Fiber-reinforced composites (FRC), which are engineered materials comprising rigid fibers embedded in a flexible matrix, typically have a constant fiber radius which limits their performance. Now, researchers at the Gwangju Institute of Science and Technology in Korea have developed an AI-aided design scheme of FRC structures with spatially varying optimal fiber sizes, making FRCs lighter without compromising their mechanical strength and rigidity, which will reduce energy consumption. cars, planes and other vehicles.
Fiber-reinforced composites (FRC) are a class of sophisticated engineering materials composed of rigid fibers embedded in a flexible matrix. When properly designed, FRCs offer exceptional structural strength and rigidity for their weight, making them an attractive option for aircraft, spacecraft and other vehicles where a lightweight structure is essential.
Despite their usefulness, however, FRCs are limited by the fact that they are designed using fibers with constant radius and spatially fixed fiber density, which compromises the trade-off between weight and mechanical strength. Simply put, the FRCs currently available are, in fact, heavier than necessary to meet application standards.
To tackle this problem, an international research team led by Professor Jaewook Lee from the Gwangju Institute of Science and Technology in Korea recently developed a new approach for the reverse design of FCRs with a size and orientation of spatially varying fibers, also known as “functional grading”. composite materials.” The proposed method is based on a “multi-scale topology optimization”, which allows to automatically find the best functionally graded composite structure given a set of design parameters and constraints.
“Topology optimization is an AI-based design technique that relies on computer simulation to generate an optimal structural shape rather than designer intuition and experience,” says Professor Lee, “on the other hand, a multi-scale approach is a numerical method that combines the results of analyzes conducted at different scales to infer structural features.” Unlike similar existing approaches that are limited to functionally gradient two-dimensional composites, the proposed methodology can simultaneously determine the optimal three-dimensional composite structure along with its microscale fiber densities and fiber orientations.
The team demonstrated the potential of their method through several computer-aided experiments where various functional gradient composite designs with constant or varying fiber sizes were compared. The experiments included designs for a bell crank, a travel reverser mechanism, and a simple support beam. As expected, the results showed improved performance in designs with locally adapted fiber sizes. This article was posted on October 9, 2021 and published in volume 279 of Composite works on January 1, 2022.
Many applications for vehicles, aircraft and robotics benefit from lightweight structures, and the proposed approach can now help engineers to this end. However, the benefits can extend far beyond the target applications themselves. As Professor Lee explains: “Our methodology could help develop more energy-efficient vehicles and machines through weight reduction, which would reduce their energy consumption and, in turn, help achieve neutrality. carbon.”
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Materials provided by GIST (Gwangju Institute of Science and Technology). Note: Content may be edited for style and length.