Biography

Navid Zobeiry was previously a research associate and lecturer at the University of British Columbia where he led manufacturing and testing for the Composites Research Network, a collaboration of academic and industry partners aimed at improving composite manufacturing and design. Throughout his research career, he has collaborated extensively with aerospace and automotive manufacturers and materials suppliers on a wide range of topics including material and process characterization, process simulation and optimization, and manufacturing-induced defects.

Education

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

Previous appointments

• Research Associate and Lecturer, University of British Columbia

Research Statement

Despite significant progress in development of new material systems, advanced and semi-automated processing methods, and high-fidelity process simulation tools, Process-Structure-Property relationships in advanced composites are not well understood. This is perhaps not surprising considering the complex and multiscale nature of composite materials. To address current industry needs, my research is focused at the intersection of materials science, data science, and manufacturing technologies. By taking advantage of combined science-based methods and machine learning approaches, my research ultimately leads to the development of self-optimizing and agile factories of the future. Currently, I am focusing on the following research topics:

• Developing new and improved material systems
• Developing accurate characterization and in-situ monitoring methods
• Optimizing existing processing methods and mitigating manufacturing-induced defects
• Developing next generation of AI-enhanced manufacturing methods
• Developing high-fidelity and real-time process simulation tools

Select publications

1. 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, DOI: 10.1016/j.engappai.2021.104232
2. Reiner J., Xu X., Zobeiry N., Vaziri R., Hallett S., Wisnom M. (2021). Virtual characterization of nonlocal continuum damage model parameters using a high fidelity finite element model, Composite Structures, 256:113073.
3. Humfeld D., Zobeiry N. (2021). Machine learning-based process simulation approach for real-time optimization and active control of composites autoclave processing, SAMPE Conference, Society for the Advancement of Material and Process Engineering, to be held in Seattle, WA.
4. Kim M., Zobeiry N. (2021). Machine learning for reduced order modeling of composites processing, SAMPE Conference, Society for the Advancement of Material and Process Engineering, to be held in Seattle, WA.
5. Zobeiry N., Seaton C., Salviato M., Chen X., Banerjee A., Devasia S., Yang J., Blom-Schieber A., Buttrick J., Pedigo S. (2020). A factory-centric workforce development approach for aerospace industry, SAMPE Conference, Society for the Advancement of Material and Process Engineering, Seattle, WA.
6. Reiner J., Zobeiry N., Vaziri R. (2020). A stacked sublaminate-based damage-plasticity model for simulating progressive damage in composite laminates under impact loading, Thin-Walled Structures, 156:107009.
7. Zobeiry N., Lee A., Mobuchon C. (2020). Fabrication of Transparent Composites, Composites Science and Technology, 197:108281.
8. Zobeiry N., Reiner J., Vaziri R. (2020). Theory-guided machine learning for damage characterization of composites, Composite Structures, DOI: 10.1016/j.compstruct.2020.112407
9. Zappino E., Zobeiry N., Petroloa M., Vaziri R., Carreraa E., Poursartip A. (2020). Analysis of process-induced deformations and residual stresses in curved composite parts considering transverse shear stress and thickness stretching, Composite Structures, 241:112057.
10. Zobeiry N., Humfeld D. (2019). An iterative machine learning approach for discovery of theories underlying physical phenomena, arXiv preprint arXiv:1909.13718.
11. Farhang L., Mohseni M., Zobeiry N., Fernlund G. (2019). Experimental study of void evolution in partially impregnated prepregs, Journal of Composite Materials, 54(11):1511-1523.
12. Mohseni M., Zobeiry N., Fernlund G. (2019). Experimental and numerical study of coupled gas and resin transport and its effect on porosity in partially impregnated prepregs, Journal of Reinforced Plastics and Composites, 0731684419865783.
13. Malek S., Zobeiry N., Dai C., Vaziri R. (2019). Strain-softening response and failure prediction in notched oriented strand board (OSB), Journal of Materials in Civil Engineering, 31(6):04019094.
14. Zobeiry N., Park J., Poursartip A. (2019). an IR Thermography based method for the evaluation of the thermal response of tooling for composites manufacturing, Journal of Composite Materials, 53(10):1277-1290.
15. Zobeiry N., Duffner C. (2018). Measuring the negative pressure during processing of advanced composites, Composite Structures, 203:11-17.
16. Fabris J., Zobeiry N., Park J., Poursartip A. (2019). Effect of tool design on thermal management in composites processing, SAMPE Journal, 54(4):28-37.
17. Fernlund G., Mobuchon C., Zobeiry N. (2018). 2.3 Autoclave Processing, In: Beaumont P.W.R. and Zweben C.H. (eds.), Comprehensive Composite Materials II, 2:42-62, Oxford: Academic Press.
18. Farnand K., Zobeiry N., Poursartip A., Fernlund G. (2017). Micro-level mechanisms of fiber waviness and wrinkling during hot drape forming of unidirectional prepreg composites, Composites Part A: Applied Science and Manufacturing, 103:168-177
19. Zobeiry N., Forghani A., McGregor C., McClennan S., Vaziri R., Poursartip A. (2017). Effective calibration and validation of a nonlocal continuum damage model for laminated composites, Composite Structures, 173:188-195
20. Zobeiry N., Forghani A., Li C., Thorpe R., Gordnian K., Vaziri R., Fernlund G., Poursartip A. (2016). Multi-scale characterization and representation of composite materials during processing, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, DOI: 10.1098/rsta.2015.0278, 374:20150278

Honors & awards

• Certificate of Appreciation at the Boeing Distinguished Researcher and Scholar Seminar Series (B-DRASS), Boeing Company, Seattle, 2019
• Teaching Faculty of the Year Award by a vote of the junior class, Materials Science and Engineering Department, University of Washington, 2020

News