Bio
Zackary Snow is an associate research staff member at Oak Ridge National Lab in the Manufacturing Systems Analytics Group of the Manufacturing Science Division. His research interests include the use of process sensing and machine learning to understand and improve advanced manufacturing processes, non-destructive inspection, integration and utilization of material characterization data into digital manufacturing paradigms, and in situ material property prediction. Zackary received his PhD from The Pennsylvania State University in Engineering Science and Mechanics and has authored multiple publications in peer-reviewed journals related to flaw formation mechanisms and in situ sensing of additive manufacturing processes.
Education
Doctor of Philosophy, Engineering Science and Mechanics
The Pennsylvania State University
Dissertation Title: Flaws in Powder Bed Fusion Additive Manufacturing: Formation Mechanisms, Detection Methods, and Effect on Fatigue
Minor: Additive Manufacturing and Design
Master of Science, Mechanical Engineering
The Pennsylvania State University
Thesis Title: Understanding Powder Spreadability in Powder Bed Fusion Additive Manufacturing
Bachelor of Science, Mechanical Engineering
Virginia Polytechnic Institute and State University
Minors: Engineering Science and Mechanics, Biomedical Engineering
Other Publications
(1) Ziabari, A.; Venkatakrishnan, S.; Snow, Z.; Lisovich, A.; Sprayberry, M.; Brackman, P.; Frederick, C.; Bhattad, P.; Graham, S.; Bingham, P.; others. Enabling Rapid X-Ray CT Characterisation for Additive Manufacturing Using CAD Models and Deep Learning-Based Reconstruction. npj Computational Materials 2023, 9 (1), 91.
(2) Snow, Z.; Cummings, C.; Reutzel, E. W.; Nassar, A.; Abbot, K.; Guerrier, P.; Kelly, S.; McKown, S.; Blecher, J.; Overdorff, R. Analysis of Factors Affecting Fatigue Performance of HIP’d Laser-Based Powder Bed Fusion Ti–6Al–4V Coupons. Materials Science and Engineering: A 2023, 144575. https://doi.org/10.1016/j.msea.2022.144575.
(3) Snow, Z.; Scime, L.; Ziabari, A.; Fisher, B.; Paquit, V. Observation of Spatter-Induced Stochastic Lack-of–Fusion in Laser Powder Bed Fusion Using In Situ Process Monitoring. Additive Manufacturing 2023, 61, 103298. https://doi.org/10.1016/j.addma.2022.103298.
(4) Snow, Z.; Reutzel, E. W.; Petrich, J. Correlating In-Situ Sensor Data to Defect Locations and Part Quality for Additively Manufactured Parts Using Machine Learning. Journal of Materials Processing Technology 2022, 302, 117476. https://doi.org/10.1016/j.jmatprotec.2021.117476.
(5) Petrich, J.; Snow, Z.; Corbin, D.; Reutzel, E. W. Multi-Modal Sensor Fusion with Machine Learning for Data-Driven Process Monitoring for Additive Manufacturing. Additive Manufacturing 2021, 48, 102364. https://doi.org/10.1016/j.addma.2021.102364.
(6) Bozek, E.; McGuigan, S.; Snow, Z.; Reutzel, E. W.; Rivière, J.; Shokouhi, P. Nonlinear Resonance Ultrasonic Spectroscopy (NRUS) for the Quality Control of Additively Manufactured Samples. NDT & E International 2021, 123, 102495. https://doi.org/10.1016/j.ndteint.2021.102495.
(7) Snow, Z.; Sundar, V.; Keist, J.; Jones, G.; Reed, R.; Reutzel, E. Flaw Identification in Additively Manufactured Parts Using X-Ray Computed Tomography and Destructive Serial Sectioning. J. of Materi Eng and Perform 2021, 30 (7), 4958–4964. https://doi.org/10.1007/s11665-021-05567-w.
(8) Snow, Z.; Diehl, B.; Reutzel, E. W.; Nassar, A. Toward In-Situ Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing through Layerwise Imagery and Machine Learning. Journal of Manufacturing Systems 2021, 59, 12–26. https://doi.org/10.1016/j.jmsy.2021.01.008.
(9) Snow, Z.; Nassar, A. R.; Reutzel, E. W. Invited Review Article: Review of the Formation and Impact of Flaws in Powder Bed Fusion Additive Manufacturing. Additive Manufacturing 2020, 36, 101457. https://doi.org/10.1016/j.addma.2020.101457.
(10) Snow, Z.; Martukanitz, R.; Joshi, S. On the Development of Powder Spreadability Metrics and Feedstock Requirements for Powder Bed Fusion Additive Manufacturing. Additive Manufacturing 2019, 28, 78–86. https://doi.org/10.1016/j.addma.2019.04.017.