrained machine learning with materials science applications A Tran, K Maupin, T Rodgers Journal of Computing and Information Science in Engineering 23 (1), 011011, 2023 | 6 | 2023 |
| Integrated computational materials engineering with monotonic gaussian processes A Tran, K Maupin, T Rodgers International Design Engineering Technical Conferences and Computers and …, 2022 | 3 | 2022 |
| Using Deep Neural Networks to Predict Material Types in Conditional Point Sampling Applied to Markovian Mixture Models. IV Davis, W Leon, AJ Olson, GA Popoola, DS Bolintineanu, T Rodgers, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | 3 | 2021 |
Jonathan MadisonSandia National LaboratoriesVerified email at sandia.gov
