Periodic Reporting for period 1 - SURFACE (Towards Future Interfaces With Tuneable Adhesion By Dynamic Excitation)
Période du rapport: 2022-10-01 au 2025-03-31
Contact Mechanics, the discipline that studies how objects interact when in contact, is of primary importance in several fields of science. Determining the forces transmitted when two objects are in contact enables the design of machine elements, robotic arms, tires, touchscreens, seals, washers, shoes, and brakes. SURFACE focuses on “soft contacts”, i.e. on contact scenarios involving materials such as rubbers, silicones, and hydrogels. This class of materials is attracting attention in the scientific community due to their crucial role in the development of cutting-edge technologies, including robotics, space tools, climbing robots, grasping systems, and manipulators. Traditionally, object manipulation has relied on energy-intensive systems like electromagnets, suction cups, and adhesives. However, manipulators that use electromagnets work only with ferromagnetic materials, suction cups fail in a vacuum, and adhesives leave residues on interfaces and are not reversible. High-level interactions between humans and robots, on the other hand, require compliant intrinsically safe materials and the capability for rapid regulation of interfacial forces, particularly adhesion. Bioinspiration has led researchers to design patterned interfaces at the micrometric scale that exploit fundamental interactions between materials, such as van der Waals forces, which occur at the nanometer scale. By imitating nature solutions as in geckos and lizards, researchers have achieved high levels of stickiness. However, the challenge remains to efficiently detach the mating interfaces, which is crucial for manipulation tasks. An alternative mechanism for adhesion enhancement exploits the viscoelastic nature of soft materials, using high-frequency, low-amplitude vibrations to excite the soft substrate. SURFACE aims to advance the state of the art in adhesion regulation by leveraging both the surface’s micrometric geometry and the viscoelastic properties of materials. The objective is to develop a metasurface whose adhesive behavior can be actively regulated in real time. By exploiting van der Waals interactions, SURFACE's working principle is not limited to ferromagnetic materials, functions in vacuum or outer space, leaves no chemical residues on contacting objects, and, by adopting soft polymers, is inherently safe for human-robot interaction. The objectives of SURFACE are to deepen our understanding of the rate-dependent adhesion properties of soft viscoelastic materials, focusing on how geometrical parameters, material properties, and loading protocols influence stickiness. To achieve this, SURFACE plans to: (i) develop efficient contact algorithms to simulate adhesive interactions with high spatial and temporal resolution, (ii) exploit the coupling effects between adhesion and viscoelasticity to understand how these mechanisms influence macroscopic adhesion properties, (iii) leverage machine learning to predict and optimize adhesion performance, (iv) validate numerical and theoretical findings through experimental tests. SURFACE's results are relevant to the fields of soft robotics, space exploration, and disaster relief, enabling the creation of lightweight, reusable, and reversible adhesive systems. By addressing both scientific and technological challenges, the project aspires to bridge the gap between theory and application, advancing the state of the art in adhesive technology.
SURFACE has made substantial progress toward understanding and advancing soft adhesive interfaces by integrating numerical, experimental, and design methodologies. Key achievements align with the project’s four objectives: developing advanced simulation tools, exploring viscoelastic adhesion mechanisms, utilizing artificial intelligence for adhesion optimization, and validating results experimentally.
On the modelling side, SURFACE successfully developed highly efficient simulation tools based on the Boundary Element Method (BEM) to analyse adhesion dynamics in soft viscoelastic substrates. This approach enables precise modelling of contact phenomena, including the effects of material viscoelasticity and interface macroscopic geometry. The numerical simulations demonstrated how adhesion strength is influenced by geometrical features, such as the geometry of the indenter and the thickness of the substate, and by the viscoelastic constitutive behaviour of the material, providing new insights into how to improve the macroscopic adhesive performance. Machine Learning (ML) models are being used to accelerate the numerical simulations and obtain real time predictions of the adhesive strength of the interface without the need to run time-consuming full numerical simulations. ML including neural networks, were trained on large datasets generated through numerical simulations to predict adhesion performance. This innovative approach accelerates the discovery of new surface designs capable of achieving optimal adhesion.
A custom tribometer for dynamic adhesion testing was designed and assembled, being among the few test rigs in the world capable of vibroadhesion tests. This setup validated theoretical predictions, revealing that micro-vibrations can amplify adhesive forces by 1400 % with respect to the quasi-static tests (without vibrations). These findings confirmed that dynamic excitation can significantly enhance adhesion strength and provide a reliable foundation for the development of tuneable adhesive interfaces.
SURFACE has thus advanced the state of the art in viscoelastic adhesion, delivering critical tools, methods, and insights to design next-generation adhesive interfaces. These achievements hold promise for transformative applications in robotics, such as manipulators and grippers.
On the modelling side, SURFACE successfully developed highly efficient simulation tools based on the Boundary Element Method (BEM) to analyse adhesion dynamics in soft viscoelastic substrates. This approach enables precise modelling of contact phenomena, including the effects of material viscoelasticity and interface macroscopic geometry. The numerical simulations demonstrated how adhesion strength is influenced by geometrical features, such as the geometry of the indenter and the thickness of the substate, and by the viscoelastic constitutive behaviour of the material, providing new insights into how to improve the macroscopic adhesive performance. Machine Learning (ML) models are being used to accelerate the numerical simulations and obtain real time predictions of the adhesive strength of the interface without the need to run time-consuming full numerical simulations. ML including neural networks, were trained on large datasets generated through numerical simulations to predict adhesion performance. This innovative approach accelerates the discovery of new surface designs capable of achieving optimal adhesion.
A custom tribometer for dynamic adhesion testing was designed and assembled, being among the few test rigs in the world capable of vibroadhesion tests. This setup validated theoretical predictions, revealing that micro-vibrations can amplify adhesive forces by 1400 % with respect to the quasi-static tests (without vibrations). These findings confirmed that dynamic excitation can significantly enhance adhesion strength and provide a reliable foundation for the development of tuneable adhesive interfaces.
SURFACE has thus advanced the state of the art in viscoelastic adhesion, delivering critical tools, methods, and insights to design next-generation adhesive interfaces. These achievements hold promise for transformative applications in robotics, such as manipulators and grippers.
SURFACE has achieved significant milestones in understanding soft viscoelastic adhesion. Thanks to the numerical modeling of soft interfaces, the research team was able to validate existing theories of viscoelastic adhesive contacts and compare these against experimental tests. It was found that current modeling approaches are valid within a limited range of loading/unloading velocities, and other contributions to adhesion enhancement should be considered to fully describe the adhesive behavior of the interface. Experimentally, the team built a custom tribometer, consisting of a hemispherical borosilicate lens in contact with a soft PDMS layer. During the contact experiment, the contact force and area were measured while microvibrations were regulated by varying the amplitude and frequency of the excitation. The enhancement of the adhesive strength of the interface was found to be approximately 14 times greater than the reference quasi-static case where vibrations were switched off. It was demonstrated that, by characterizing the rate-dependent adhesive behavior of the interface, it is possible to develop a contact mechanics model capable of predicting the adhesive strength of the interface with satisfactory accuracy. The results pave the way for the development of new technologies for smart adhesive grippers and manipulators, particularly in the field of soft robotics. From a scientific standpoint, further work is needed to better understand the influence of geometrical and material parameters on adhesive performance. To favor technology transfer, the technology requires miniaturization and integration into a metasurface capable of reproducing the experimental findings in a compact prototype.