UW Composites Group

I am an assistant professor in the Materials Science and Engineering department at the University of Washington, specializing in advanced composite materials. My research team merges materials science, data science, and advanced manufacturing, focusing on three main areas: 1) Smart testing methods combining theory-guided machine learning and traditional techniques; 2) Smart manufacturing using automation, sensing, and machine learning; 3) Smart engineering methods to accelerate aerospace certification and qualification through machine learning and simulations. Our research is anchored in a novel machine learning framework that blends probabilistic and deterministic approaches, integrating engineering data and physical laws. This approach has led to several patented AI innovations for the aerospace sector.

research activities

Recent Linkedin Posts

I'm thrilled to announce a special issue of Composites Part A journal on Machine Learning and AI in Composite Science and Manufacturing. Looking forward to your submissions by 30-Nov-2024...

It was great meeting my friend Babak Raeisinia, Co-founder of the amazing Machina Labs, at the AIAA Scitech2024 Conference. We both graduated from the University of British Columbia and worked in the same building...

Exciting activities are happening at UW Advanced Composites Center. We have just received our carbon fiber composite tool for Automated Fiber Placement (AFP) production ...

It was such a pleasant experience introducing about 1,000 freshman engineers at University of Washington to manufacturing and automation, along with two other great panelists ...

Recent Youtube Videos

Materials Science and Engineering: Transforming Aerospace Manufacturing

AI in Aerospace: From Material Innovation to Autonomy in Factories and Flight

Research Spotlight: Autonomous Robots & AI for Advanced Materials Testing

Research Spotlight: Machine learning for real-time optimization and active control of composites autoclave processing

Research Spotlight: Automated evaluation of tool surface condition in composites manufacturing using machine learning

Recent Awards

Navid Zobeiry recives the certificate for Boeing Distinguished Researcher & Scholar Seminar Series (B-DRASS).

Caleb Schoenholz takes SAMPE URS third Best Paper Award.

Online Courses

This 8-lecture series on composite structural engineering is designed for training the next generation of aerospace engineers, focusing on the certification of composite civil aircraft structures per FAA curriculum standards. It covers topics such as material and process development, defects and damage, material allowables, design development, and structural substantiation.

Selected Publications

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.

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.

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.

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.

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.

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.

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.

Reiner, J., Vaziri, R., & Zobeiry, N. (2021). Machine learning assisted characterisation and simulation of compressive damage in composite laminates. Composite Structures, 273, 114290.

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.

Zobeiry, N., Lee, A., & Mobuchon, C. (2020). Fabrication of transparent advanced composites. Composites Science and Technology, 197, 108281.

Zobeiry, N., Reiner, J., & Vaziri, R. (2020). Theory-guided machine learning for damage characterization of composites. Composite Structures, 246, 112407.