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Article

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

1
Instituto Tecnológico de Aragón—ITAINNOVA, C/María de Luna 7-8, 50018 Zaragoza, Spain
2
Institut für Dynamik und Schwingungen—IDS, Leibniz Universität Hannover—LUH, An der Universität 1, 30823 Garbsen, Germany
*
Author to whom correspondence should be addressed.
Lubricants 2021, 9(5), 57; https://doi.org/10.3390/lubricants9050057
Received: 30 March 2021 / Revised: 15 May 2021 / Accepted: 16 May 2021 / Published: 20 May 2021
(This article belongs to the Special Issue Machine Learning in Tribology)
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. View Full-Text
Keywords: reduced order modelling; dynamic friction; rubber seal applications; tensor decomposition; laser surface texturing; texturing during moulding; digital twin; machine learning; artificial intelligence reduced order modelling; dynamic friction; rubber seal applications; tensor decomposition; laser surface texturing; texturing during moulding; digital twin; machine learning; artificial intelligence
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MDPI and ACS Style

Zambrano, V.; Brase, M.; Hernández-Gascón, B.; Wangenheim, M.; Gracia, L.A.; Viejo, I.; Izquierdo, S.; Valdés, J.R. A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures. Lubricants 2021, 9, 57. https://doi.org/10.3390/lubricants9050057

AMA Style

Zambrano V, Brase M, Hernández-Gascón B, Wangenheim M, Gracia LA, Viejo I, Izquierdo S, Valdés JR. A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures. Lubricants. 2021; 9(5):57. https://doi.org/10.3390/lubricants9050057

Chicago/Turabian Style

Zambrano, Valentina, Markus Brase, Belén Hernández-Gascón, Matthias Wangenheim, Leticia A. Gracia, Ismael Viejo, Salvador Izquierdo, and José R. Valdés. 2021. "A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures" Lubricants 9, no. 5: 57. https://doi.org/10.3390/lubricants9050057

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