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Scientific Unit
Polymer adhesive films sandwiched between two rigid solids are a common bonding strategy. The mechanics and consequently the adhesion of such geometrically confined films depend mainly on their thickness, Young's modulus, and the Poisson's ratio of the material. In this work, we explore the effect of a micropatterned subsurface embedded into the adhesive layer. We compare experiments with three-dimensional numerical simulations to evaluate the impact of the microstructure on the contact stiffness and effective modulus. The results are used to extend a previously proposed size scaling argument on adhesion from incompressible to slightly compressible films to account for the silicone used in our study with a Poisson's ratio of 0.495. In addition, interfacial stress distributions between the elastic film and the glass disc are obtained from plane strain simulations to evaluate characteristic adhesion failures such as edge cracks and cavitation. Overall, the micropatterned subsurface has a large impact on the contact stiffness, the interfacial stress distribution, and the detachment behavior; however, the adhesion performance is only slightly improved in comparison to a non-patterned subsurface.
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.
Viscoelasticity is well known to cause significant hysteresis of crack closure and opening when an elastomer is brought in and out of contact with a flat, rigid, adhesive counterface. A separate origin of adhesive hysteresis is small-scale, elastic multistability. Here, we study a system in which both mechanisms act concurrently. Specifically, we compare the simulated and experimentally measured time evolution of the interfacial force and the real contact area between a soft elastomer and a rigid, flat punch, to which small-scale, single-sinusoidal roughness is added. To this end, we further the Green's function molecular dynamics method and extend recently developed imaging techniques to elucidate the rate- and preload-dependence of the pull-off process. Our results reveal that hysteresis is much enhanced when the saddle points of the topography come into contact, which, however, is impeded by viscoelastic forces and may require sufficiently large preloads. A similar coaction of viscous- and multistability effects is expected to occur in macroscopic polymer contacts and to be relevant, e.g., for pressure-sensitive adhesives and modern adhesive gripping devices.
Micro-objects stick tenaciously to each other—a well-known show-stopper in microtechnology and in handling micro-objects. Inspired by the trigger plant, we explore a mechanical metastructure for overcoming adhesion involving a snap-action mechanism. We analyze the nonlinear mechanical response of curved beam architectures clamped by a tunable spring, incorporating mono- and bistable states. As a result, reversible miniaturized snap-through devices are successfully realized by micron-scale direct printing, and successful pick-and-place handling of a micro-object is demonstrated. The technique is applicable to universal scenarios, including dry and wet environment, or smooth and rough counter surfaces. With an unprecedented switching ratio (between high and low adhesion) exceeding 104, this concept proposes an efficient paradigm for handling and placing superlight objects. Nature teaches us how to design reliable grippers for moving and placing super-small objects that tend to stick to everywhere.
The remarkable properties of bio-inspired microstructures make them extensively accessible for various applications, including industrial, medical, and space applications. However, their implementation especially as grippers for pick-and-place robotics can be compromised by multiple factors. The most common ones are alignment imperfections with the target object, unbalanced stress distribution, contamination, defects, and roughness at the gripping interface. In the present work, three different approaches to assess the contact phenomena between patterned structures and the target object are presented. First, in-situ observation and machine learning are combined to realize accurate real-time predictions of adhesion performance. The trained supervised learning models successfully predict the adhesion performance from the contact signature. Second, two newly developed optical systems are compared to observe the correct grasping of various target objects (rough or transparent) by looking through the microstructures. And last, model experiments are provided for a direct comparison with simulation efforts aiming at a prediction of the contact signature and an analysis of the rate and preload-dependency of the adhesion strength of a soft polymer film in contact with roughness-like surface topography. The results of this thesis open new perspectives for improving the reliability of handling systems using bioinspired microstructures.