Abbas Milani, professor of mechanical engineering at the University of British Columbia, and his research team are hoping to streamline these analyses through AI/machine learning.

The researchers took micro-CT scans of different woven fabrics at different angles and configurations. The images captured internal complexities, such as waviness, voids and fiber misalignment. Next came mechanical testing on 50 of those sample fabrics to determine the specific properties of each. This image and testing information was entered into a database.

The team then took micro images of other textile samples and, using AI/machine learning, successfully predicted their properties and performance by comparing them to the database images. The entire process of identifying the fabric properties, including imaging, took less than five minutes.

With this process, composites designers who want to use a particular material will someday be able to feed its images into the database to determine its likely properties.

“The inverse application would be for materials discovery,” says Milani. “If you want a particular property from a material that doesn’t exist, you could use the images to determine what its texture should be, what the fiber orientation should be.”

Milani, who serves as technical chair of Canada’s Composites Research Network, says several industry partners have expressed interest in the process.

Mary Lou Jay is a freelance writer based in Timonium, Md. Email comments to

Professor Abba Milani and doctoral student Tina Olfabakhsh at the University of British Columbia are predicting material properties by using AI to compare images taken from micro-CT scans of 50 sample materials to scans of other materials.

Photo Credit: University of British Columbia, Okanagan

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Photo Credit: Waruna Seneviratne, NIAR

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Photo Credit: Plyable

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Photo Credit: Plataine