A Digital Twin for Friction Prediction in Dynamic Rubber Applications with Surface Textures
Abstract
:1. Introduction
1.1. Surface Texturing in Tribological Applications
1.2. Reduced Order Modelling
1.3. Objectives
2. Materials and Methods
2.1. Rubber Specimen Geometry and Surface Texture Parameters
2.2. Test Rig Setup and Experimental Procedure for Determining the Coefficient of Friction
2.3. Method of Measurement for Real Dimple Dimensions
2.4. Software Development for Reduced Order Modelling
2.5. Reduced Order Modelling Data Pre-Processing for Friction Reduction
2.6. Statistical Analysis of Real Dimple Dimensions
3. Results
3.1. Measurement Results of the Real Dimple Dimensions and Definition of Dimensionless Dimple Parameters
3.2. Reduced Order Modelling on Friction Coefficient Data
3.3. Reduced Order Modelling on Pre-Processed Data for Friction Variations
3.4. Statistical Analysis Results of Real Dimple Dimensions
3.5. Experimental Friction Measurement Results and ROM Friction Prediction Outcome
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
CFD | Cumulative Density Function |
DoE | Design of Experiment |
DT | Digital Twin |
FEM | Finite Element Method |
LST | Laser Surface Texturing |
ML | Machine Learning |
Probability Density Function | |
PGD | Proper Generalized Decomposition |
POD | Proper Orthogonal Decomposition |
ROM | Reduced Order Modelling |
SEHL | Soft Elasto-Hydrodynamic Lubrication |
SVD | Singular Value Decomposition |
TDM | Texturing During Moulding |
TRD | Tensor Rank Decomposition |
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Sample i | Dimple Diameter [m] | Dimple Distance [m] | Dimple Depth [m] |
---|---|---|---|
1 | 100 | 300 | 10 |
2 | 200 | 100 | 10 |
3 | 200 | 200 | 10 |
4 | 300 | 100 | 30 |
5 | 300 | 200 | 20 |
6 | 300 | 200 | 30 |
7 | 300 | 300 | 20 |
8 | - | - | - |
a | |||
---|---|---|---|
Rotational Speed n[min] | Relative Velocity [mm/s] | ||
0.6 | 6 | ||
1.2 | 12 | ||
1.8 | 19 | ||
3.0 | 31 | ||
6.0 | 63 | ||
12.0 | 126 | ||
18.0 | 188 | ||
24.0 | 251 | ||
b | |||
Normal Force [N] | Max Contact Pressure [MPa] | Contact Diameter [mm] | Nominal Contact Area [mm] |
3.9 | 0.5 | 4.2 | 13.8 |
7.9 | 0.7 | 5.0 | 19.6 |
13.3 | 0.9 | 5.8 | 26.4 |
Sample i | Dimple Diameter [m] | Dimple Distance [m] | Dimple Depth [m] | Aspect Ratio | Textured Area [%] |
---|---|---|---|---|---|
1 | 135 | 258 | 16 | 0.11 | 9 |
2 | 242 | 100 | 11 | 0.05 | 39 |
3 | 241 | 165 | 10 | 0.04 | 28 |
4 | 337 | 66 | 35 | 0.10 | 55 |
5 | 330 | 170 | 22 | 0.06 | 34 |
6 | 346 | 153 | 35 | 0.10 | 37 |
7 | 336 | 252 | 20 | 0.06 | 25 |
8 | - | - | - | - | - |
a | b | ||||
---|---|---|---|---|---|
Untextured ROM | Textured ROM | ||||
Train | Test | Train | Test | ||
1 | 1.000 | 0.895 | 1 | 1.000 | 0.914 |
2 | 1.000 | 0.968 | 2 | 1.000 | 0.872 |
3 | 1.000 | 0.988 | 3 | 1.000 | 0.882 |
4 | 1.000 | 0.974 | 4 | 1.000 | 0.943 |
5 | 1.000 | 0.988 | 5 | 1.000 | 0.923 |
6 | 1.000 | 0.903 | 6 | 1.000 | 0.896 |
7 | 1.000 | 0.871 | 7 | 1.000 | 0.963 |
8 | 1.000 | 0.994 | 8 | 1.000 | 0.912 |
9 | 1.000 | 0.915 | 9 | 1.000 | 0.953 |
10 | 1.000 | 0.982 | 10 | 1.000 | 0.938 |
avg | 1.000 | 0.948 | avg | 1.000 | 0.919 |
Use Case Num. | Relative Velocity [mm/s] | Max Contact Pressure [MPa] | Dimple Diameter [m] | Dimple Distance [m] | Dimple Depth [m] | Aspect Ratio | Textured Area [%] | Predicted Friction Reduction [%] |
---|---|---|---|---|---|---|---|---|
1 | 251 | 0.5 | 270 | 100 | 10 | 0.04 | 42 | 63 |
2 | 31 | 0.7 | 300 | 186 | 11 | 0.04 | 30 | 81 |
3 | 6 | 0.9 | 274 | 111 | 11 | 0.04 | 39 | 72 |
4 | 100 | 0.6 | 300 | 140 | 11 | 0.04 | 36 | 79 |
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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
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 StyleZambrano, Valentina, Markus Brase, Belén Hernández-Gascón, Matthias Wangenheim, Leticia A. Gracia, Ismael Viejo, Salvador Izquierdo, and José Ramón 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