Details
| Original language | English |
|---|---|
| Article number | 57 |
| Journal | Lubricants |
| Volume | 9 |
| Issue number | 5 |
| Publication status | Published - 20 May 2021 |
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.
Keywords
- Artificial intelligence, Digital twin, Dynamic friction, Laser surface texturing, Machine learning, Reduced order modelling, Rubber seal applications, Tensor decomposition, Texturing during moulding
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Materials Science(all)
- Surfaces, Coatings and Films
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In: Lubricants, Vol. 9, No. 5, 57, 20.05.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures
AU - Zambrano, Valentina
AU - Brase, Markus
AU - Hernandez Gascon, Belen
AU - Wangenheim, Matthias
AU - Gracia, Leticia
AU - Viejo, Ismael
AU - Izqiuerdo, Salvador
AU - Valdes, Jose Ramon
N1 - Funding Information: This work has been funded by the European Union?s research program Horizon2020, under grant agreement No. 768705.
PY - 2021/5/20
Y1 - 2021/5/20
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Digital twin
KW - Dynamic friction
KW - Laser surface texturing
KW - Machine learning
KW - Reduced order modelling
KW - Rubber seal applications
KW - Tensor decomposition
KW - Texturing during moulding
UR - http://www.scopus.com/inward/record.url?scp=85107174315&partnerID=8YFLogxK
U2 - 10.3390/lubricants9050057
DO - 10.3390/lubricants9050057
M3 - Article
VL - 9
JO - Lubricants
JF - Lubricants
SN - 2075-4442
IS - 5
M1 - 57
ER -