Machine Learning-Based Shear Optimal Adhesive Microstructures with Experimental Validation

Bioinspired 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.

Machine Learning-Based and Experimentally Validated Optimal Adhesive Fibril Designs

Setae, fibrils located on a gecko's feet, have been an inspiration of synthetic dry microfibrillar adhesives in the last two decades for a wide range of applications due to unique properties: residue-free, repeatable, tunable, controllable and silent adhesion; self-cleaning; and breathability. However, designing dry fibrillar adhesives is limited by a template-based-design-approach using a pre-determined bioinspired T- or wedge-shaped mushroom tip. Here, a machine learning-based computational approach to optimize designs of adhesive fibrils is shown, exploring a much broader design space. A combination of Bayesian optimization and finite element methods creates novel optimal designs of adhesive fibrils, which are fabricated by two-photon-polymerization-based 3D microprinting and double-molding-based replication out of polydimethylsiloxane. Such optimal elastomeric fibril designs outperform previously proposed designs by maximum 77% in the experiments of dry adhesion performance on smooth surfaces. Furthermore, finite-element-analyses reveal that the adhesion of the fibrils is sensitive to the 3D fibril stem shape, tensile deformation, and fibril microfabrication limits, which contrast with the previous assumptions that mostly neglect the deformation of the fibril tip and stem, and focus only on the fibril tip geometry. The proposed computational fibril design could help design future optimal fibrils with less help from human intuition.