Surface texturing is an effective method to reduce friction without the need to change materials. In this study, surface textures were transferred to rubber samples in the form of dimples, using a novel laser surface texturing (LST)—based texturing during moulding (TDM) production process, developed within the European Project MouldTex. The rubber samples were used to experimentally determine texture-induced friction variations, although, due to the complexity of manufacturing, only a limited amount was available. The tribological friction measurements were hence combined with an artificial intelligence (AI) technique, i.e., Reduced Order Modelling (ROM). ROM allows obtaining a virtual representation of reality through a set of numerical strategies for problem simplification. The ROM model was created to predict the friction outcome under different operating conditions and to find optimised dimple parameters, i.e., depth, diameter and distance, for friction reduction. Moreover, the ROM model was used to evaluate the impact on friction when manufacturing deviations on dimple dimensions were observed. These results enable industrial producers to improve the quality of their products by finding optimised textures and controlling nominal surface texture tolerances prior to the rubber components production.

The ability to interact safely with the environment is known as one of the major advantages of soft robots (SRs). Due to their low material stiffness, these continuously deformable robots offer inherent flexibility. These advantages make them suitable for application that involve human-robot collaboration in industrial settings as well as medical application such as minimally invasive surgery. To date only few research groups have analyzed the contact and frictional behavior of soft robots. In fact, the contact behavior is often oversimplified or neglected. Motivated by the idea to bridge this gap, this work presents measurements and the resulting coefficient of friction (COF) for silicone rubbers that are widely used in the field of SRs and different contact partners which depend on contact pressure and ambient temperature. From these measurements, a more representative contact model is established and used to more accurately simulate soft material robots’ frictional contact behavior. Moreover the influence of friction and therefore the need to implement frictional behavior is demonstrated for a typical application of a SR.

Tread wear appears as a consequence of friction, which mainly depends on surface characteristics, contact pressure, slip velocity, temperature and dissipative material properties of the tread material itself. The subsequent description introduces a wear model as a function of the frictional energy rate. A post-processing as well as an adaptive re-meshing algorithm are implemented into a finite element code in order to predict wear loss in terms of mass. The geometry of block models is generated by image processing tools using photographs of the rubber samples in the laboratory. In addition, the worn block shape after the wear test is compared to simulation results.

Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions.

Soft material robotic systems offer inherent safety and flexibility due to their low material stiffness. Therefore, soft material robots are prone to operate in unknown environments and fulfill tasks that involve and even exploit contact with the environment. Moving to the application of soft robots, incorporating validated contact models in modeling frameworks can be crucial for simulation tasks in, e.g. design optimization, motion planning or control. Cosserat rod models have proven themselves not only to be accurate but also computationally efficient for slender soft continuum robots (SCRs). However, only recently the topic of contact modeling has been introduced to Cosserat rod frameworks for SCRs. In this letter, for the first time we present and analyze an approach to include contact modeling in a widely used shooting-based Cosserat rod implementation. Evaluation against detailed finite element (FE) simulations indicate comparable accuracy, while the computational time remains a small fraction. Simulated data for the considered contact scenarios reveal a consistent level of agreement to experimental data, with minor discrepancies. The results are a promising basis on which further contact investigations can build.

Not only in the automotive sector, but also in the field of aircraft tires, the topic of abrasion is of great importance. The aircraft tire manufacturers provide criteria for the permissible degree of wear. If these limits are exceeded, the tire must be replaced or retreaded. By this time, the tire should withstand as many takeoff and landing cycles as possible. Abrasion models should help to predict the wear behavior in preflight modeling. At the Institute of Dynamics and Vibration Research, quasi-steady abrasion tests are performed using tread block samples from an aircraft tire. For various pressures and sliding speeds, the abrasion is determined by recording the mass loss of the rubber sample. Based on these measurement data, a wear model is derived as a function of coefficient of friction, contact pressure, and sliding speed for different ambient temperatures. The well-known brush model forms the basis for the wear simulations. With parameters validated on the aircraft tire, such as contact length, stiffness, and friction coefficient, the resulting mechanical forces within the contact area are calculated. Finally, the classic brush model is extended by the abrasion calculation. The tire wear is determined during unsteady load and slip conditions by use of the quasi-steady wear maps derived from our experiments. Within a measurement campaign on the complete tire, the tread depth is measured after various driving maneuvers and is in good agreement with the simulation results.