Researchers at UBC Okanagan have come up with an easier way to examine the complex structure of fibres and multiscale materials, helping to ensure newly developed composites won’t fail under excessive loads.
Using materials informatics and machine learning, the team has uncovered a new way to analyze the effectiveness of state-of-the-art fabric composites used in aerospace, construction, automotive and sports industries.
The complex structures and configurations of these composites—while making them more durable and functional—are challenging to analyze, explains Dr. Abas Milani, a Professor in UBC Okanagan’s School of Engineering and founding Director of the Materials and Manufacturing Research Institute.
Fabric composites are interwoven materials that provide a lightweight, stronger and often more formable alternative to simpler one-dimensional composite materials, he explains. Understanding the relationship between the geometry of these materials and their microstructural properties helps engineers to build a composite based on how they want the material to perform in the real world.
“For example, if we want the wings of an aircraft to resist specific high shear forces, building a composite material with a particular microstructure will help us achieve that,” he explains.
The UBC research team, including doctoral student Tina Olfatbakhsh, was able to connect the images of the fabric material structure to its mechanical property through the use of smart technologies and machine learning.
“Experimental or numerical modelling techniques are effective tools, but they are time-consuming and require expensive devices or high-power computers,” says Olfatbakhsh, co-author of the study. “They also often assume the material geometry to be perfect, although, in the actual manufacturing process, textile composites can have many different internal complexities like waviness, voids and even fibre misalignment. This complicates matters significantly.”
The proposed method enables researchers to capture the details in the material microstructure by advanced X-ray imaging techniques and making predictions about the material property only based on the images. This information can also be fed into a large materials database.
This database is a good opportunity to exchange knowledge with scientists around the world to prevent doing repetitive tests and analysis, explains Olfatbakhsh. Now, whenever they need a specific performance, they know which material arrangement to choose using this database.
Olfatbakhsh is the lab manager of the Composite Research Network’s (CRN) Okanagan Node. CRN is a collaboration of academic and industry partners that support the composites industry in Canada and beyond.
“As manufacturers develop more innovative composite materials that are formulated at the micro-scale, our testing needs to keep pace so we can ensure the integrity and strength of these new microstructures,” says Dr. Milani, principal researcher at CRN’s Okanagan Node. “Here at CRN, we are using X-ray computed tomography to non-destructively capture high-resolution 3D images of composite specimens to study their internal structure.”
Olfatbakhsh says the new approach is accurate, effective and applicable to existing manufacturing processes.
“By streamlining the analysis using machine learning techniques, we are making great strides towards a framework for smart, data-driven design and optimization of woven fabric composites,” she adds. “Our findings are a promising step forward for the smart design of next-generation tactile composites, especially in prominent industries like aerospace and transportation.”
The research was published in Composites Science and Technology, and funded by the Natural Sciences and Engineering Research Council of Canada.