Contact Aging Enhances Adhesion of Micropatterned Silicone Adhesives to Glass Substrates

The transfer of biological concepts into synthetic micropatterned adhesives has recently enabled a new generation of switchable, reversible handling devices. Over the last two decades, many design principles have been explored that helped to understand the underlying mechanics and to optimize such adhesives for certain applications. An aspect that has been overlooked so far is the influence of longer hold times on the adhesive contacts. Exemplarily, the pull‐off stress and work of separation of a micropatterned adhesive specimen are enhanced by factors 3 and 6, respectively, after 1000 min in contact with a glass substrate. In addition to such global measures, the increase of adhesion of all individual micropillars is analyzed. It is found that contact aging varied across the microarray, as it drastically depends on local conditions. Despite great differences on the micropillar scale, the adhesion of entire specimens increased with very similar power laws, as this is determined by the mean contact ageing of the individual structures. Overall, contact aging must be critically evaluated before using micropatterned adhesives, especially for long‐term fixations and material combinations that are chemically attractive to each other

Preventing Catastrophic Failure of Microfibrillar Adhesives in Compliant Systems Based on Statistical Analysis of Adhesive Strength

Adhesives based on fibrillar surface microstructures have shown great potential for handling applications requiring strong, reversible, and switchable adhesion. Recently, the importance of the statistical distribution of adhesive strength of individual fibrils in controlling the overall performance was revealed. Strength variations physically correspond to different interfacial defect sizes, which, among other factors, are related to surface roughness. For analysis of the strength distribution, Weibull's statistical theory of fracture was introduced. In this study, the importance of the statistical properties in controlling the stability of attachment is explored. Considering the compliance of the loading system, we develop a stability criterion based on the Weibull statistical parameters. It is shown that when the distribution in fibril adhesive strength is narrow, the global strength is higher but unstable detachment is more likely. Experimental variation of the loading system compliance for a specimen of differing statistical properties shows a transition to unstable detachment at low system stiffness, in good agreement with the theoretical stability map. This map serves to inform the design of gripper compliance, when coupled with statistical analysis of strength on the target surface of interest. Such a treatment could prevent catastrophic failure by spontaneous detachment of an object from an adhesive gripper. © 2021 The Authors. Published by American Chemical Society

Application of machine learning to object manipulation with bio-inspired microstructures

Bioinspired fibrillar adhesives have been proposed for novel gripping systems with enhanced scalability and resource efficiency. Here, we propose an in-situ optical monitoring system of the contact signatures, coupled with image processing and machine learning. Visual features were extracted from the contact signature images recorded at maximum compressive preload and after lifting a glass object. The algorithm was trained to cope with several degrees of misalignment and with unbalanced weight distributions by off-center gripping. The system allowed an assessment of the picking process for objects of various mass (200, 300, and 400 g). Several classifiers showed a high accuracy of about 90 % for successful prediction of attachment, depending on the mass of the object. The results promise improved reliability of handling objects, even in difficult situations

Application of machine learning to object manipulation with bio-inspired microstructures

Bioinspired fibrillar adhesives have been proposed for novel gripping systems with enhanced scalability and resource efficiency. Here, we propose an in-situ optical monitoring system of the contact signatures, coupled with image processing and machine learning. Visual features were extracted from the contact signature images recorded at maximum compressive preload and after lifting a glass object. The algorithm was trained to cope with several degrees of misalignment and with unbalanced weight distributions by off-center gripping. The system allowed an assessment of the picking process for objects of various mass (200, 300, and 400 g). Several classifiers showed a high accuracy of about 90 % for successful prediction of attachment, depending on the mass of the object. The results promise improved reliability of handling objects, even in difficult situations