A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures

Abstract

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.

Demoulding process assessment of elastomers in micro-textured moulds

“…The development of a holistic design software has already been started at ITAINNOVA and preliminary results have been obtained. The software, based on ROM results for different rubber materials, geometries, operating conditions, and textures, is proven to be a powerful tool to compute real-time predictions for both friction and DF values 4 , 48 .…”

“…The DoE was planned (see Table 7 ) with a statistical software 49 ; a 2-level full factorial design including 3 factors and 1 center point with 3 replicates was selected. Values of DDD were selected according to finite element analysis (FEA) simulations 48 , where friction reduction of textured rubber seals in dynamic conditions was expected and finally achieved. We found that the texturing of the metal plates increased the DF, which could compromise the correct texture transfer.…”

“…TRD is a completely data-driven method for which a problem of N variables can be written as the product of N one-dimensional functions, one for each of the system’s variables, as detailed in a previous publication 47 . Due to promising results obtained from the application of ROM techniques to both friction reduction prediction and statistical analysis of texture replication 48 , the method was selected to be applied for DF modelling and prediction, as described in this paper.…”

Current Trends and Applications of Machine Learning in Tribology—A Review

“…Based upon a LHS (70 data sets) and contact simulations, the contact pressure, lubricant film height, and frictional force were predicted with CoPs larger than 82%, allowing subsequent optimization with an EA. Zambrano et al [117] used reduced order modeling (ROM) to predict and optimize the frictional behavior of surface textures in dynamic rubber applications under different operating conditions. It is noteworthy that this was based on a limited number of experimental measurements and the ROM was fed with microscope-based texture measurements.…”

In Situ Observation of Micro-Patterned Elastomeric Surfaces: The Formation of the Area of Real Contact and the Influence on Its Friction and Deformation Behaviour

“…Surface texturing is an effective way to achieve these goals without changing the material or lubricant. Zambrano et al combined friction measurements of a surface textured rubber specimen with Reduced Order Modelling to compute optimal surface texture parameters that provide the highest friction reduction within a given parameter space [23]. One class of materials that comes into focus here are thiol-acrylate polymers, which exhibit excellent shape memory behaviour upon thermal triggering [24,25].…”