Affiliation:
1. Physical Intelligence Department Max Planck Institute for Intelligent Systems 70569 Stuttgart Germany
2. Institute for Biomedical Engineering ETH Zürich Zürich 8092 Switzerland
3. School of Medicine and College of Engineering Koç University Istanbul 34450 Turkey
Abstract
AbstractBioinspired fibrillar structures are promising for a wide range of disruptive adhesive applications. Especially micro/nanofibrillar structures on gecko toes can have strong and controllable adhesion and shear on a wide range of surfaces with residual‐free, repeatable, self‐cleaning, and other unique features. Synthetic dry fibrillar adhesives inspired by such biological fibrils are optimized in different aspects to increase their performance. Previous fibril designs for shear optimization are limited by predefined standard shapes in a narrow range primarily based on human intuition, which restricts their maximum performance. This study combines the machine learning‐based optimization and finite‐element‐method‐based shear mechanics simulations to find shear‐optimized fibril designs automatically. In addition, fabrication limitations are integrated into the simulations to have more experimentally relevant results. The computationally discovered shear‐optimized structures are fabricated, experimentally validated, and compared with the simulations. The results show that the computed shear‐optimized fibrils perform better than the predefined standard fibril designs. This design optimization method can be used in future real‐world shear‐based gripping or nonslip surface applications, such as robotic pick‐and‐place grippers, climbing robots, gloves, electronic devices, and medical and wearable devices.
Subject
Biomaterials,Biotechnology,General Materials Science,General Chemistry