UW Composites Group

Welcome to the homepage of the Composites Group at the University of Washington, Seattle. At composites group, we take advantage of experimental data, numerical data, and theory-guided machine learning methods to accelerate innovation while reducing risk for the industry. Examples are accelerating the development of next generation of high performance material systems, optimizing fabrication process of advanced composites, and accelerating material qualification and part certification for aerospace industry.

research activities

Recent Posts

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 ...

Last week, my PhD student Caleb Schoenholz and I had the opportunity to visit Toray Composite Materials America, Inc., a premier facility of Toray Industries where aerospace materials ...

Recent Videos

Materials Science and Engineering: Transforming Aerospace Manufacturing

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

Selected Recent Publications

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.