Navid Zobeiry

Biography

Dr. Navid Zobeiry is an associate professor in the Materials Science and Engineering department at the University of Washington, with an adjunct position in the Aeronautics and Astronautics department. His research focuses on the intersection of materials science, data science, and advanced manufacturing, working in close collaboration with aerospace manufacturers and materials suppliers. Zobeiry’s work addresses three main areas: 1) Smart testing methods that integrate physics-informed machine learning with traditional characterization techniques; 2) Smart manufacturing methods that leverage automation, sensing, model-based engineering, and machine learning; and 3) Smart engineering methods that focus on uncertainty quantification to accelerate aerospace part and process certification and qualification, using machine learning and physics-based simulations. A central feature of his research is a novel machine learning framework that combines probabilistic and deterministic approaches, seamlessly integrating model-based engineering data, targeted testing, and physical laws. This innovative approach has led to the development of several patented AI software solutions, with some being exclusively licensed to notable aerospace companies.

Education

• University of British Columbia
• University of British Columbia
• University of Tehran

Previous appointments

• Assistant Professor, University of Washington
• Research Associate and Lecturer, University of British Columbia

Select publications

1. Schoenholz, C., Zappino, E., Petrolo, M., & Zobeiry, N. (2024). Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning. Composites Part B: Engineering, 280, 111499.
2. Wynn, M., Oster, L., Chase, G., Salviato, M., & Zobeiry, N. (2024). Assessment of the effect of processing parameters on peel failure of laser-assisted automated fiber placed thermoplastic composites. Manufacturing Letters, 40, 93-96.
3. Picazo, P. P., Cheng, R., Gray, A., & Zobeiry, N. (2024). A Machine Learning-based Accelerated Pyrolysis Characterization and Optimization of High-temperature Composites. SAMPE Journal, 60(2).
4. Schoenholz, C., & Zobeiry, N. (2024). Investigating the Impacts of Processing Variability on Tool-part Interaction for Interply-toughened Aerospace Composites using a Novel Shear Technique. Composites Part A: Applied Science and Manufacturing, 178, 107973.
5. Schoenholz, C., & Zobeiry, N. (2024). An Accelerated Process Optimization Method to Minimize Deformations in Composites Using Theory-guided Probabilistic Machine Learning. Composites Part A: Applied Science and Manufacturing, 176, 107842.
6. Schoenholz, C., Li, S., Bainbridge, K., Huynh, V., Gray, A., & Zobeiry, N. (2023). Accelerated In Situ Inspection of Release Coating and Tool Surface Condition in Composites Manufacturing Using Global Mapping, Sparse Sensing, and Machine Learning. Journal of Manufacturing and Materials Processing, 7(3), 81.
7. Byar, A. D., Kabir, M. H., Stere, A. I., Doty, C., Joseph, A., & Zobeiry, N. (2023). Simulation Model Validation for Structure Material Characterization. U.S. Patent Application No. 17/648,526.
8. Kabir, M. H., Byar, A. D., Stere, A. I., DePauw, T. C., Doty, C. M., Joseph, A. P. K., & Zobeiry, N. (2023). Outlier detection and management. U.S. Patent Application No. 17/654,087.
9. Wynn, M., & Zobeiry, N. (2022). Investigating the Effect of Temperature History on Crystal Morphology of Thermoplastic Composites Using In Situ Polarized Light Microscopy and Probabilistic Machine Learning. Polymers, 15(1), 18.
10. Freed, Y., Salviato, M., & Zobeiry, N. (2022). Implementation of a probabilistic machine learning strategy for failure predictions of adhesively bonded joints using cohesive zone modeling. International Journal of Adhesion and Adhesives, 118, 103226.
11. Lee, A., Wynn, M., Quigley, L., Salviato, M., & Zobeiry, N. (2022). Effect of temperature history during additive manufacturing on crystalline morphology of PEEK. Advances in Industrial and Manufacturing Engineering, 4, 100085.
12. Humfeld, K. D., Gu, D., Butler, G. A., Nelson, K., & Zobeiry, N. (2021). A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control. Composites Part B: Engineering, 223, 109150.
13. Reiner, J., Vaziri, R., & Zobeiry, N. (2021). Machine learning assisted characterisation and simulation of compressive damage in composite laminates. Composite Structures, 273, 114290.
14. Zobeiry, N., & Humfeld, K. D. (2021). A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Engineering Applications of Artificial Intelligence, 101, 104232.
15. Zobeiry, N., Lee, A., & Mobuchon, C. (2020). Fabrication of transparent advanced composites. Composites Science and Technology, 197, 108281.
16. Zobeiry, N., Reiner, J., & Vaziri, R. (2020). Theory-guided machine learning for damage characterization of composites. Composite Structures, 246, 112407.

Honors & awards

• Certificate of Appreciation, 2024, Boeing Distinguished Researcher & Scholar Seminar Series (B-DRASS), Boeing Company, WA
• National 3rd Best Paper Award, 2022, SAMPE URS, "An automated evaluation of tool surface condition in composites manufacturing with machine learning & sparse sensing", USA.
• Teaching Faculty of the Year Award by a vote of the junior class, Materials Science and Engineering Department, University of Washington, 2020
• Certificate of Appreciation, 2019, Boeing Distinguished Researcher & Scholar Seminar Series (B-DRASS), Boeing Company, WA
• UBC President's Recognition Award, 2009, Outstanding Contributions to Student Leadership & Learning, Access and Diversity Center, Vancouver, Canada

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