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Auxetic Advancement

Transforming Biomedical Engineering

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0.5s design compilation time
4.5s model generation time
Fabrication of high-resolution structures up to 100μm

The Challenge

Traditional additive manufacturing methods are limited in their ability to produce intricate structures with tailored mechanical properties. Recognizing this challenge, Hyperganic collaborated with the Department of Health Sciences and Technology at ETH Zürich University to overcome the complexities inherent in designing and fabricating constructs for biomedical purposes.

Algorithmic engineering is used to rapidly design and fabricate complex constructs

The Process

Algorithmic engineering played a pivotal role in the overall process, as the algorithms used were able to process a wide array of point-point connections, allowing the team to computationally model auxetic meshes and cylinders at an average duration of ≈4.5 s. Additionally, the project integrated advanced computational modeling within a deep learning framework to rapidly screen the mechanical properties of large arrays of complex architectures. This innovative method of integration ensured that interconnections between constitutive points or contours were satisfied, even as shape complexity increased.

The Results

The end product is a line of 3D-printed helmets uniquely tailored to each cyclist's head, offering unmatched safety and comfort. The use of algorithmic design not only made it possible to adjust internal structures for individual fit but also to enhance aerodynamics and reduce material use without compromising safety. This innovative approach to helmet manufacturing sets a new industry standard, demonstrating the tangible benefits of integrating Algorithmic Engineering and 3D printing in consumer goods production.

Rapid design and fabrication of perfusable constructs, which were then printed with Rhod-labeled resin surrounding the alveolar construct